Decentralized Motion Planning for Multiple Mobile Robots: The Cocktail Party Model

This paper presents an approach for decentralized real-time motion planning for multiple mobile robots operating in a common 2-dimensional environment with unknown stationary obstacles. In our model, a robot can see (sense) the surrounding objects. It knows its current and its target's position, is able to distinguish a robot from an obstacle, and can assess the instantaneous motion of another robot. Other than this, a robot has no knowledge about the scene or of the paths and objectives of other robots. There is no mutual communication among the robots; no constraints are imposed on the paths or shapes of robots and obstacles. Each robot plans its path toward its target dynamically, based on its current position and the sensory feedback; only the translation component is considered for the planning purposes. With this model, it is clear that no provable motion planning strategy can be designed (a simple example with a dead-lock is discussed); this naturally points to heuristic algorithms. The suggested strategy is based on maze-searching techniques. Computer simulation results are provided that demonstrate good performance and a remarkable robustness of the algorithm (meaning by this a virtual impossibility to create a dead-lock in a random scene).     

Motion planning for cooperative unicycle-type mobile robots with limited sensing ranges: A distributed receding horizon approach

This paper presents a decentralized motion planner for a team of nonholonomic mobile robots subject to constraints imposed by sensors and the communication network. The motion planning scheme consists of decentralized receding horizon planners that reside on each vehicle to achieve coordination among flocking agents. The advantage of the proposed algorithm is that each vehicle only requires local knowledge of its neighboring vehicles. The main requirement for designing an optimal conflict-free trajectory in a decentralized way is that each robot does not deviate too far from its presumed trajectory designed without taking the coupling constraints into account. A comparative study between the proposed algorithm and other existing algorithms is provided in order to show the advantages, especially in terms of computing time. Finally, experiments are performed on a team of three mobile robots to demonstrate the validity of the proposed approach.     

Motion planning for formations of mobile robots

This paper is concerned with planning the motion of mobile robots in formation, which means certain geometrical constraints are imposed on the relative positions and orientations of the robots throughout their travel. Specifically, a method of planning motion for formations of mobile robots with non-holonomic constraints is presented. The kinematic equations developed allow a certain class of formations to be maintained while the group as a whole exhibits motion. The work was validated using the Stanford Micro-Autonomous RoverS Testbed.     

非完整约束下的机器人运动规划算法

由非完整约束定义提出一种改进RRT(快速搜索随机树)算法,解决受动力学约束的移动机器人运动规划问题.该算法将移动机器人的非完整约束条件与RRT搜索算法相结合.针对RRT算法在全局状态空间均匀随机搜索导致算法无谓耗费代价大的缺陷,引入目标偏向思想,并选择计算复杂度低的距离参数提高求解速度.通过几类典型的非完整约束下的机器...     

Motion control for a wheeled robot following a curvilinear path

A control synthesis problem for planar motion of a wheeled robot with regard to the steering gear dynamics is considered. The control goal is to bring the robot to a given curvilinear path and to stabilize its motion along the path. The trajectory is assumed to be an arbitrary parameterized smooth curve satisfying some additional curvature constraints. A change of variables is found by means of which the system of differential equations governing controlled motion of the robot reduces to the form that admits feedback linearization. A control law is synthesized for an arbitrary target path with regard to phase and control constraints. The form of the boundary manifold and the phase portrait of the system for the case of the straight target trajectory are studied. Results of numerical experiments are presented.     

Waypoint Following for Differentially Driven Wheeled Robots with Limited Velocity Perturbations

In this paper a unified motion control strategy dedicated for the waypoint following task realized by a differentially driven robot is presented. It is assumed that the vehicle moves with limited velocities and accelerations in order to reduce excessive slip and skid effects. In order to include operational constraints, a motion planner is combined with a universal stabilizer taking advantage of transverse functions. To improve tracking precision translated transverse functions are deployed and a new adaptive technique for the controller tuning is proposed. During the motion planning stage an auxiliary trajectory connecting points in the configuration space and satisfying assumed phase constraints is generated. The resulting motion execution system has been implemented on a laboratory-scale skid-steering mobile robot, which served as platform for experimental validation of presented algorithms.     

Motion planning for humanoid robots based on virtual constraints extracted from recorded human movements

In the field of robotics there is a great interest in developing strategies and algorithms to reproduce human-like behavior. In this paper, we consider motion planning for humanoid robots based on the concept of virtual holonomic constraints. At first, recorded kinematic data of particular human motions are analyzed in order to extract consistent geometric relations among various joint angles defining the instantaneous postures. Second, a simplified human body representation leads to dynamics of an underactuated mechanical system with parameters based on anthropometric data. Motion planning for humanoid robots of similar structure can be carried out by considering solutions of reduced dynamics obtained by imposing the virtual holonomic constraints that are found in human movements. The relevance of such a reduced mathematical model in accordance with the real human motions under study is shown. Since the virtual constraints must be imposed on the robot dynamics by feedback control, the design procedure for a suitable controller is briefly discussed.     

Constrained Motion Model of Mobile Robots and Its Applications

Target detecting and dynamic coverage are fundamental tasks in mobile robotics and represent two important features of mobile robots: mobility and perceptivity. This paper establishes the constrained motion model and sensor model of a mobile robot to represent these two features and defines the k -step reachable region to describe the states that the robot may reach. We show that the calculation of the k-step reachable region can be reduced from that of 2^k reachable regions with the fixed motion styles to k + 1 such regions and provide an algorithm for its calculation. Based on the constrained motion model and the k -step reachable region, the problems associated with target detecting and dynamic coverage are formulated and solved. For target detecting, the k-step detectable region is used to describe the area that the robot may detect, and an algorithm for detecting a target and planning the optimal path is proposed. For dynamic coverage, the k-step detected region is used to represent the area that the robot has detected during its motion, and the dynamic-coverage strategy and algorithm are proposed. Simulation results demonstrate the efficiency of the coverage algorithm in both convex and concave environments.     

异构多目标差分-动态窗口算法 及其在移动机器人中的应用

为了实现在多移动机器人和多窄通道的复杂动态环境中机器人的节能运动规划,提出异构多目标差分-动态窗口法(heterogeneous multi-objective differential evolution-dynamic window algorithm,HMODE-DWA).首先,建立行驶时间、执行器作用力和平滑度的3目标优化模型,设计具有碰撞约束的异构多目标差分进化算法来获得3个目标函数的最优解,进而在已知的静态环境中获得帕累托前沿,利用平均隶属度函数获得起点与终点间最优的全局路径;其次,定义基于环境缓冲区域的模糊动态窗口法使机器人完成动态复杂环境中避障,利用所提出的HMODE-DWA算法动态避障的同时实现节能规划.仿真和实验结果表明,所提出的混合路径规划控制策略能够有效降低移动机器人动态避障过程中的能耗.     

Motion planning for tree climbing with inchworm‐like robots

This paper proposes a global path‐ and motion‐planning algorithm that enables inchworm‐like robots to navigate their way up tree branches. The intuitive climbing space representation method proposed here greatly simplifies the path‐planning problem. The dynamic programming algorithm can be used to identify the optimal path leading to the target position in the target direction according to the constraints and requirements specified. The planned path can be applied in any tree‐climbing robot that utilizes the nonenclosure gripping method. An efficient motion‐planning algorithm for continuum inchworm‐like robots is then developed to enable them to climb along the planned path with a high degree of accuracy. In comparison with the method proposed in our previous study, the method proposed herein significantly improves consistency between the planned path and the motions of the robot, and therefore makes it more practical to implement the motion‐planning algorithm in trees of different shapes. The paper also describes hardware experiments in which the proposed planning algorithm is applied to enable inchworm‐like robots to climb real trees, thus validating the proposed planning algorithm in practice. © 2012 Wiley Periodicals, Inc.     

带滚动约束轮移式机器人动态规划的研究

根据轮移式机器人的运动学模型,研究受到滚动约束轮移式机器人在动态环境中的运动规划问题.将快速随机搜索树算法与优化方法相结合,实现了一种新的算法,规划出既可避障又可满足机器人滚动约束的运动.将该算法运用到动态环境下机器人的运动规划中,并通过仿真表明该算法能较好地引导机器人在动态环境中实现满足滚动约束的避障路径.     

Dynamics and Motion Planning of Trident Snake Robot

The trident snake robot is a mechanical device that serves as a demanding testbed for motion planning and control algorithms of constrained non-holonomic systems. This paper provides the equations of motion and addresses the motion planning problem of the trident snake with dynamics, equipped with either active joints (undulatory locomotion) or active wheels (wheeled locomotion). Thanks to a partial feedback linearization of the dynamics model, the motion planning problem basically reduces to a constrained kinematic motion planning. Two kinds of constraints have been taken into account, ensuring the regularity of the feedback and the collision avoidance between the robot’s arms and body. Following the guidelines of the endogenous configuration space approach, two Jacobian motion planning algorithms have been designed: the singularity robust Jacobian algorithm and the imbalanced Jacobian algorithm. Performance of these algorithms have been illustrated by computer simulations.     

基于路径跟踪的拖车式移动机器人动态窗口法

针对目前局部路径规划算法只适用于单车体机器人的问题,提出了一种针对拖车式移动机器人的动态窗口法。首先,利用多车体结构的路径跟踪方程实现对拖车式移动机器人的运动控制;然后,利用评价函数同时对牵引车和拖车进行评价并根据权重相加;最后,针对拖车结构特性,添加了运动过程中牵引车与拖车的夹角约束,保证运动轨迹的稳定性。仿真实验表明:拖车式移动机器人的运动控制可满足收敛性,同时所提算法实现了拖车式移动机器人局部路径规划的任务,且在运动过程中夹角变化均未超出限制。该研究对拖车式移动机器人的自主导航有极大的参考价值。     

Motion planning for robotic manipulators using robust constrained control

Since their first appearance in the 1970's, industrial robotic manipulators have considerably extended their application fields, allowing end-users to adopt this technology in previously unexplored scenarios. Correspondingly, the way robot motion can be specified has become more and more complex, requiring new capabilities to the robot, such as reactivity and adaptability. For an even enhanced and widespread use of industrial manipulators, including the newly introduced collaborative robots, it is necessary to simplify robot programming, thus allowing this activity to be handled by non-expert users. Next generation robot controllers should intelligently and autonomously interpret production constraints, specified by an application expert, and transform them into motion commands only at a lower and real-time level, where updated sensor information or other kind of events can be handled consistently with the higher level specifications. The availability of several execution strategies could be then effectively exploited in order to further enhance the flexibility of the resulting robot motion, especially during collaboration with humans.This paper presents a novel methodology for motion specification and robust reactive execution. Traditional trajectory generation techniques and optimisation-based control strategies are merged into a unified framework for simultaneous motion planning and control. An experimental case study demonstrates the effectiveness and the robustness of this approach, as applied to an image-guided grasping task.     

凸优化与A*算法结合的路径避障算法

为提高足式移动机器人的避障能力和路径规划效率,提出一种凸优化与A*算法结合的路径避障算法.首先,基于半定规划的迭代区域膨胀方法IRI-SDP(iterative regional inflation by semi-definite programming),通过交替使用两种凸优化算法快速计算出地面环境中无障碍凸多边形及其最大面积内切椭圆,用于移动机器人的局部避障和任务动作规划;然后,结合经典的A*算法,建立机器人局部和世界坐标系、机器人质心轨迹转换模型、碰撞模型和启发式代价函数,在全局环境中寻找最优成本最小的路径;最后,通过仿真实验验证该算法的有效性.     

A decentralized approach for the conflict-free motion of multiple mobile robots

This article presents a novel approach to decentralized motion planning and conflict-resolution for multiple mobile robots. The proposed multi-robot motion planning is an on-line operation, based on cost wave propagation within a discretized configuration space-time. By use of the planning method, a framework for negotiation is developed, which permits quick decentralized and parallel decision making. The key objective of the negotiation procedure is dynamic assignment of robot motion priorities. Thus, robots involved in a local conflict situation cooperate in planning and execution of the lowest cost motion paths without application of any centralized components. The features required for individual and cooperative motion are embedded in a hybrid control architecture. Results obtained from realistic simulation of a multi-robot environment and also from experiments performed with two mobile robots demonstrate the flexibility and the efficiency of the proposed method.     

Convex Optimization Strategies for Coordinating Large-Scale Robot Formations

This paper investigates convex optimization strategies for coordinating a large-scale team of fully actuated mobile robots. Our primary motivation is both algorithm scalability as well as real-time performance. To accomplish this, we employ a formal definition from shape analysis for formation representation and repose the motion planning problem to one of changing (or maintaining) the shape of the formation. We then show that optimal solutions, minimizing either the total distance or minimax distance the nodes must travel, can be achieved through second-order cone programming techniques. We further prove a theoretical complexity for the shape problem of O(m^1.5) as well as O(m) complexity in practice, where m denotes the number of robots in the shape configuration. Solutions for large-scale teams (1000's of robots) can be calculated in real time on a standard desktop PC. Extensions integrating both workspace and vehicle motion constraints are also presented with similar complexity bounds. We expect these results can be generalized for additional motion planning tasks, and will prove useful for improving the performance and extending the mission lives of large-scale robot formations as well as mobile ad hoc networks.     

Motion planning algorithms of an omni-directional mobile robot with active caster wheels

This work deals with motion planning algorithms of an omni-directional mobile robot with active caster wheels. A typical problem occurred in the motion control of such omni-directional mobile robot, which has been identified through experimental experiences, is skidding of the mobile wheel. It sometimes results in uncertain rotation of the steering wheel. To cope with this problem, a motion planning algorithm which resolves the skidding problem and uncertain motions of the steering wheel is mainly investigated. For navigation of the mobile robot, the posture of the omni-directional mobile robot is initially calculated using the odometry information. Then, the accuracy of the mobile robot’s odometry is measured through comparison of the odometry information and the external sensor measurement. Finally, for successful navigation of the mobile robot, a motion planning algorithm that employs kinematic redundancy resolution method is proposed. Through simulations and experimentation, the feasibility of proposed algorithms was verified.     

融合环境信息和运动约束的改进A*算法研究

标准A*算法存在着无法考虑移动机器人运动特性及处理后的路径不利于移动机器人运动等问题。针对这一问题提出了一种新改进A*算法,通过环境信息引入障碍物权重系数来改进算法的启发函数并进行全局路径规划;优化搜索节点的选取方式和设定障碍物与路径之间的安全距离;基于对移动机器人的运动特性的考虑优化其路径,并在不同环境地图中与其他算法进行仿真实验对比分析。相关实验表明：基于新改进A*算法规划的路径始终与障碍物保持一定的安全距离;改进A*算法在时间上相比标准A*算法平均减少了80%,路径长度平均减少了2%,路径转角平均降低了82%。改进后算法相比其他算法在时间、搜索节点以及平滑度上有很大的改进,融合机器人环境信息和运动特性的规划路径算法可为移动机器人的路径规划提供一种新的方法。     

三轮全方位移动机器人运动模型及其控制方法研究

全方位移动机器人在二维空间具有完整性约束条件,能够灵活地任意方向移动,受到了广泛的关注。针对三轮全方位机器人的运动控制问题,建立其运动学模型。采用输入变换解耦及状态反馈法实现对坐标位置、方位角等位姿状态的控制,通过仿真实验验证了算法的有效性。最后给出了一种三轮全方位移动机器人硬件设计方案及控制算法软件实现,实际样机运动实验验证了控制方法的可行性与有效性。