未知环境下移动智能体自主导航研究
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摘要
自主移动智能体（AutonomousMobileAgent）是智能体研究和工程应用的一个重要领域，对于移动智能体而言，其自主导航能力是智能体各个能力中重要的一部分。自主导航包括路径规划、避障策略、定位、环境感知等多个方面，要求移动智能体不具备任何先验的环境知识，仅根据自身配备的传感器来感知周边环境内的障碍物信息，并根据此信息自主规划它的动作、制定运动轨迹，最终尽可能高效、可靠地从初始位置（或初始位姿）到达指定的目标位置（或终止位姿）。移动智能体自主导航需要满足一定的约束条件，这些约束条件包括：与障碍物间无碰撞、路径长度最短、能源消耗最少、时间消耗最少、以及移动智能体的动力学约束等。
本文主要研究了移动智能体在大尺度范围内和小尺度范围内未知环境下的自主导航问题，主要的研究内容和创新点现概括如下：
1.提出矢量化路径描述方法，以路径矢量组来表示路径。该方法中提出以一组首尾相连的矢量来描述、保存规划路径，移动智能体在根据矢量化的路径行进时，可以直接根据规划路径执行前进、转向指令。而且该方法可以有效的避免路径规划过程对地图构建的依赖，无需地图存储及维护的庞大数据，因此可以大幅度降低保存路径时的空间需求。提出的路径更新计算方法可方便的进行路径的维护和更新，并大大减少路径更新时的计算量。
2.将矢量化路径描述方法与Bug算法思想相结合，解决大尺度范围内静态未知环境下移动智能体自主导航问题。研究中借鉴了Bug系列算法中“朝向目标位置移动”和“遇到障碍物转身”的思想，并将其与矢量化路径描述方法紧密结合。首先以起始位置和目标位置构建初始路径，令移动智能体沿初始路径行进，在行进过程中，移动智能体根据不同的条件对未知的环境及其中的障碍物进行检测，一旦发现有障碍物阻挡当前行进路径，则生成中间插入点并更新路径，然后令移动智能体沿更新后的路径继续行进，最终令移动智能体无碰撞的到达目标位置。研究中提出了两种中间插入点的生成方法，分别是扫描生成和随机生成。两种方法中，扫描方式相比于随机方式而言，更容易得到路径长度较优的规划结果；而随机生成方式则具有更好的全局搜索能力。对于扫描生成中间点时易得到局部最优解的缺点，研究中提出了改进方法，使得移动智能体在陷入局部最优后，能够迅速走出局部最优；而对于中间点的随机生成方式则提出了快速收敛策略，以得到较短的运行轨迹和较少的规划时间。
3.将矢量化路径描述方法运用到大尺度范围内动态未知环境下移动智能体的自主导航研究中，解决环境中具有未知移动障碍物的自主导航问题。依据矢量化路径描述方法的思想，首先建立初始路径，令移动智能体在沿路径行进的过程中以规定的时间间隔对周边环境进行检测，查找是否有障碍物，一旦发现有障碍物存在，则根据研究中提出的一系列步骤，判定障碍物的移动趋势，并断定该障碍物是否会阻挡当前行进路径。一旦发现有障碍物已经阻挡或将有可能阻挡当前路径时，根据提出的中间点生成算法，朝向障碍物移动的相反方向生成中间插入点并实时更新路径，然后令移动智能体沿更新后的路径行进，直至安全无碰撞的到达目标位置。
4.充分考虑移动智能体的动力学约束，提出基于Bug思想的小尺度范围内移动智能体自主导航算法。将Dubins路径与Bug算法相结合，在移动智能体从一个位姿向另一个位姿运动的过程中，根据移动智能体的最小转向半径构建Dubins路径，并令移动智能体沿Dubins路径行进，以满足其动力学特性。首先令移动智能体沿Dubins路径从初始位姿向终止位姿移动，并在规定的条件下感知环境信息，一旦移动过程中发现有障碍物阻挡当前路径，则根据提出的中间位姿生成方法，生成中间位姿，并构造两条分别由当前位姿到中间位姿、由中间位姿到终止位姿的Dubins路径，令移动智能体从当前位姿沿该路径向终止位姿移动。移动过程中不断重复移动、感知、生成中间位姿等一系列过程，直至移动智能体无碰撞的到达终止位姿。
Research of the autonomous mobile agent is an important field for the research and engineering application of the agent. For a mobile agent, its ability of autonomous navigation is an important part of its various capacities. Autonomous navigation for the mobile agent includes online path planning, obstacle avoidance, localization of the mobile agent, and environment and obstacle perception, etc. It requires the mobile agent with no prior information of environment or obstacles, only perceiving surrounding environment information according to its own perception equipment, and making the mobile agent autonomously plan its movement and moving trajectory based on its sensing information. Eventually, the mobile agent can autonomously move from the starting position (or initial configuration) to the specified goal position (or final configuration) as efficient and reliable as possible. Autonomous navigation for the mobile agent requires the agent meeting certain constraints, such as collision-free with the obstacle, the shortest path length, the lowest energy consumption, the shortest time cost, and kinematic constraints, etc.
This paper studies the autonomous navigation problem for the mobile agent in unknown environment within the scope of large-scale and small-scale. Major researches and innovations are summarized as follows:
1. The concept of vectorization path is proposed in this research, and the planned path is described in the form of vector set. All the planned paths are described and saved in a set of end-to-end vectors. Different from other path representation, when the mobile agent moves along the vectorization path, it can directly execute the moving and steering instructions according to the planning path. And the proposed method can effectively avoid the dependence on map construction in the planning process. There is no huge data requirement for map storage and maintenance, so the space requirement for path saving can be reduced significantly. And the proposed path regeneration method can facilitate the maintenance and updating of the path, and greatly reduce the amount of computation on path updating.
2. This research combines the vectorization path representation and the thinking of Bug algorithm in order to solve the autonomous navigation problem for the mobile agent in static unknown environment within the scope of large-scale. The study draws on the move-to-goal and boundary-following thinking in Bug family, and combines them firmly with the vectorization path thinking. Firstly, construct the initial path in accordance with the starting position and the goal position. And then make the mobile agent moving along the initial path. In the process of moving, the mobile agent perceives the obstacles in the completely unknown environment depending on the different conditions. Once there is obstacle constructing the current path, an intermediate point will be generated and inserted into the current path to get a regenerated collision-free path. And then make the mobile agent move along the regenerated path, so as to arrive at the goal position with no collision. In this research, two intermediate point generation methods are proposed, which are scanned generating method and randomly generating method. Compare to random generating method, scanned generating method can get a shorter path length, but random generating method has better global search ability. The shortcoming of scanned generating method is that the mobile agent is easily getting the local optimal solution. The research proposed improving strategy for this shortcoming in order to make the agent quickly out of the local optimum after it falling into the local optimum. For the random generating method, the rapid convergence strategy is proposed to get shorter path trajectory and executing time.
3. The vectorization path representation is applied to the research of autonomous navigation for mobile agent in dynamic unknown environment within the scope of large-scale, in order to solve the path planning problem in the environment with moving obstacles. According to the vectorization path representation thinking, establish the initial path firstly, and make the mobile agent moving along the path with perceiving the environment at certain time interval. Once the obstacle is found, the mobile agent will determine the moving trend of the obstacle, and make a judgment of whether the obstacle will construct the current path or not. If there is or will be an obstacle constructing the current path, an intermediate point, in the opposite direction to the moving direction of obstacle, will be generated in accordance with the proposed strategy. And then the collision-free path will be regenerated by inserting the intermediate point. The mobile agent will move along the regenerated path to arrive at the goal position collision-free.
4. Taking full consideration of the kinematic models and constraints of the mobile agent, a novel autonomous navigation method for mobile agent in the range of small-scale based on Bug thinking is proposed. Combining the Dubins path with the Bug family algorithm, when the mobile agent moving from one configuration to another, it always moving along the Dubins path which is made up in accordance with the agent's minimum turning radius, in order to meet the kinematic constraints of the mobile agent. First of all, make the mobile agent moving from the initial configuration to the final configuration along the Dubins path. Perceive the obstacles in environment under certain conditions while moving. If there is obstacle constructed the current path, generate the intermediate configuration in accordance with the proposed strategy, and make the mobile agent move along the new Dubins path from current configuration to intermediate configuration. The Dubins path from intermediate configuration to the final configuration is also generated. Repeat the series of process, such as moving, perception and intermediate configuration generation, until the mobile agent arrives at the final configuration with no collision.
本文主要研究了移动智能体在大尺度范围内和小尺度范围内未知环境下的自主导航问题，主要的研究内容和创新点现概括如下：
1.提出矢量化路径描述方法，以路径矢量组来表示路径。该方法中提出以一组首尾相连的矢量来描述、保存规划路径，移动智能体在根据矢量化的路径行进时，可以直接根据规划路径执行前进、转向指令。而且该方法可以有效的避免路径规划过程对地图构建的依赖，无需地图存储及维护的庞大数据，因此可以大幅度降低保存路径时的空间需求。提出的路径更新计算方法可方便的进行路径的维护和更新，并大大减少路径更新时的计算量。
2.将矢量化路径描述方法与Bug算法思想相结合，解决大尺度范围内静态未知环境下移动智能体自主导航问题。研究中借鉴了Bug系列算法中“朝向目标位置移动”和“遇到障碍物转身”的思想，并将其与矢量化路径描述方法紧密结合。首先以起始位置和目标位置构建初始路径，令移动智能体沿初始路径行进，在行进过程中，移动智能体根据不同的条件对未知的环境及其中的障碍物进行检测，一旦发现有障碍物阻挡当前行进路径，则生成中间插入点并更新路径，然后令移动智能体沿更新后的路径继续行进，最终令移动智能体无碰撞的到达目标位置。研究中提出了两种中间插入点的生成方法，分别是扫描生成和随机生成。两种方法中，扫描方式相比于随机方式而言，更容易得到路径长度较优的规划结果；而随机生成方式则具有更好的全局搜索能力。对于扫描生成中间点时易得到局部最优解的缺点，研究中提出了改进方法，使得移动智能体在陷入局部最优后，能够迅速走出局部最优；而对于中间点的随机生成方式则提出了快速收敛策略，以得到较短的运行轨迹和较少的规划时间。
3.将矢量化路径描述方法运用到大尺度范围内动态未知环境下移动智能体的自主导航研究中，解决环境中具有未知移动障碍物的自主导航问题。依据矢量化路径描述方法的思想，首先建立初始路径，令移动智能体在沿路径行进的过程中以规定的时间间隔对周边环境进行检测，查找是否有障碍物，一旦发现有障碍物存在，则根据研究中提出的一系列步骤，判定障碍物的移动趋势，并断定该障碍物是否会阻挡当前行进路径。一旦发现有障碍物已经阻挡或将有可能阻挡当前路径时，根据提出的中间点生成算法，朝向障碍物移动的相反方向生成中间插入点并实时更新路径，然后令移动智能体沿更新后的路径行进，直至安全无碰撞的到达目标位置。
4.充分考虑移动智能体的动力学约束，提出基于Bug思想的小尺度范围内移动智能体自主导航算法。将Dubins路径与Bug算法相结合，在移动智能体从一个位姿向另一个位姿运动的过程中，根据移动智能体的最小转向半径构建Dubins路径，并令移动智能体沿Dubins路径行进，以满足其动力学特性。首先令移动智能体沿Dubins路径从初始位姿向终止位姿移动，并在规定的条件下感知环境信息，一旦移动过程中发现有障碍物阻挡当前路径，则根据提出的中间位姿生成方法，生成中间位姿，并构造两条分别由当前位姿到中间位姿、由中间位姿到终止位姿的Dubins路径，令移动智能体从当前位姿沿该路径向终止位姿移动。移动过程中不断重复移动、感知、生成中间位姿等一系列过程，直至移动智能体无碰撞的到达终止位姿。
Research of the autonomous mobile agent is an important field for the research and engineering application of the agent. For a mobile agent, its ability of autonomous navigation is an important part of its various capacities. Autonomous navigation for the mobile agent includes online path planning, obstacle avoidance, localization of the mobile agent, and environment and obstacle perception, etc. It requires the mobile agent with no prior information of environment or obstacles, only perceiving surrounding environment information according to its own perception equipment, and making the mobile agent autonomously plan its movement and moving trajectory based on its sensing information. Eventually, the mobile agent can autonomously move from the starting position (or initial configuration) to the specified goal position (or final configuration) as efficient and reliable as possible. Autonomous navigation for the mobile agent requires the agent meeting certain constraints, such as collision-free with the obstacle, the shortest path length, the lowest energy consumption, the shortest time cost, and kinematic constraints, etc.
This paper studies the autonomous navigation problem for the mobile agent in unknown environment within the scope of large-scale and small-scale. Major researches and innovations are summarized as follows:
1. The concept of vectorization path is proposed in this research, and the planned path is described in the form of vector set. All the planned paths are described and saved in a set of end-to-end vectors. Different from other path representation, when the mobile agent moves along the vectorization path, it can directly execute the moving and steering instructions according to the planning path. And the proposed method can effectively avoid the dependence on map construction in the planning process. There is no huge data requirement for map storage and maintenance, so the space requirement for path saving can be reduced significantly. And the proposed path regeneration method can facilitate the maintenance and updating of the path, and greatly reduce the amount of computation on path updating.
2. This research combines the vectorization path representation and the thinking of Bug algorithm in order to solve the autonomous navigation problem for the mobile agent in static unknown environment within the scope of large-scale. The study draws on the move-to-goal and boundary-following thinking in Bug family, and combines them firmly with the vectorization path thinking. Firstly, construct the initial path in accordance with the starting position and the goal position. And then make the mobile agent moving along the initial path. In the process of moving, the mobile agent perceives the obstacles in the completely unknown environment depending on the different conditions. Once there is obstacle constructing the current path, an intermediate point will be generated and inserted into the current path to get a regenerated collision-free path. And then make the mobile agent move along the regenerated path, so as to arrive at the goal position with no collision. In this research, two intermediate point generation methods are proposed, which are scanned generating method and randomly generating method. Compare to random generating method, scanned generating method can get a shorter path length, but random generating method has better global search ability. The shortcoming of scanned generating method is that the mobile agent is easily getting the local optimal solution. The research proposed improving strategy for this shortcoming in order to make the agent quickly out of the local optimum after it falling into the local optimum. For the random generating method, the rapid convergence strategy is proposed to get shorter path trajectory and executing time.
3. The vectorization path representation is applied to the research of autonomous navigation for mobile agent in dynamic unknown environment within the scope of large-scale, in order to solve the path planning problem in the environment with moving obstacles. According to the vectorization path representation thinking, establish the initial path firstly, and make the mobile agent moving along the path with perceiving the environment at certain time interval. Once the obstacle is found, the mobile agent will determine the moving trend of the obstacle, and make a judgment of whether the obstacle will construct the current path or not. If there is or will be an obstacle constructing the current path, an intermediate point, in the opposite direction to the moving direction of obstacle, will be generated in accordance with the proposed strategy. And then the collision-free path will be regenerated by inserting the intermediate point. The mobile agent will move along the regenerated path to arrive at the goal position collision-free.
4. Taking full consideration of the kinematic models and constraints of the mobile agent, a novel autonomous navigation method for mobile agent in the range of small-scale based on Bug thinking is proposed. Combining the Dubins path with the Bug family algorithm, when the mobile agent moving from one configuration to another, it always moving along the Dubins path which is made up in accordance with the agent's minimum turning radius, in order to meet the kinematic constraints of the mobile agent. First of all, make the mobile agent moving from the initial configuration to the final configuration along the Dubins path. Perceive the obstacles in environment under certain conditions while moving. If there is obstacle constructed the current path, generate the intermediate configuration in accordance with the proposed strategy, and make the mobile agent move along the new Dubins path from current configuration to intermediate configuration. The Dubins path from intermediate configuration to the final configuration is also generated. Repeat the series of process, such as moving, perception and intermediate configuration generation, until the mobile agent arrives at the final configuration with no collision.
引文
[1]Wooldridge M J, Jennings N R. Intelligent agent:theory and practice. Knowledge Engineering Review,1995,10(2):115-152.
[2]Siegwart R, Nourbakhsh I R. Introduction to autonomous mobile robots. Cambridge: The MIT Press,2004.257-298.
[3]Tarokh M. Hybrid intelligent path planning for articulated rovers in rough terrain. Fuzzy Sets and System,2008,159(21):2927-2937.
[4]Dolgov D, Thrun S, Montemerlo M, Diebel J. Path planning for autonomous vehicles in unknown semi-structured environments. The International Journal of Robotics Research,2010,29(5):485-501.
[5]Jan G E, Chang K Y, Parberry I. Optimal path planning for mobile robot navigation. IEEE/ASME Transaction on Mechatronics,2008,13(4):451-460.
[6]谢础,贾玉红,黄俊,吴永康.航空航天技术概论(第2版).北京：北京航空航天大学出版社,2008.3-11.
[7]徐玉如,庞永杰,甘永,孙玉山.智能水下机器人技术展望.智能系统学报,2006,1(1)：9-16.
[8]葛晖,徐德民,项庆睿.自主式水下航行器控制技术新进展.鱼雷技术,2007,15(3)：1-7,14.
[9]Pioneer robot to inspect Chernobyl. Power Engineering,1999,103(1):146.
[10]Burgard W, Cremers A B, Fox D, et al. Experiences with an interactive tour-guide robot. Artificial Intelligence,1999,114(1-2):3-55.
[11]Scheuerman R J, Merkle W O. Automated guided vehicle. United States, United States Patent Application Publication, US2011/0106352A1,2011-05-05.
[12]Thrun S, Hahnel D, Ferguson D, et al. A system for volumetric robotic mapping of abandoned mines. In Proceedings of the2003IEEE International Conference on Robtics and Automation,2003,3:4270-4275.
[13]Nuchter A, Surmann H, Lingemann K, et al.6D SLAM with an application in autonomous mine mapping. In Proceedings of the2004IEEE International Conference on Robtics and Automation,2004,2:1998-2003.
[14]Baker C, Morris A, Ferguson D, et al. A campaign in autonomous mine mapping. In Proceedings of the2004IEEE International Conference on Robtics and Automation,2004,2:2004-2009.
[15]Thrun S, Thayer S, Whittaker W, et al. Autonomous exploration and mapping of abandoned mines. Robotics and Automation Magazine, IEEE,2004,11(4):79-91.
[16]Morris A, Ferguson D, Omohundro Z, et al. Recent developments in subterranean robotics. Journal of Field Robotics,2006,23(1):35-57.
[17]Silver D, Ferguson D, Morris A, et al. Feature extraction for topological mine maps. In Proceedings of the IEEE International Conference on Intelligent Robotics and Systems,2004,1:773-779.
[18]Ng J, Braunl T. Performance comparison of Bug navigation algorithms. Journal of Intelligent and Robotic Systems,2007,50(1):73-84.
[19]Lumelsky V J, Stepanov A A. Dynamic path planning for a mobile automaton with limited information on the environmen. IEEE Transactions on Automatic Control,1986,31(11):1058-1063.
[20]Lumelsky V J, Stepanov A A. Path-planning strategies for a point mobile automaton moving amidst unknown obstacles for arbitrary shape. Algorithmica,1987,2(1-4):403-430.
[21]Sankaranarayanan A, Vidyasagar M. A new path planning algorithm for moving a point object amidst unknown obstacles in a plane. In Proceedings of the IEEE International Conference on Robotics and Automation,1990,3:1930-1936.
[22]Sankaranarayanan A, Vidyasagar M. Path planning for moving a point objects amidst unknownobstacles in a plane:a new algorithm and a general theory for algorithm development. In Proceedings of the29th IEEE International Conference on Decision and Control,1990,2:1111-1119.
[23]Noborio H, Maeda Y, Urakawa K. A comparative study of sensor-based path-planning algorithms in an unknown maze. In Proceedings of the IEEE International Conference on Intelligent Robotics and Systems,2000,2:909-916.
[24]Kamon I, Rivlin E. Sensory-based motion planning with global proofs. IEEE Transactions on Robotics and Automation,1997,13(6):814-822.
[25]Lumelsky V J, Skewis T. Incorporating range sensing in the robot navigation function. IEEE Transactions on System, Man, and Cybernetics,1990,20(5):1058-1068.
[26]Kamon I, Rimon E, Rivlin E. TangentBug:a range-sensor-based navigation algorithm. International Journal of Robotics Research,1998,17(9):934-953.
[27]Laubach S L, Burdick J W. An autonomous sensor-based path-planner for planetary microrovers. In Proceedings of the IEEE International Conference on Robotics and Autom ation,1999,1:347-354.
[28]Laubach S, Burdick J. RoverBug:long range navigation for mars rovers. Lecture Notes in Control and Information Sciences,2000,250/2000:339-348.
[29]Magid E, Rivlin E. CautiousBug:a competitive algorithm for sensor-based robot navigation. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems,2004,3:2757-2762.
[30]Taylor K, LaValle S M. I-Bug:an intensity-based bug algorithm. In Proceedings of the ICRA'09IEEE International Conference on Robotics and Automation,2009:3981-3986.
[31]Buniyamin N, Wan Ngah W A J, Sariff N, Mohamad Z. A simple local path planning algorithm for autonomous mobile robots. International Journal of Systems Applications, Engineering and Development,2011,2(5):151-159.
[32]Guruprasad K R. EgressBug:a real time path planning algorithm for a mobile robot in an unknown environment. Lecture Notes in Computer Science,2012,7135/2012:228-236.
[33]Russell S J,Norvig P. Artifiacial Intelligent:A Modern Approach (Third Edition). USA: Prentice Hall,2009.97-107,126-129.
[34]Hart P E, Nils son N J, Raphael B. A formal basis for the heuristic determination of minimum cost paths. IEEE Trans of Systems Science and Cybernetics,1968,4(2):100-107.
[35]Trovato K I, Dorst, L. Differential A*. IEEE Transactions on Knowledge and Data Engineering,2002,14(6):1218～1229.
[36]Sun X, Koenig S. The fringe-saving A*search algorithm-a feasibility study. In Proceedings of the International Joint Conference on Artificial Intelligence,2007:2391-2397.
[37]王红卫,马勇,谢勇,郭敏.基于平滑A*算法的移动机器人路径规划.同济大学学报(自然科学版),2010,38(11)：1647-1650,1655.
[38]Sun X, Yeoh W, Koenig S. Efficient incremental search for moving target search. In Proceedings of the International Joint Conference on Artificial Intelligence,2009:615-620.
[39]Koenig S, Sun X. Comparing real-time and incremental heuristic search for real-time situated agents. Autonomous Agents and Multi-Agent Systems,2009,18(3):313-341.
[40]Stentz A. The focus sed D*algorithm for real-time replanning. In Proceedings of the International Joint Conference on Artificial Intelligence,1995:1652-1659.
[41]Koenig S, Likhachev M. D*Lite. In Proceedings of the National Conference on Artificial Intelligence,2002:476-483.
[42]Holland J H Adaptation in Natural and Artificial Systems:an Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. MA:The MIT Press,1992.1-158.
[43]Geisler T, Manikas T W. Autonomous robot navigation system using a novel value encoded genetic algorithm. In Proceedings of the IEEE Midwest Symposium on Circuits and Systems, Tulsa, United states,2002,3:45～48.
[44]Sedighi K H, Ashenayi K, et al. Autonomous local path planning for a mobile robot using a genetic algorithm. Congress on Evolutionary Computation,2004,2:1338～1345.
[45]Tarokh M. Hybrid intelligent path planning for articulatedrovers in rough terrain. Fuzzy Sets and Systems,2008,159(21):2927-2937.
[46]Ismail AL T, A Sheta, AI-Weshah M. A mobile robot path planning using genetic algorithm in static environment. Journal of Computer Science,2008,4(4):341～344.
[47]Wei J-H, Liu J-S. Mobile robot path planning with η3-splines using spatial-fitness-sharing variable-length genetic algorithm. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems,2009:978-983.
[48]Fries T P. Evolutionary terrain-based navigation of autonomours mobile robots. Studies in Computational Intelligence,2009,177:209-226.
[49]Colorni A, Dorigo M, Maniezzo V. Distributed optimization by ant colonies. In Proceedings of the First European Conference on Artificial Life,1992:134-142.
[50]Dorigo M, Maniezzo V, Colorni A. Ant system:optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics, Part B:Cybernetics,1996,26(1):29-41.
[51]Dorigo M, Gambardella L M. Ant colony system:a cooperatoive learning approach to the travelling salesman problem. IEEE Transactions on Evolutionary Computation,1997,1(1):53-66.
[52]朱庆保,张玉兰.基于栅格法的机器人路径规划蚁群算法.机器人,2005,27(2)：132-136.
[53]王沛栋,冯祖洪,黄新.一种改进的机器人路径规划蚁群算法.机器人,2008,30(6)：554-560.
[54]Cong Y Z, Ponnambalam S G. Mobile robot path planning using ant colony optimization. In Proceedings of the IEEE/ASME International Conference on Advanced Intelligent Mechatronics, Singapore,2009:851-856.
[55]Ghoseiri K, Nadjari B. An ant colony optimization algorithm for the bi-objective shortest path problem. Applied Soft Computing Journal,2010,10(4):1237-1246.
[56]Wang X, Zhang Y-Y, Wu D, et al. Collision-free path planning for mobile cranes based on ant colony algorithm. Key Engineering Materials,2011,467-469:1108-1115.
[57]Wang P-D, Tang G-Y, Li Y, et al. Ant colony algorithm using endpoint approximation for robot path planning. In Proceedings of the31st Chinese Control Conference (CCC),2012:4960-4965.
[58]Ioannidis K, Sirakoulis G Ch, Andreadis I. Cellular ants:a method to create collision free trajectories for a cooperative robot team. Robotics and Autonomous Systems,2011,59(2):113-127.
[59]Khatib O. Real-time obstacle avoidance for manipulators and mobile robots. In Proceedings of the IEEE International Conference on Robotics and Automation,1985,2:500-505.
[60]Khatib O. Real-time obstacle avoidance for manipulators and mobile robots. The International Journal of Robotics Research,1986,5(1):90-98.
[61]Latombe J C. Robot Motion Planning. Norwood, MA:Kluwer Academic Publishers,1991.295-356.
[62]Khatib M, Chatila R. An extended potential field approach for mobile robot sensor-based motions. In Proceedings of the Intelligent Autonomous Systems,1995:490-496.
[63]Barraquand J, Latombe J C. Robot motion planning:a distributed representation approach. The International Journal of Robotics Research,1991,10(6):628-649.
[64]Barraquand J, Langlois B, Latombe J C. Numerical potential field techniques for robot path planning. IEEE Transactions on Systems, Man, and Cybernetics (TSMC),1992,22(2):224-241.
[65]Feder H J S, Slotine J J. Real-time path planning using harmonic potentials in dynamic environments. In Proceedings of the1997IEEE International Conference on Robtics and Automation,1997,1:874-881.
[66]Zhang T, Zhu Y, Song J Y. Real-time motion planning for mobile robots by means of artificial potential field method in unknown environment. Industrial Robot:An International Journal,2010,37(4):384-400.
[67]Mabrouk M H, McInnes C R. Solving the potential field local minimum problem using internal agent states. Robotics and Autonomous System,2008,56(12):1050-1060.
[68]Reif J H, Wang H. Social potential fields:a distributed behavioral control for autonomous robots. Robotics and Autonomous Systems,1999,27(3):171-194.
[69]Masoud A A. Decentralized self-organizing potential field-based control for individually motivated mobile agents in a cluttered environment:a vector-harmonic potential field approach. IEEE Transactions on Systems, Man, and Cybernetics,2007,37(3):372-390.
[70]Ren J, McIsaac K A, Patel R V, Peters T M. A Potential field model using generalized sigmoid functions. IEEE Transactions on Systems, Man, and Cybernetics,2007,37(2):477-484.
[71]Howden W E. The sofa problem. Computer Journal,1968,11(3):299-301.
[72]LaValle S M, Branicky M S, Lindemann S R. On the relationship between classical grid search and probabilistic roadmaps. The International Journal of Robotics Research,2004,23(7-8):673-692.
[73]顾嘉俊.移动机器人在非平坦地形上的自主导航研究：[博士学位论文].上海：上海交通大学,2010.
[74]李杰.邻域可视性相关的路径规划问题研究：[博士学位论文].安徽：中国科学技术大学,2011.
[75]Dunbabin M, Roberts J, Usher K, et al. A hybrid AUV design for shallow water reef navigation. In Proceedings of the2005IEEE International Conference on Robotics and Automation,2005,:2105-2110.
[76]Fiorelli E, Leonard N E, Bhatta P, et al. Multi-AUV control and adaptive sampling in Monterey bay. IEEE Journal of Oceanic Engineering,2006,31(4):935-948.
[77]Lapierre L, Jouvencel B. Robust nonlinear path-following control of an AUV. IEEE Journal of Oceanic Engineering,2008,33(2):89-102.
[78]宁晓琳,房建成.基于信息融合的深空探测器的自主导航方法.中国空间科学技术,2004,24(1)：66-71.
[79]Doody D. Deep Space craft:an overview of interplanetary flight. Chichester UK:Praxis Publishing Ltd,2009.75-82.
[80]Zhang C, Li L, Xu F, et al. Evolutionary route planner for unmanned air vehicles. IEEE Transaction s on Robotics,2005,21(4):609-620.
[81]Shanmugavel M, Tsourdos A, White B, et al. Co-operative path planning of multiple UAVs using Dubins pths with clothoid arcs. Control Engineering Practice,2010,18(9):1084-1092.
[82]Pehlivanoglu Y V. A new vibrational genetic algorithm enhanced with a voronoi diagram for path planning of autonomous UAV. Aerospace Science and Technology,2012,16(1):47-55.
[83]Li P, Huang X-H, Wang M. A new hybrid method for mobile robot dynamic local path planning in unknown environment. Journal of Computers,2010,5(5):773-781.
[84]Kuffner J J, LaValle S M. RRT-connect:an efficient approach to single-query path planning. In Proceedings of the IEEE International Conference on Robotics and automation,2000:995-1001.
[85]宋金泽,戴斌,单恩忠等.一种改进的RRT路径规划算法.电子学报,2010,38(2A)：225-228.
[86]LaValle S M, Kuffner J J. Rapidly-exploring random trees:progress and prospects. In Proceedings of the Algorithmic and Computational Robotics:New Directions,2001:293-308.
[87]Kuffner JJ, LaValle S M. An efficient approach to single-query path planning. In Proceedings of the IEEE International Conference on Robotics and Automation,2000:995-1001.
[88]Bruce J, Veloso M. Real-time randomized path planning for robot navigation. In Proceedings of the IEEE International Conference on Intelligent robots and Systems,2002:2383-2388.
[89]Ferguson D, Kalra N, Stentz A T. Replanning with RRTs. In Proceedings of the IEEE International Conference on Robotics and automation,2006:1243-1248.
[90]Zucker M, Kuffner J, Branicky M. Multipartite RRTs for rapid replanning in dynamic environments. In Proceedings of the IEEE International Conference on Robotics and automation,2007:1603-1609.
[91]高扬,孙树栋,赫东锋.部分未知环境中移动机器人动态路径规划方法.控制与决策,2010,25(12)：1885-1890.
[92]Saska M, Maca M, Preucil L, et al. Robot path planning using particle swarm optimization of Ferguson splines. In Proceedings of the2006IEEE Conference on Emerging Thechnologies and Facory Automation, ETFA,2006:833-839.
[93]吴宪祥,郭宝龙,王娟.基于粒子群三次样条优化的移动机器人路径规划算法.机器人,2009,31(6)：556-560.
[94]Ye J, Qu R. Fairing of parametric cubic splines. Mathematical and Computer Modelling,1999,30(5-6):121-131.
[95]Kennedy J, Eberhart R. Particle swarm optimization. In Proceedings of the IEEE International Conference on Neural Networks,1995,4:1942-1948.
[96]Poli R, Kennedy J, Blackwell T. Particle swarm optimization. Swarm Intelligence,2007,1(1):33-57.
[97]Clerc M. Particle swarm optimization. Great Britain:IST Ltd,2010.17-20.
[98]Choset H, Lynch K M, Hutchinson S, et al. The principles of robot motion-theory, algorithms, and implementation. Cambridge:The MIT Press,2005.23-30.
[99]于殿勇,安永植,郭伟.可变履带式移动机器人.高技术通讯,1995,1：17-20.
[100]Wakabayashi S, Sato H, Nishida S-I. Design and mobility evaluation of tracked lunar vehicle. Journal of Terramechanics,2009,46(3):105-114.
[101]Pin F G, Killough S M. A new family of omnidirectional and holonomic wheeled platforms for mobile robots. IEEE Transactions on Robotics and Automation,1994,10(4):480-489.
[102]北京航空航天大学.全方位运动球型机器人.中华人民共和国,发明专利,200520023003,2006-12-13.
[103]上海交通大学.全对称球型机器人.中华人民共和国,发明专利,200710037291,2007-08-08.
[104]卢亮,孙汉旭,褚明.一种全向运动球型机器人的方案设计和动力学分析.机械工程,2012.
[105]Antonelli G, Chiaverini S, Fusco G. A fuzzy-logic-based approach for mobile robot path tracking. IEEE Transactions on Fuzzy Systems,2007,15(2):211-221.
[106]Juang Ch-F, Hsu Ch-H. Reinforcement ant optimized fuzzy controller for mobile-robot wall-following control. IEEE Transactions on Industrial Electronics,2009,56(10):3931-3940.
[107]Yang K, Sukkarieh S. Real-time continuous curvature path planning of UAVs in cluttered environments. In Proceedings of the5th International Symposium on Mechatronics and Its Applications,2008:1-6.
[108]Yang K, Sukkarieh S.3D smooth path planning for a UAV in cluttered nature environments. In Proceedings of the IEEE International Conference on Intelligent Robots and Systems,2008:794-800.
[109]Yang K, Sukkarieh S. Planning continuous curvature path for UAVs amongst obstacles. In Proceedings of the Australasian Conference on Robotics and Automation,2008.
[110]Yang K, Gan S K, Sukkarieh S. An efficient path planning and control algorithm for RUAV's in unknown and cluttered environments. Journal of Intelligent and Robotic Systems,2010,57(1-4):101-122.
[111]Yang K, Sukkarieh S. An analytical continuous-curvature path-smoothing algorithm. IEEE Transactions on Robotics,2010,26(3):561-568.
[112]Hoy M, Matveev A S, Savkin A V. Collision free cooperative navigation of multiple wheeled robots in unknown cluttered environments. Robotics and Autonomous Systems,2012,60(10):1253-1266.
[113]Bianco C G L, Gerelli O. Generation of paths with minimum curvature derivative with η3-splines. IEEE Transactions on Automation Science and Engineering,2010,7(2):249-256.
[114]Conner D C, Choset H, Rizzi A A. Integrating planning and control for single-bodied mobile robots. Autonomous Robots,2011,30(3):243-264.
[115]Choi J-W, Elkaim G H. Bezier curves for trajectory guidance. World Congress on Engineering and Computer Science,2008:625-630.
[116]Choi J-W, Curry R E, Elkaim G H Path planning based on Bezier curves for autonomous ground vehicles. In Proceedings of the Advances in Electrical and Electronics Engineering-IAENG Special Edition of the World Congress on Engineering and Computer Science,2008:158-166.
[117]Choi J-W, Curry R E, Elkaim G H. Smooth path generation based on Bezier curves for autonomous vehicles. In Proceedings of the World Congress on Engineering and Computer Science,2009:668-673.
[118]Choi J-W, Curry R E, Elkaim G H. Continuous Curvature path generation based on Bezier curves for autonomous vehicles. International Journal of Applied Mathematics,2010,40(2):91-101.
[119]Choi J-W, Curry R E, Elkaim G H. Minimizing the maximum curvature of quadratic Bezier curves with a tetragonal concave polygonal boundary constraint. Computer-Aided Design,2012,44(4):311-319.
[120]常飞,赵忆文.基于模型参数估计的履带式移动机器人航向保持.仪器仪表学报,2009,30(10)：38-41.
[121]Lipschutz M M. Schaum's outline of differential geometry. New York:McGraw-Hill,1969.80-101.
[122]Dubins L. On the curves of minimal length with a constraint on average curvature, and with prescribed initial and terminal positions and tangents. American Journal of Mathematic,1957,79(3):4497-516.
[123]Scheuer A, Fraichard Th. Continuous-curvature path planning for car-like vehicles. In Proceedings of IEEE international Conference on Intelligent Robots and Systems,1997:867-873.
[124]吴友谦,裴海龙.基于Dubins曲线的无人直升机轨迹规划.计算机工程与设计,2011,32(4)：1426-1429,1448.
[2]Siegwart R, Nourbakhsh I R. Introduction to autonomous mobile robots. Cambridge: The MIT Press,2004.257-298.
[3]Tarokh M. Hybrid intelligent path planning for articulated rovers in rough terrain. Fuzzy Sets and System,2008,159(21):2927-2937.
[4]Dolgov D, Thrun S, Montemerlo M, Diebel J. Path planning for autonomous vehicles in unknown semi-structured environments. The International Journal of Robotics Research,2010,29(5):485-501.
[5]Jan G E, Chang K Y, Parberry I. Optimal path planning for mobile robot navigation. IEEE/ASME Transaction on Mechatronics,2008,13(4):451-460.
[6]谢础,贾玉红,黄俊,吴永康.航空航天技术概论(第2版).北京：北京航空航天大学出版社,2008.3-11.
[7]徐玉如,庞永杰,甘永,孙玉山.智能水下机器人技术展望.智能系统学报,2006,1(1)：9-16.
[8]葛晖,徐德民,项庆睿.自主式水下航行器控制技术新进展.鱼雷技术,2007,15(3)：1-7,14.
[9]Pioneer robot to inspect Chernobyl. Power Engineering,1999,103(1):146.
[10]Burgard W, Cremers A B, Fox D, et al. Experiences with an interactive tour-guide robot. Artificial Intelligence,1999,114(1-2):3-55.
[11]Scheuerman R J, Merkle W O. Automated guided vehicle. United States, United States Patent Application Publication, US2011/0106352A1,2011-05-05.
[12]Thrun S, Hahnel D, Ferguson D, et al. A system for volumetric robotic mapping of abandoned mines. In Proceedings of the2003IEEE International Conference on Robtics and Automation,2003,3:4270-4275.
[13]Nuchter A, Surmann H, Lingemann K, et al.6D SLAM with an application in autonomous mine mapping. In Proceedings of the2004IEEE International Conference on Robtics and Automation,2004,2:1998-2003.
[14]Baker C, Morris A, Ferguson D, et al. A campaign in autonomous mine mapping. In Proceedings of the2004IEEE International Conference on Robtics and Automation,2004,2:2004-2009.
[15]Thrun S, Thayer S, Whittaker W, et al. Autonomous exploration and mapping of abandoned mines. Robotics and Automation Magazine, IEEE,2004,11(4):79-91.
[16]Morris A, Ferguson D, Omohundro Z, et al. Recent developments in subterranean robotics. Journal of Field Robotics,2006,23(1):35-57.
[17]Silver D, Ferguson D, Morris A, et al. Feature extraction for topological mine maps. In Proceedings of the IEEE International Conference on Intelligent Robotics and Systems,2004,1:773-779.
[18]Ng J, Braunl T. Performance comparison of Bug navigation algorithms. Journal of Intelligent and Robotic Systems,2007,50(1):73-84.
[19]Lumelsky V J, Stepanov A A. Dynamic path planning for a mobile automaton with limited information on the environmen. IEEE Transactions on Automatic Control,1986,31(11):1058-1063.
[20]Lumelsky V J, Stepanov A A. Path-planning strategies for a point mobile automaton moving amidst unknown obstacles for arbitrary shape. Algorithmica,1987,2(1-4):403-430.
[21]Sankaranarayanan A, Vidyasagar M. A new path planning algorithm for moving a point object amidst unknown obstacles in a plane. In Proceedings of the IEEE International Conference on Robotics and Automation,1990,3:1930-1936.
[22]Sankaranarayanan A, Vidyasagar M. Path planning for moving a point objects amidst unknownobstacles in a plane:a new algorithm and a general theory for algorithm development. In Proceedings of the29th IEEE International Conference on Decision and Control,1990,2:1111-1119.
[23]Noborio H, Maeda Y, Urakawa K. A comparative study of sensor-based path-planning algorithms in an unknown maze. In Proceedings of the IEEE International Conference on Intelligent Robotics and Systems,2000,2:909-916.
[24]Kamon I, Rivlin E. Sensory-based motion planning with global proofs. IEEE Transactions on Robotics and Automation,1997,13(6):814-822.
[25]Lumelsky V J, Skewis T. Incorporating range sensing in the robot navigation function. IEEE Transactions on System, Man, and Cybernetics,1990,20(5):1058-1068.
[26]Kamon I, Rimon E, Rivlin E. TangentBug:a range-sensor-based navigation algorithm. International Journal of Robotics Research,1998,17(9):934-953.
[27]Laubach S L, Burdick J W. An autonomous sensor-based path-planner for planetary microrovers. In Proceedings of the IEEE International Conference on Robotics and Autom ation,1999,1:347-354.
[28]Laubach S, Burdick J. RoverBug:long range navigation for mars rovers. Lecture Notes in Control and Information Sciences,2000,250/2000:339-348.
[29]Magid E, Rivlin E. CautiousBug:a competitive algorithm for sensor-based robot navigation. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems,2004,3:2757-2762.
[30]Taylor K, LaValle S M. I-Bug:an intensity-based bug algorithm. In Proceedings of the ICRA'09IEEE International Conference on Robotics and Automation,2009:3981-3986.
[31]Buniyamin N, Wan Ngah W A J, Sariff N, Mohamad Z. A simple local path planning algorithm for autonomous mobile robots. International Journal of Systems Applications, Engineering and Development,2011,2(5):151-159.
[32]Guruprasad K R. EgressBug:a real time path planning algorithm for a mobile robot in an unknown environment. Lecture Notes in Computer Science,2012,7135/2012:228-236.
[33]Russell S J,Norvig P. Artifiacial Intelligent:A Modern Approach (Third Edition). USA: Prentice Hall,2009.97-107,126-129.
[34]Hart P E, Nils son N J, Raphael B. A formal basis for the heuristic determination of minimum cost paths. IEEE Trans of Systems Science and Cybernetics,1968,4(2):100-107.
[35]Trovato K I, Dorst, L. Differential A*. IEEE Transactions on Knowledge and Data Engineering,2002,14(6):1218～1229.
[36]Sun X, Koenig S. The fringe-saving A*search algorithm-a feasibility study. In Proceedings of the International Joint Conference on Artificial Intelligence,2007:2391-2397.
[37]王红卫,马勇,谢勇,郭敏.基于平滑A*算法的移动机器人路径规划.同济大学学报(自然科学版),2010,38(11)：1647-1650,1655.
[38]Sun X, Yeoh W, Koenig S. Efficient incremental search for moving target search. In Proceedings of the International Joint Conference on Artificial Intelligence,2009:615-620.
[39]Koenig S, Sun X. Comparing real-time and incremental heuristic search for real-time situated agents. Autonomous Agents and Multi-Agent Systems,2009,18(3):313-341.
[40]Stentz A. The focus sed D*algorithm for real-time replanning. In Proceedings of the International Joint Conference on Artificial Intelligence,1995:1652-1659.
[41]Koenig S, Likhachev M. D*Lite. In Proceedings of the National Conference on Artificial Intelligence,2002:476-483.
[42]Holland J H Adaptation in Natural and Artificial Systems:an Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. MA:The MIT Press,1992.1-158.
[43]Geisler T, Manikas T W. Autonomous robot navigation system using a novel value encoded genetic algorithm. In Proceedings of the IEEE Midwest Symposium on Circuits and Systems, Tulsa, United states,2002,3:45～48.
[44]Sedighi K H, Ashenayi K, et al. Autonomous local path planning for a mobile robot using a genetic algorithm. Congress on Evolutionary Computation,2004,2:1338～1345.
[45]Tarokh M. Hybrid intelligent path planning for articulatedrovers in rough terrain. Fuzzy Sets and Systems,2008,159(21):2927-2937.
[46]Ismail AL T, A Sheta, AI-Weshah M. A mobile robot path planning using genetic algorithm in static environment. Journal of Computer Science,2008,4(4):341～344.
[47]Wei J-H, Liu J-S. Mobile robot path planning with η3-splines using spatial-fitness-sharing variable-length genetic algorithm. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems,2009:978-983.
[48]Fries T P. Evolutionary terrain-based navigation of autonomours mobile robots. Studies in Computational Intelligence,2009,177:209-226.
[49]Colorni A, Dorigo M, Maniezzo V. Distributed optimization by ant colonies. In Proceedings of the First European Conference on Artificial Life,1992:134-142.
[50]Dorigo M, Maniezzo V, Colorni A. Ant system:optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics, Part B:Cybernetics,1996,26(1):29-41.
[51]Dorigo M, Gambardella L M. Ant colony system:a cooperatoive learning approach to the travelling salesman problem. IEEE Transactions on Evolutionary Computation,1997,1(1):53-66.
[52]朱庆保,张玉兰.基于栅格法的机器人路径规划蚁群算法.机器人,2005,27(2)：132-136.
[53]王沛栋,冯祖洪,黄新.一种改进的机器人路径规划蚁群算法.机器人,2008,30(6)：554-560.
[54]Cong Y Z, Ponnambalam S G. Mobile robot path planning using ant colony optimization. In Proceedings of the IEEE/ASME International Conference on Advanced Intelligent Mechatronics, Singapore,2009:851-856.
[55]Ghoseiri K, Nadjari B. An ant colony optimization algorithm for the bi-objective shortest path problem. Applied Soft Computing Journal,2010,10(4):1237-1246.
[56]Wang X, Zhang Y-Y, Wu D, et al. Collision-free path planning for mobile cranes based on ant colony algorithm. Key Engineering Materials,2011,467-469:1108-1115.
[57]Wang P-D, Tang G-Y, Li Y, et al. Ant colony algorithm using endpoint approximation for robot path planning. In Proceedings of the31st Chinese Control Conference (CCC),2012:4960-4965.
[58]Ioannidis K, Sirakoulis G Ch, Andreadis I. Cellular ants:a method to create collision free trajectories for a cooperative robot team. Robotics and Autonomous Systems,2011,59(2):113-127.
[59]Khatib O. Real-time obstacle avoidance for manipulators and mobile robots. In Proceedings of the IEEE International Conference on Robotics and Automation,1985,2:500-505.
[60]Khatib O. Real-time obstacle avoidance for manipulators and mobile robots. The International Journal of Robotics Research,1986,5(1):90-98.
[61]Latombe J C. Robot Motion Planning. Norwood, MA:Kluwer Academic Publishers,1991.295-356.
[62]Khatib M, Chatila R. An extended potential field approach for mobile robot sensor-based motions. In Proceedings of the Intelligent Autonomous Systems,1995:490-496.
[63]Barraquand J, Latombe J C. Robot motion planning:a distributed representation approach. The International Journal of Robotics Research,1991,10(6):628-649.
[64]Barraquand J, Langlois B, Latombe J C. Numerical potential field techniques for robot path planning. IEEE Transactions on Systems, Man, and Cybernetics (TSMC),1992,22(2):224-241.
[65]Feder H J S, Slotine J J. Real-time path planning using harmonic potentials in dynamic environments. In Proceedings of the1997IEEE International Conference on Robtics and Automation,1997,1:874-881.
[66]Zhang T, Zhu Y, Song J Y. Real-time motion planning for mobile robots by means of artificial potential field method in unknown environment. Industrial Robot:An International Journal,2010,37(4):384-400.
[67]Mabrouk M H, McInnes C R. Solving the potential field local minimum problem using internal agent states. Robotics and Autonomous System,2008,56(12):1050-1060.
[68]Reif J H, Wang H. Social potential fields:a distributed behavioral control for autonomous robots. Robotics and Autonomous Systems,1999,27(3):171-194.
[69]Masoud A A. Decentralized self-organizing potential field-based control for individually motivated mobile agents in a cluttered environment:a vector-harmonic potential field approach. IEEE Transactions on Systems, Man, and Cybernetics,2007,37(3):372-390.
[70]Ren J, McIsaac K A, Patel R V, Peters T M. A Potential field model using generalized sigmoid functions. IEEE Transactions on Systems, Man, and Cybernetics,2007,37(2):477-484.
[71]Howden W E. The sofa problem. Computer Journal,1968,11(3):299-301.
[72]LaValle S M, Branicky M S, Lindemann S R. On the relationship between classical grid search and probabilistic roadmaps. The International Journal of Robotics Research,2004,23(7-8):673-692.
[73]顾嘉俊.移动机器人在非平坦地形上的自主导航研究：[博士学位论文].上海：上海交通大学,2010.
[74]李杰.邻域可视性相关的路径规划问题研究：[博士学位论文].安徽：中国科学技术大学,2011.
[75]Dunbabin M, Roberts J, Usher K, et al. A hybrid AUV design for shallow water reef navigation. In Proceedings of the2005IEEE International Conference on Robotics and Automation,2005,:2105-2110.
[76]Fiorelli E, Leonard N E, Bhatta P, et al. Multi-AUV control and adaptive sampling in Monterey bay. IEEE Journal of Oceanic Engineering,2006,31(4):935-948.
[77]Lapierre L, Jouvencel B. Robust nonlinear path-following control of an AUV. IEEE Journal of Oceanic Engineering,2008,33(2):89-102.
[78]宁晓琳,房建成.基于信息融合的深空探测器的自主导航方法.中国空间科学技术,2004,24(1)：66-71.
[79]Doody D. Deep Space craft:an overview of interplanetary flight. Chichester UK:Praxis Publishing Ltd,2009.75-82.
[80]Zhang C, Li L, Xu F, et al. Evolutionary route planner for unmanned air vehicles. IEEE Transaction s on Robotics,2005,21(4):609-620.
[81]Shanmugavel M, Tsourdos A, White B, et al. Co-operative path planning of multiple UAVs using Dubins pths with clothoid arcs. Control Engineering Practice,2010,18(9):1084-1092.
[82]Pehlivanoglu Y V. A new vibrational genetic algorithm enhanced with a voronoi diagram for path planning of autonomous UAV. Aerospace Science and Technology,2012,16(1):47-55.
[83]Li P, Huang X-H, Wang M. A new hybrid method for mobile robot dynamic local path planning in unknown environment. Journal of Computers,2010,5(5):773-781.
[84]Kuffner J J, LaValle S M. RRT-connect:an efficient approach to single-query path planning. In Proceedings of the IEEE International Conference on Robotics and automation,2000:995-1001.
[85]宋金泽,戴斌,单恩忠等.一种改进的RRT路径规划算法.电子学报,2010,38(2A)：225-228.
[86]LaValle S M, Kuffner J J. Rapidly-exploring random trees:progress and prospects. In Proceedings of the Algorithmic and Computational Robotics:New Directions,2001:293-308.
[87]Kuffner JJ, LaValle S M. An efficient approach to single-query path planning. In Proceedings of the IEEE International Conference on Robotics and Automation,2000:995-1001.
[88]Bruce J, Veloso M. Real-time randomized path planning for robot navigation. In Proceedings of the IEEE International Conference on Intelligent robots and Systems,2002:2383-2388.
[89]Ferguson D, Kalra N, Stentz A T. Replanning with RRTs. In Proceedings of the IEEE International Conference on Robotics and automation,2006:1243-1248.
[90]Zucker M, Kuffner J, Branicky M. Multipartite RRTs for rapid replanning in dynamic environments. In Proceedings of the IEEE International Conference on Robotics and automation,2007:1603-1609.
[91]高扬,孙树栋,赫东锋.部分未知环境中移动机器人动态路径规划方法.控制与决策,2010,25(12)：1885-1890.
[92]Saska M, Maca M, Preucil L, et al. Robot path planning using particle swarm optimization of Ferguson splines. In Proceedings of the2006IEEE Conference on Emerging Thechnologies and Facory Automation, ETFA,2006:833-839.
[93]吴宪祥,郭宝龙,王娟.基于粒子群三次样条优化的移动机器人路径规划算法.机器人,2009,31(6)：556-560.
[94]Ye J, Qu R. Fairing of parametric cubic splines. Mathematical and Computer Modelling,1999,30(5-6):121-131.
[95]Kennedy J, Eberhart R. Particle swarm optimization. In Proceedings of the IEEE International Conference on Neural Networks,1995,4:1942-1948.
[96]Poli R, Kennedy J, Blackwell T. Particle swarm optimization. Swarm Intelligence,2007,1(1):33-57.
[97]Clerc M. Particle swarm optimization. Great Britain:IST Ltd,2010.17-20.
[98]Choset H, Lynch K M, Hutchinson S, et al. The principles of robot motion-theory, algorithms, and implementation. Cambridge:The MIT Press,2005.23-30.
[99]于殿勇,安永植,郭伟.可变履带式移动机器人.高技术通讯,1995,1：17-20.
[100]Wakabayashi S, Sato H, Nishida S-I. Design and mobility evaluation of tracked lunar vehicle. Journal of Terramechanics,2009,46(3):105-114.
[101]Pin F G, Killough S M. A new family of omnidirectional and holonomic wheeled platforms for mobile robots. IEEE Transactions on Robotics and Automation,1994,10(4):480-489.
[102]北京航空航天大学.全方位运动球型机器人.中华人民共和国,发明专利,200520023003,2006-12-13.
[103]上海交通大学.全对称球型机器人.中华人民共和国,发明专利,200710037291,2007-08-08.
[104]卢亮,孙汉旭,褚明.一种全向运动球型机器人的方案设计和动力学分析.机械工程,2012.
[105]Antonelli G, Chiaverini S, Fusco G. A fuzzy-logic-based approach for mobile robot path tracking. IEEE Transactions on Fuzzy Systems,2007,15(2):211-221.
[106]Juang Ch-F, Hsu Ch-H. Reinforcement ant optimized fuzzy controller for mobile-robot wall-following control. IEEE Transactions on Industrial Electronics,2009,56(10):3931-3940.
[107]Yang K, Sukkarieh S. Real-time continuous curvature path planning of UAVs in cluttered environments. In Proceedings of the5th International Symposium on Mechatronics and Its Applications,2008:1-6.
[108]Yang K, Sukkarieh S.3D smooth path planning for a UAV in cluttered nature environments. In Proceedings of the IEEE International Conference on Intelligent Robots and Systems,2008:794-800.
[109]Yang K, Sukkarieh S. Planning continuous curvature path for UAVs amongst obstacles. In Proceedings of the Australasian Conference on Robotics and Automation,2008.
[110]Yang K, Gan S K, Sukkarieh S. An efficient path planning and control algorithm for RUAV's in unknown and cluttered environments. Journal of Intelligent and Robotic Systems,2010,57(1-4):101-122.
[111]Yang K, Sukkarieh S. An analytical continuous-curvature path-smoothing algorithm. IEEE Transactions on Robotics,2010,26(3):561-568.
[112]Hoy M, Matveev A S, Savkin A V. Collision free cooperative navigation of multiple wheeled robots in unknown cluttered environments. Robotics and Autonomous Systems,2012,60(10):1253-1266.
[113]Bianco C G L, Gerelli O. Generation of paths with minimum curvature derivative with η3-splines. IEEE Transactions on Automation Science and Engineering,2010,7(2):249-256.
[114]Conner D C, Choset H, Rizzi A A. Integrating planning and control for single-bodied mobile robots. Autonomous Robots,2011,30(3):243-264.
[115]Choi J-W, Elkaim G H. Bezier curves for trajectory guidance. World Congress on Engineering and Computer Science,2008:625-630.
[116]Choi J-W, Curry R E, Elkaim G H Path planning based on Bezier curves for autonomous ground vehicles. In Proceedings of the Advances in Electrical and Electronics Engineering-IAENG Special Edition of the World Congress on Engineering and Computer Science,2008:158-166.
[117]Choi J-W, Curry R E, Elkaim G H. Smooth path generation based on Bezier curves for autonomous vehicles. In Proceedings of the World Congress on Engineering and Computer Science,2009:668-673.
[118]Choi J-W, Curry R E, Elkaim G H. Continuous Curvature path generation based on Bezier curves for autonomous vehicles. International Journal of Applied Mathematics,2010,40(2):91-101.
[119]Choi J-W, Curry R E, Elkaim G H. Minimizing the maximum curvature of quadratic Bezier curves with a tetragonal concave polygonal boundary constraint. Computer-Aided Design,2012,44(4):311-319.
[120]常飞,赵忆文.基于模型参数估计的履带式移动机器人航向保持.仪器仪表学报,2009,30(10)：38-41.
[121]Lipschutz M M. Schaum's outline of differential geometry. New York:McGraw-Hill,1969.80-101.
[122]Dubins L. On the curves of minimal length with a constraint on average curvature, and with prescribed initial and terminal positions and tangents. American Journal of Mathematic,1957,79(3):4497-516.
[123]Scheuer A, Fraichard Th. Continuous-curvature path planning for car-like vehicles. In Proceedings of IEEE international Conference on Intelligent Robots and Systems,1997:867-873.
[124]吴友谦,裴海龙.基于Dubins曲线的无人直升机轨迹规划.计算机工程与设计,2011,32(4)：1426-1429,1448.