行为控制月球车路径规划技术

提出了一种新的路径规划方法即自主行为路径规划方法.该方法能够自动构造大范围 自然环境下与路径规划任务相关的一类拓扑结构,从而大大地提高自然环境下月球车路径规划 速度.该方法在Tangent-Bug切线法基础上引入一组自适应行为来构造切线拓扑图,它是人工 路径规划行为的模仿,克服了计算几何路径规划方法非线性计算时间的不足.文中给出了相关 定义、定理、算法及真实环境下的仿真结果.     

一种移动机器人环境模型——灰色定性地图

针对移动机器人的环境建模问题,提出一种综合拓扑地图和儿何地图特点的混合环境模型——灰色定性地图.用凸剖分算法将环境中的自由空间分解为一组凸多边形.灰色定性地图的定性层由凸多边形及其之间的邻接关系构成,用于模拟人类在路径规划时的高层定性推理.定量层由凸多边形顶点的坐标和势场向量构成,用于决定机器人在连续空间中的运动方向和速度.理论分析和实验均表明:灰色定性地图可以模拟人类对环境认知的知识表达,并且可以仪由凸多边形邻接信息和顶点信息支持机器人完成路径规划且确保路径的平滑性,有效地降低了环境模型的空间复杂度.     

智能机器人的一种新路径规划算法

为了使智能机器人的运动过程更加顺利快速,使其用更短的时间和更短的路径到达终点,采用了基于几何理论的路径规划算法,寻求智能机器人最优路径规划。该算法利用切线最短的理论优化机器人的运动过程,对智能机器人运动的每一段路径都进行了规划和优化,使智能机器人的整个运动过程更加顺畅。在实际应用中,成功地缩短了机器人的运动路径,并成功地进行了避障。这种方法使用简单,容易理解,可广泛应用于智能机器人的路径规划和避障系统中,在实际应用中更能减少能量损耗。     

自主车辆越野路径规划的新方法——领域式规划法

本文提出了一种解决 ALV(Autonomous Land Vehicle)越野路径规划的新途径——领域式规划法.介绍了一种面向地形本身分布特点和性质的地形分割方法,讨论了领域式规划空间的二叉树表示方法.在此基础上,提出了采用虚拟矩阵匹配搜索二叉树以确定领域式规划空间中邻接关系的方法,并描述了启发式方法和概率方法相结合的路径搜索算法.领域式规划方法有效地解决了非结构化环境中的规划效率问题.     

不确定动态环境下移动机器人的完全遍历路径规划

基于生物激励神经网络、滚动窗口和启发式搜索，提出了一种新的完全遍历路径规划方法．该方法用Grossberg的生物神经网络实现移动机器人的局部环境建模，将滚动窗口的概念引入到局部路径规划，由启发式算法决定滚动窗口内的局域路径规划目标．该方法能在不确定动态环境中有效地实现机器人自主避障的完全遍历路径规划．仿真研究证明了该方法的可用性和有效性．     

基于局部探测信息的机器人滚动路径规划

用基于滚动窗口的路径规划方法研究了全局环境未知时的机器人路径规划问题.该法 充分利用机器人实时测得的局部环境信息,以滚动方式进行在线规划,实现了优化与反馈的合理 结合.文中分析了不同凸障碍环境下滚动路径规划子目标选择策略,并且还探讨了规划算法的可 达性.     

基于蚁群算法的机器人路径规划

针对移动机器人规避障碍和寻找最优路径问题,提出了在复杂环境下移动机器人的一种路径规划方法.采用了栅格法建立了机器人工作平面的坐标系,整个系统由全局路径规划和局部避碰规划两部分组成.在全局路径规划中,用改进蚁群算法规划出初步全局优化路径;局部避碰规划是在跟踪全局优化路径的过程中,通过基于滚动窗口的环境探测和碰撞预测,对动态障碍物实施有效的局部避碰策略,从而使机器人能够安全顺利的到达目标点.仿真实验的结果表明了所述方法能在较短时间内找到最佳路径并规避障碍.     

一种蚂蚁遗传融合的机器人路径规划新算法

针对栅格法建模的不足,本文研究一种全新的蚂蚁算法与遗传算法融合的机器人路径规划算法.该方法首先用栅格法建立机器人运动空间模型,在此基础上利用蚂蚁算法进行全局搜索得到全局导航路径,然后用遗传算法局部调节全局导航路径上的路径点,得到更优路径.计算机仿真实验表明,即使在复杂的环境下,利用本算法也可以规划出一条全局优化路径,且能安全避障.     

动态调整操作序列的多机器人无碰运动规划

针对共享工作空间的多台机器人,提出了一种协调无碰运动规划方法.作为离线规划的解耦法,该方法主要分为2个阶段.第1阶段,根据任务需求在不考虑机器人间相互冲突的情况下,通过概率路径地图(PRM)法规划出各机器人与静态环境的无碰路径;第2阶段,把机器人的路径描述成连续的位形序列后构造系统的状态空间,形象地把所需解决的问题转换成高维状态空间中的连续路径搜索问题.在此基础上,提出了多机器人的避碰策略、运动序列优先级的动态调整方法和改进的A*算法,实现了多机器人系统无碰协调运动规划.通过2个仿真案例验证了该方法的可行性及有效性.结果表明,所提方法能快速、有效地得到多机器人协调无碰运动路径.     

足球机器人动态路径规划方法研究

针对机器人足球系统的高度实时性、不确定性,提出了一种基于统计预测的路径规划方法,该方法考虑到障碍物的速度大小和方向的不确定性,用数学统计的方法对障碍物的运动进行建模;机器人在运动过程中,根据得到的环境信息在机器视觉范围内建立预测窗口和避障窗口,在预测窗口内,机器人根据障碍物的信息建立障碍物的预测区域,在避障窗口内,机器人根据自身的位置与障碍物的预测区域,分别调用切线法或滚动窗口法进行路径规划;该方法属于局部路径规划方法,机器人在移动过程中需要不断更新环境信息来进行避障.     

A Gaussian process-based RRT planner for the exploration of an unknown and cluttered environment with a UAV

A new framework which adopts a rapidly-exploring random tree (RRT) path planner with a Gaussian process (GP) occupancy map is developed for the navigation and exploration of an unknown but cluttered environment. The GP map outputs the probability of occupancy given any selected query point in the continuous space and thus makes it possible to explore the full space when used in conjunction with a continuous path planner. Furthermore, the GP map-generated path is embedded with the probability of collision along the path which lends itself to obstacle avoidance. Finally, the GP map-building algorithm is extended to include an exploration mission considering the differential constraints of a rotary unmanned aerial vehicle and the limitation arising from the environment. Using mutual information as an information-theoretic measure, an informative path which reduces the uncertainty of the environment is generated. Simulation results show that GP map combined with RRT planner can achieve the 3D navigation and exploration task successfully in unknown and complex environments.     

基于变维度状态空间的增量启发式路径规划方法研究

在移动机器人路径规划中需要考虑运动几何约束,同时,由于它经常工作于动态、时变的环 境中,因此,还必须保证路径规划算法的效率.本文提出了一种基于变维度状态空间的增量启发式路径规划 方法,该方法既能满足移动机器人的运动几何约束,又能保证规划算法的效率.首先,设计了变维度状态空间, 在机器人周围的局部区域考虑运动几何约束组织高维状态空间,其他区域组织低维状态空间;然后,基于变维 度状态空间,提出了一种增量启发式路径规划方法,该方法在新的规划进程中可以使用以前的规划结果,仅对 机器人周围的局部区域进行重搜索,从而能保证算法的增量性及实时性;最后,通过仿真计算和机器人实验验 证了算法的有效性.     

Intelligent adaptive immune-based motion planner of a mobile robot in cluttered environment

Learning of an autonomous mobile robot for path generation includes the use of previous experience to obtain the better path within its work space. When the robot is moving in its search space for target seeking, each task requires different form of learning. Therefore, the modeling of an efficient learning mechanism is the hardest problem for an autonomous mobile robot. To solve this problem, the present research work introduced an adaptive learning-based motion planner using artificial immune system, called adaptive immune-based path planner. Later the developed adaptive mechanism has been integrated to the innate immune-based path planner in order to obtain the better results. To verify the effectiveness of the proposed adaptive immune-based motion planner, simulation results as well as experimental results are presented in various unknown environments.     

Integration of Path/Maneuver Planning in Complex Environments for Agile Maneuvering UCAVs

In this work, we consider the problem of generating agile maneuver profiles for Unmanned Combat Aerial Vehicles in 3D Complex environments. This problem is complicated by the fact that, generation of the dynamically and geometrically feasible flight trajectories for agile maneuver profiles requires search of nonlinear state space of the aircraft dynamics. This work suggests a two layer feasible trajectory/maneuver generation system. Integrated Path planning (considers geometrical, velocity and acceleration constraints) and maneuver generation (considers saturation envelope and attitude continuity constraints) system enables each layer to solve its own reduced order dimensional feasibility problem, thus simplifies the problem and improves the real time implement ability. In Trajectory Planning layer, to solve the time depended path planning problem of an unmanned combat aerial vehicles, we suggest a two step planner. In the first step, the planner explores the environment through a randomized reachability tree search using an approximate line segment model. The resulting connecting path is converted into flight way points through a line-of-sight segmentation. In the second step, every consecutive way points are connected with B-Spline curves and these curves are repaired probabilistically to obtain a geometrically and dynamically feasible path. This generated feasible path is turned in to time depended trajectory with using time scale factor considering the velocity and acceleration limits of the aircraft. Maneuver planning layer is constructed upon multi modal control framework, where the flight trajectories are decomposed to sequences of maneuver modes and associated parameters. Maneuver generation algorithm, makes use of mode transition rules and agility metric graphs to derive feasible maneuver parameters for each mode and overall sequence. Resulting integrated system; tested on simulations for 3D complex environments, gives satisfactory results and promises successful real time implementation.     

Optimisation based path planning for car parking in narrow environments

In the last decades, a large variety of robot motion planners emerged. However, manoeuvring in very narrow environments, e.g., for common parking scenarios, cannot be reliably handled with existing path planners at low computing costs. This is why, this paper presents a fast optimisation based path planner which specialises on narrow environments. In the proposed approach, the kinematic differential equations are discretised. For the resulting discrete path segments, a static optimisation problem is formulated to determine the path independently of the considered scenario. In each iteration step, the path length is also optimised to handle close distances to the obstacles as well as longer driving distances. Due to the local nature heuristic rules for driving direction changes are formulated which intend to imitate the behaviour of a human driver. The drawback of the local nature is the lack of global information to handle scenarios with obstacles blocking the path. To overcome this problem, a tree-based guidance for the local planner is introduced. The landmarks for the tree are implicitly created by the local planner allowing an efficient exploration of the free configuration space. The performance of the algorithm is evaluated utilising Monte-Carlo simulations and compared to state-of-the-art path planning algorithms.     

ON THE PATH PLANNING PROBLEM IN THE VICINITY OF OBSTACLES

This paper presents a new approach to motion planning in the neighborhood of obstacles. The technique presented here, the configuration space vector path planner CSVPP , generates a collision-free path for a robot amongst unknown arbitrarily shaped obstacles. The CSVPP algorithm utilizes discrete vectors in the configuration space of the robot to generate a path between any two points in the robot's dynamic time-varying workspace. The calculation of the robot's path assumes interpolated joint control, and provides a computational speed that enables the algorithm to be implemented in real time. A number of simulations are provided for several varying environments.     

Planning in highly dynamic environments: an anytime approach for planning under time constraints

In this paper, we present a novel and domain-independent planner aimed at working in highly dynamic environments with time constraints. The planner follows the anytime principles: a first solution can be quickly computed and the quality of the final plan is improved as long as time is available. This way, the planner can provide either fast reactions or very good quality plans depending on the demands of the environment. As an on-line planner, it also offers important advantages: our planner allows the plan to start its execution before it is totally generated, unexpected events are efficiently tackled during execution, and sensing actions allow the acquisition of required information in partially observable domains. The planning algorithm is based on problem decomposition and relaxation techniques. The traditional relaxed planning graph has been adapted to this on-line framework by considering information about sensing actions and action costs. Results also show that our planner is competitive with other top-performing classical planners.     

Planning collision-free trajectories in time-varying environments: a two-level hierarchy

We propose a two-level hierarchy for planning collision-free trajectories in time varying environments. Global geometric algorithms for trajectory planning are used in conjunction with a local avoidance strategy. Simulations have been developed for a mobile robot in the plane among stationary and moving obstacles. Essentially, the robot has a global geometric planner that provides a coarse global trajectory (the path and velocity along it), which may be locally modified by the low-level local avoidance module if local sensors detect any obstacles in the vicinity of the robot. This hierarchy makes effective use of the complementary aspects of the global trajectory planning approaches and the local obstacle avoidance approaches.     

A chaotic coverage path planner for the mobile robot based on the Chebyshev map for special missions

We introduce a novel strategy of designing a chaotic coverage path planner for the mobile robot based on the Chebyshev map for achieving special missions. The designed chaotic path planner consists of a two-dimensional Chebyshev map which is constructed by two one-dimensional Chebyshev maps. The performance of the time sequences which are generated by the planner is improved by arcsine transformation to enhance the chaotic characteristics and uniform distribution. Then the coverage rate and randomness for achieving the special missions of the robot are enhanced. The chaotic Chebyshev system is mapped into the feasible region of the robot workplace by affine transformation. Then a universal algorithm of coverage path planning is designed for environments with obstacles. Simulation results show that the constructed chaotic path planner can avoid detection of the obstacles and the workplace boundaries, and runs safely in the feasible areas. The designed strategy is able to satisfy the requirements of randomness, coverage, and high efficiency for special missions.     

Geometry and search in motion planning

Robot path planning is a typical example of a problem that requires searching a “continuous” space, the robot's configuration space, for a solution, a collision-free path. The global approach to path planning first captures the connectivity of the robot's free space into a concise connectivity path, and next searches this graph. The local approach directly embarks on a search procedure, and performs geometric computation according to the needs of the search. Global methods may waste a large amount of computation before they have any chance to find a path. On the other hand, local methods, which lack the global vision provided by the connectivity graph, have very poor worst-case complexity. Is it possible to instill some local opportunism in a global approach, or a limited amount of precomputed global information in a local approach? More generally: How can geometric computation and search help each other to produce a path quickly? These questions probably do not have definite domain-independent answers. However, raising them may help us engineer path planners that better meet specific application needs. This paper considers these questions through a series of informal case studies, each corresponding to a particular way to engineer the interaction between geometry and search in a path planner.