A real-time planning algorithm for obstacle avoidance of redundant robots

A computationally efficient, obstacle avoidance algorithm for redundant robots is presented in this paper. This algorithm incorporates the neural networks and pseudodistance function D _p in the framework of resolved motion rate control. Thus, it is well suited for real-time implementation. Robot arm kinematic control is carried out by the Hopfield network. The connection weights of the network can be determined from the current value of Jacobian matrix at each sampling time, and joint velocity commands can be generated from the outputs of the network. The obstacle avoidance task is achieved by formulating the performance criterion as D _p>d _min (d _min represents the minimal distance between the redundant robot and obstacles). Its calculation is only related to some vertices which are used to model the robot and obstacles, and the computational times are nearly linear in the total number of vertices. Several simulation cases for a four-link planar manipulator are given to prove that the proposed collision-free trajectory planning scheme is efficient and practical.     

基于模糊力控制算法的移动机器人避障控制

根据机器人的末端执行器和外界环境表面接触与移动机器人避障控制的相似点,将力/位置控制成功应用到移动机器人的避障控制领域内.对新颖的移动机器人避障控制算法是通过在移动机器人和障碍物之间形成虚拟力场,且对其进行整定以使两者之间能保持期望的距离.因为机器人动力学模型和障碍物的不确定性会对避障控制性能造成影响,为避免碰撞,采用模糊PD的智能混合力/位置控制来整定机器人和障碍物精确距离的力场.通过仿真研究证明了算法的有效性,可为机器人设计提出可靠依据.     

Collision-avoidance for redundant robots through control of the self-motion of the manipulator

A new method to on-line collision-avoidance of the links of redundant robots with obstacles is presented. The method allows the use of redundant degrees of freedom such that a manipulator can avoid obstacles while tracking the desired end-effector trajectory. It is supposed that the obstacles in the workspace of the manipulator are presented by convex polygons. The recognition of collisions of the links of the manipulator with obstacles results on-line through a nonsensory method. For every link of the redundant manipulator and every obstacle a boundary ellipse is defined in workspace such that there is no collision if the robot joints are outside these ellipses. In case a collision is imminent, the collision-avoidance algorithm compute the self-motion movements necessary to avoid the collision. The method is based on coordinate transformation and inverse kinematics and leads to the favorable use of the abilities of redundant robots to avoid the collisions with obstacles while tracking the end-effector trajectory. This method has the advantage that the configuration of the manipulator after collision-avoidance can be influenced by further requirements such as avoidance of singularities, joint limits, etc. The effectiveness of the proposed method is discussed by theoretical considerations and illustrated by simulation of the motion of three-and four-link planar manipulators between obstacles.     

Repulsive force control based on distance computation algorithm

Computer-aided repulsive force control of collision avoidance is presented in this paper. A repulsive force is artificially created using the distances between the robot links and obstacles, which are generated by a simplified distance computation algorithm. This distance computation algorithm is based on the Gilbert-Johnson-Keerthi algorithm. Control gains in the repulsive force control model are selected based on traditional control design and genetic algorithms. Results on shortest distance computation and collision detection are presented. Real-time manipulator collision avoidance control has achieved. A repulsive force gain is introduced through the approaches for definition of link coordinate frames and kinematics computations. The safety distance between objects is affected by the repulsive force gain. Safety zone can be adjustable by the repulsive force gain which is selected by a specified fitness function of the genetic algorithm.     

Path tracking with the links of a planar hyper- redundant robotic manipulator

A technique for path tracking with the links of a planar hyper-redundant robotic manipulator is presented. It is a joint-space trajectory planning method that can be used for obstacle avoidance by ensuring that the links of the manipulator closely follow a collision-free path planned for the tool. A closed-form formulation of resolved-motion rate control equations is developed that decomposes the manipulator into proximal and distal parts. The joint velocities are constrained to ensure that the far distal links remain tangent to the tool path, while the near distal links and the proximal links assume a compressed configuration near the base. Path tracking is illustrated with several examples that examine the effects of controller gain, link length, and number of links. © 1995 John Wiley & Sons, Inc.     

Obstacle Avoidance for Spatial Hyper‐Redundant Manipulators Using Harmonic Potential Functions and the Mode Shape Technique

This paper deals with the obstacle avoidance problem for spatial hyper‐redundant manipulators in known environments. The manipulator is divided into two sections, a proximal section that has not entered the space among the obstacles and a distal section among the obstacles. Harmonic potential functions are employed to achieve obstacle avoidance for the distal section in three‐dimensional space in order to avoid local minima in cluttered environments. A modified panel method is used to generate the potential of any arbitrary shaped obstacle in three‐dimensional space. An alternative backbone curve concept and an efficient fitting method are introduced to control the trajectory of proximal links. The fitting method is recursive and avoids the complications involved with solving large systems of nonlinear algebraic equations. The combination of a three‐dimensional safe path derived from the harmonic potential field and the backbone curve concept leads to an elegant kinematic control strategy that guarantees obstacle avoidance. © 2003 Wiley Periodicals, Inc.     

基于遗传算法的机械臂实时避障路径规划研究

针对模块化机械臂在运行时可能与工作空间中的障碍物发生碰撞的问题, 提出一种基于遗传算法的避障路径规划算法。首先采用D-H(Denavit-Hartenberg)表示法对机械臂进行建模, 并进行运动学和动力学分析, 建立机械臂运动学和动力学方程。在此基础上, 利用遗传算法分别在单/多个障碍物工作环境中, 以运动的时间、移动的空间距离和轨迹长度作为优化指标, 实现机械臂避障路径规划的优化。通过仿真验证了基于遗传算法的机械臂避障路径规划算法的有效性与可行性, 该算法提高了运行中的机械臂有效避开工作空间中障碍物的效率。     

基于运动平衡点的水下机器人自主避障方式

当前大多数水下机器人的实时避障方式只简单地考虑了目标和障碍物之间的相对距 离,并没有考虑实际的海洋环境以及会不会对控制器产生不利的影响．本文研究的目的就是 从实际出发,通过设定各个自由度上的运动平衡点,将水下机器人的自主避障规划和运动控 制结合起来,设计出一个集目标、障碍物和控制性能一体的避障规划方式,从仿真实验结果 来看,机器人可以安全地通过障碍空间,到达目的地．     

Obstacle avoidance of redundant manipulators using neural networks based reinforcement learning

This paper proposes a new approach for solving the problem of obstacle avoidance during manipulation tasks performed by redundant manipulators. The developed solution is based on a double neural network that uses Q-learning reinforcement technique. Q-learning has been applied in robotics for attaining obstacle free navigation or computing path planning problems. Most studies solve inverse kinematics and obstacle avoidance problems using variations of the classical Jacobian matrix approach, or by minimizing redundancy resolution of manipulators operating in known environments. Researchers who tried to use neural networks for solving inverse kinematics often dealt with only one obstacle present in the working field. This paper focuses on calculating inverse kinematics and obstacle avoidance for complex unknown environments, with multiple obstacles in the working field. Q-learning is used together with neural networks in order to plan and execute arm movements at each time instant. The algorithm developed for general redundant kinematic link chains has been tested on the particular case of PowerCube manipulator. Before implementing the solution on the real robot, the simulation was integrated in an immersive virtual environment for better movement analysis and safer testing. The study results show that the proposed approach has a good average speed and a satisfying target reaching success rate.     

冗余自由度机械手的避障控制

避障碍物一直是冗余自由度机械手的主要应用，本文采用伪逆矩阵法，以障碍物和 机械手之间的距离的函数作为性能指标函数来解冗余自由度机械手逆解，进行避障控制，并 提出一种简单的计算机械手和障碍物之间的距离方法，通过对一个三自由度的平面机械手进 行仿真，验证了算法的正确性．     

基于凸面体圆弧航路的无人机自主避障算法

为解决无人机飞行过程中障碍物规避问题,提出一种新的三维自主避障算法.首先,根据障碍物的若干信息利用标准凸面体对不规则障碍物进行数学建模,用一个或多个标准凸面体覆盖障碍物整体或关键部分;然后,根据障碍物模型设计圆弧规避航路算法,将避障问题转化为跟踪规避航路控制问题,并定义避障判定、避障方向判断和成功避障规则;最后,结合非...     

Application of a dynamic programming algorithm for trajectory planning of finger-like manipulators

Dextrous hand designs require planning and coordination of multijointed fingers. This communication presents a dynamic programming algorithm to optimize the trajectory of a finger or a fingerlike manipulator. The algorithm generates a minimum cost trajectory for a specified cost function (such as accuracy or time of travel) and a set of constraints (such as obstacles along the trajectory). We present simulations demonstrating the trajectory planning for simplified fingerlike manipulator configurations. This approach is potentially applicable to several additional situations faced by multijointed fingers employed in dextrous hand designs, such as complex trajectory planning, obstacle avoidance, and fault tolerance. We illustrate some results obtained by computer simulations of these problems.     

Adaptive flexible performance function approach for dynamic obstacle avoidance to distributed formation tracking of range-constrained switched nonlinear multiagent systems

Prescribed performance control allows preselected transient and steady-state bounds of formation control performance. However, owing to the predetermined decaying property of performance functions, it is unavailable for the formation tracking problem with dynamic obstacle avoidance. Thus, it is essential to design a flexible performance function to accommodate the increasing formation error during dynamic obstacle avoidance. This article pays attention to the development of an adaptive flexible performance (AFP) function usable for the avoidance of dynamic obstacles with unknown velocities in the distributed formation framework. We develop an AFP-based distributed formation tracker design for range-constrained switched multi-input multi-output nonlinear multiagent systems with asynchronous switching. The AFP functions for ensuring graph connectivity and dynamic obstacle avoidance are derived by designing time-varying variables adjusted by an adaptive relaxation signal. The neural-network-based adaptive distributed formation tracker using the derived performance functions is constructed with the adaptive relaxation signal to ensure the stability of the closed-loop formation system, regardless of asynchronous switching and dynamic obstacles. The effectiveness of the proposed method is shown by simulation results.     

空间机械臂在线实时避障路径规划研究

针对目前空间机械臂避障路径规划算法计算量大难以达到在线实时规划的缺点,对空间机械臂的在线实时避障路径规划问题进行了研究和探讨。采用规则体的包络对障碍物进行建模,并借助C空间法的思想,把障碍物和机械臂映射到两个相互垂直的平面内,将机械臂工作空间的三维问题转化为二维问题,并结合二岔树逆向寻优的方法进行路径搜索,从而大大减少了计算量,达到了在线实时规划的要求。最后在空间机器人仿真系统上对其进行了仿真研究,验证了该方法的可行性。     

Decentralized behavior-based formation control of multiple robots considering obstacle avoidance

This paper proposes a decentralized behavior-based formation control algorithm for multiple robots considering obstacle avoidance. Using only the information of the relative position of a robot between neighboring robots and obstacles, the proposed algorithm achieves formation control based on a behavior-based algorithm. In addition, the robust formation is achieved by maintaining the distance and angle of each robot toward the leader robot without using information of the leader robot. To avoid the collisions with obstacles, the heading angles of all robots are determined by introducing the concept of an escape angle, which is related with three boundary layers between an obstacle and the robot. The layer on which the robot is located determines the start time of avoidance and escape angle; this, in turn, generates the escape path along which a robot can move toward the safe layer. In this way, the proposed method can significantly simplify the step of the information process. Finally, simulation results are provided to demonstrate the efficiency of the proposed algorithm.     

三维动态环境下多无人机编队分布式保持控制

针对无人机编队沿参考轨迹飞行时遭遇突发障碍物而发生碰撞的问题, 提出一种可实时避障及机间避碰的分布式编队保持算法. 基于虚拟结构编队策略, 采用非线性模型预测控制(NMPC) 方法设计分布式编队控制器. 为了实现通讯延迟下的机间避碰, 采用基于不同优先级的改进避碰惩罚策略. 仿真结果表明, 所设计的分布式编队控制器能保证编队及时避开环境中的突发障碍物, 且无人机间不发生互碰, 避障后的各编队继续以原队形沿参考轨迹飞行.

     

A genetic approach to motion planning of redundant mobile manipulator systems considering safety and configuration

This article presents a genetic algorithm approach to multi-criteria motion planning of mobile manipulator systems. For mobile robot path planning, traveling distance and path safety are considered. The workspace of a mobile robot is represented as a grid by cell decomposition, and a wave front expansion algorithm is used to build the numerical potential fields for both the goal and the obstacles. For multi-criteria position and configuration optimization of a mobile manipulator, least torque norm, manipulability, torque distribution and obstacle avoidance are considered. The emphasis of the study is placed on using genetic algorithms to search for global optimal solutions and solve the minimax problem for manipulator torque distribution. Various simulation results from two examples show that the proposed genetic algorithm approach performs better than the conventional methods.     

Evolutionary trajectory planning for an industrial robot

This paper presents a novel general method for computing optimal motions of an industrial robot manipulator (AdeptOne XL robot) in the presence of fixed and oscillating obstacles. The optimization model considers the nonlinear manipulator dynamics, actuator constraints, joint limits, and obstacle avoidance. The problem has 6 objective functions, 88 variables, and 21 constraints. Two evolutionary algorithms, namely, elitist non-dominated sorting genetic algorithm (NSGA-II) and multi-objective differential evolution (MODE), have been used for the optimization. Two methods (normalized weighting objective functions and average fitness factor) are used to select the best solution tradeoffs. Two multi-objective performance measures, namely solution spread measure and ratio of non-dominated individuals, are used to evaluate the Pareto optimal fronts. Two multi-objective performance measures, namely, optimizer overhead and algorithm effort, are used to find the computational effort of the optimization algorithm. The trajectories are defined by B-spline functions. The results obtained from NSGA-II and MODE are compared and analyzed.     

Robust underwater obstacle detection and collision avoidance

A robust obstacle detection and avoidance system is essential for long term autonomy of autonomous underwater vehicles (AUVs). Forward looking sonars are usually used to detect and localize obstacles. However, high amounts of background noise and clutter present in underwater environments makes it difficult to detect obstacles reliably. Moreover, lack of GPS signals in underwater environments leads to poor localization of the AUV. This translates to uncertainty in the position of the obstacle relative to a global frame of reference. We propose an obstacle detection and avoidance algorithm for AUVs which differs from existing techniques in two aspects. First, we use a local occupancy grid that is attached to the body frame of the AUV, and not to the global frame in order to localize the obstacle accurately with respect to the AUV alone. Second, our technique adopts a probabilistic framework which makes use of probabilities of detection and false alarm to deal with the high amounts of noise and clutter present in the sonar data. This local probabilistic occupancy grid is used to extract potential obstacles which are then sent to the command and control (C2) system of the AUV. The C2 system checks for possible collision and carries out an evasive maneuver accordingly. Experiments are carried out to show the viability of the proposed algorithm.     

碰撞锥检测改进的多智能体避障算法

避障是多智能体能够适应复杂环境并顺利完成任务的必要条件之一。为使多智能体更快通过障碍物并达到一致,提出了一种多智能体避障控制算法。算法引入了避障系数,该系数由基于角度比较的碰撞锥检测方法来确定,并通过牵制控制输入完成多智能体的避障。证明了在该算法作用下所有智能体最终会避开障碍,避免碰撞并最终达到一致。通过实验仿真分析和对比,该方法能够使得多智能体更快避开障碍物。