A novel path planning and control framework for passive resistance therapy with a robot manipulator

In this article, we present a paradigm for safe path generation and control for a robotic manipulator such that it provides programmable passive resistance therapy to patients with deficits in the upper extremities. When the patient applies an interaction force at the robot's end-effector, a dynamic path generator time parameterises any therapist-specified contour in the robot's workspace–thus, the robot mimics the dynamics of a passive impedance whose anisotropy vector can be continuously reconfigured. The proposed algorithm is easily implementable because it is robust to uncertainty in the robot dynamics. Moreover, the proposed strategy also guarantees user safety by maintaining the net flow of energy during the human robot interaction from the user towards the manipulator.     

Path planning of a mobile robot by optimization and reinforcement learning

At AROB5, we proposed a solution to the path planning of a mobile robot. In our approach, we formulated the problem as a discrete optimization problem at each time step. To solve the optimization problem, we used an objective function consisting of a goal term, a smoothness term, and a collision term. While the results of our simulation showed the effectiveness of our approach, the values of the weights in the objective function were not given by any theoretical method. This article presents a theoretical method using reinforcement learning for adjusting the weight parameters. We applied Williams' learning algorithm, episodic REINFORCE, to derive a learning rule for the weight parameters. We verified the learning rule by some experiments. This work was presented, in part, at the Sixth International Symposium on Artificial Life and Robotics, Tokyo, Japan, January 15–17, 2001     

On generating the motion of industrial robot manipulators

In this study, an intelligent search algorithm is proposed to define the path that leads to the desired position and orientation of an industrial robot׳s manipulator end effector. The search algorithm gradually approaches the desired configuration by selecting and evaluating a number of alternative robot׳s configurations. A grid of the robot׳s alternative configurations is constructed using a set of parameters which are reducing the search space to minimize the computational time. In the evaluation of the alternatives, multiple criteria are used in order for the different requirements to be fulfilled. The alternative configurations are generated with emphasis being given to the robot׳s joints that mainly affect the position of the end effector. Grid resolution and size parameters are set on the basis of the desired output. High resolution is used for a smooth path and lower for a rough estimation, by providing only a number of the intermediate points to the goal position. The path derived is a series of robot configurations. This method provides an inexperienced robot programmer with flexibility to generate automatically a robotic path that would fulfill the desired criteria without having to record intermediate points to the goal position.     

Pose estimation-based path planning for a tracked mobile robot traversing uneven terrains

A novel path-planning algorithm is proposed for a tracked mobile robot to traverse uneven terrains, which can efficiently search for stability sub-optimal paths. This algorithm consists of combining two RRT-like algorithms (the Transition-based RRT (T-RRT) and the Dynamic-Domain RRT (DD-RRT) algorithms) bidirectionally and of representing the robot–terrain interaction with the robot’s quasi-static tip-over stability measure (assuming that the robot traverses uneven terrains at low speed for safety). The robot’s stability is computed by first estimating the robot’s pose, which in turn is interpreted as a contact problem, formulated as a linear complementarity problem (LCP), and solved using the Lemke’s method (which guarantees a fast convergence). The present work compares the performance of the proposed algorithm to other RRT-like algorithms (in terms of planning time, rate of success in finding solutions and the associated cost values) over various uneven terrains and shows that the proposed algorithm can be advantageous over its counterparts in various aspects of the planning performance.     

Path-planning and navigation of a mobile robot as discrete optimization problems

There is huge diversity among navigation and path-planning problems in the real world because of the enormous number and great variety of assumptions about the environments, constraints, and tasks imposed on a robot. To deal with this diversity, we propose a new solution to the path-planning and navigation of a mobile robot. In our approach, we formulated the following two problems at each time-step as discrete optimization problems: (1) estimation of a robot's location, and (2) action decision. For the first problem, we minimize an objective function that includes a data term, a constraint term, and a prediction term. This approach is an approximation of Markov localization. For the second problem, we define and minimize another objective function that includes a goal term, a smoothness term, and a collision term. Simulation results show the effectiveness of our approach. This work was presented in part at the Fifth International Symposium on Artificial Life and Robotics, Oita, Japan, January 26–28, 2000     

基于改进A*算法的翼伞航迹规划研究

在空投翼伞着陆航迹优化的研究中,翼伞航迹规划是完成精确空投任务的关键环节.由于现有翼伞航迹设计方法没有考虑环境的不利影响和在线航迹规划需求,提出建立翼伞着陆过程威胁数学模型,提出了利用改进A *算法的翼伞航迹规划方法和虚拟威胁法,使翼伞满足逆风着陆条件,设计了盘旋规划,保证了精确着陆.最后仿真验证了上述方法能够满足复杂战场环境条件下翼伞航迹规划要求,并且具有较好的威胁回避能力和快速规划能力,具有很大的实际应用价值.     

基于侦察蚁和觅食蚁协作的机器人路径规划算法

根据对真实蚂蚁的最新研究成果,提出了一种全新的机器入路径规划蚂蚁算法.该算法由两组侦察蚁采用最近邻侦察搜索策略相向搜索出一条较优路径,再由一组觅食蚁通过在该路径附近觅食实现对该路径的优化,从而得到一条优化的路径.计算机仿真实验结果表明,算法收敛速度提高显著,且在障碍物非常复杂的地理环境,也能迅速规划出最优或基本最优的路径,效果令人满意.     

Cellular ants: A method to create collision free trajectories for a cooperative robot team

Creating collision-free trajectories for mobile robots, known as the path planning problem, is considered to be one of the basic problems in robotics. In case of multiple robotic systems, the complexity of such systems increases proportionally with the number of robots, due to the fact that all robots must act as one unit to complete one composite task, such as retaining a specific formation. The proposed path planner employs a combination of Cellular Automata (CA) and Ant Colony Optimization (ACO) techniques in order to create collision-free trajectories for every robot of a team while their formation is kept immutable. The method reacts with obstacle distribution changes and therefore can be used in dynamical or unknown environments, without the need of a priori knowledge of the space. The team is divided into subgroups and all the desired pathways are created with the combined use of a CA path planner and an ACO algorithm. In case of lack of pheromones, paths are created using the CA path planner. Compared to other methods, the proposed method can create accurate collision-free paths in real time with low complexity while the implemented system is completely autonomous. A simulation environment was created to test the effectiveness of the applied CA rules and ACO principles. Moreover, the proposed method was implemented in a system using a real world simulation environment, called Webots. The CA and ACO combined algorithm was applied to a team of multiple simulated robots without the interference of a central control. Simulation and experimental results indicate that accurate collision free paths could be created with low complexity, confirming the robustness of the method.     

基于移动机器人路径规划的鼠群算法

研究静态环境下机器人路径规划问题,并根据老鼠觅食行为提出一种鼠群算法.该算法引入环境因子和经验因子,每次搜索后对路径进行经验因子更新,通过迭代的方式寻找静态环境下机器人最佳路径.同时提出一种禁忌策略,有效地避免了路径死锁问题.理论分析和实验结果表明,该算法能使机器人在有较多障碍的环境下迅速找到一条优化路径,而且安全避碰,与同类算法相比具有一定的优越性.     

Mobile robot path planning with surrounding point set and path improvement

The objective of the path planning problem for a mobile robot is to generate a collision-free path from a starting position to a target position with respect to a certain fitness function, such as distance. Although, over the last few decades, path planning has been studied using a number of methodologies, the complicated and dynamic environment increases the complexity of the problem and makes it difficult to find an optimal path in reasonable time. Another issue is the existence of uncertainty in previous approaches. In this paper, we propose a new methodology to solve the path planning problem in two steps. First, the surrounding point set (SPS) is determined where the obstacles are circumscribed by these points. After the initial feasible path is generated based on the SPS, we apply a path improvement algorithm depending upon the former and latter points (PI_FLP), in which each point in the path is repositioned according to two points on either side. Through the SPS, we are able to identify the necessary points for solving path planning problems. PI_FLP can reduce the overall distance of the path, as well as achieve path smoothness. The SPS and PI_FLP algorithms were tested on several maps with obstacles and then compared with other path planning methods As a result, collision-free paths were efficiently and consistently generated, even for maps with narrow geometry and high complexity.     

Applying a path planner based on RRT to cooperative multirobot box-pushing

Considering robot systems in the real world, a multirobot system where multiple robots work simultaneously without colliding with each other is more practical than a single-robot system where only one robot works. Therefore, solving the path-planning problem in a multirobot system is very important. In this study, we developed a path-planner based on the rapidly exploring random tree (RRT), which is a data structure and algorithm designed for efficiently searching for multirobot box-pushing, and made experiments in real environments. A path planner must construct a plan which avoids the robot colliding with obstacles or with other robots. Moreover, in some cases, a robot must collaborate with other robots to transport the box without colliding with any obstacles. Our proposed path planner constructs a box-transportation plan and the path plans of each robot bearing the above considerations in mind. Experimental results showed that our proposed planner can construct a multirobot box-pushing plan without colliding with obstacles, and that the robots can execute tasks according to the plan in real environments. We also checked that multiple robots can perform problem tasks when only one robot could not transport the box to the goal. This work was presented in part at the 13th International Symposium on Articifial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

一种移动机器人路径规划新算法

快速搜索随机树（Rapidly-exploring random Tree Star,RRT*）算法在移动机器人实际应用中规划路径在转向部分存在较多的冗余转折点,导致移动机器人在移动转向过程中出现多次停顿与转向,为剔除规划路径中的冗余路径点,提高机器人移动流畅性,提出一种改进的 RRT*算法。算法将局部逆序试连法引入移动机器人路径规划,在确保RRT*算法概率完备性和渐进最优性的前提下,剔除规划路径中的冗余路径节点,使最终路径更加接近最短路径。通过MATLAB仿真实验证明,规划路径平均长度缩短4%,算法耗时缩短35%,改进后的RRT*算法能缩短规划路径且转向部分路径更加平滑。最后,使用改进后的RRT*算法在室内环境下进行移动机器人路径规划实验。实验结果表明：规划路径上无冗余路径点,且移动机器人沿路径移动流畅。     

一类新的PPA型三连杆欠驱动机器人的控制策略

PPA型机器人是一类新型的三连杆欠驱动机械系统,具有3个自由度,但只有1个驱动装置.针对这类欠驱动机器人的运动控制,提出一种分区的控制策略,使之从垂直向下的位置摇起到垂直向上的位置,并实现稳定控制.首先,将系统的运动空间划分为摇起区和吸引区;其次,应用一种基于Lyapunov函数的控制方法来增加系统能量和控制驱动杆姿态,实现摇起操作;再次,采用最优控制方法设计平衡控制,实现稳定的平衡控制;最后,通过仿真实验验证了所提出控制方法的有效性.     

Application of probability to enhance the performance of fuzzy based mobile robot navigation

This paper presents a new path planning algorithm based on Probability and Fuzzy Logic (PFL) as a duality technique to enhance the performance of Fuzzy Logic alone. Fuzzy Logic interacts with the grading of obstacles existed in the path and probability lies over the decision to move the mobile robot. The fuzzy grading correspondence with the probabilistic decision is the primary function of moving the mobile robot towards the goal and the secondary is path planning which lies over the probability distribution function. The distance–speed combination rule is developed for effective navigation. The single and multiple mobile robot systems have been tested successfully in a dense environment in presence of obstacles (static and dynamic) and moving goal. The obtained results are optimal when compared to other navigational approaches in sense of navigational path length and time in the static and dynamic environment.     

一种移动机器人的路径规划算法

本文提出一种移动机器人路径规划最短切线路径算法。依据此算法，机器人能顺利地避开障碍物到达目标位置，其原理简单，计算快捷，容易实现。仿真结果验证了它的有效性和实用性。     

Grid modeling of robot cells: A memory-efficient approach

This article introduces a new approach to modeling robot workspace in two dimensions (and its extension to 2^1/2 dimensions and to mobile objects). The scheme proves to be attractive because it requires less memory space and is execution-efficient. It is very useful for real-time applications.The grid modeling consists in dividing the space into horizontal and vertical strips whose intersection will form elementary cells. A function giving the access conditions (either a binary one allowing access or nonallowed access, or a more elaborate one including other elements (task, geometries)) is associated to each cell. The size of the adaptive grid will depend on the criterion of the obstacle minimal approach by the robot.Then a method for path planning is developed. The Lee and A^* algorithms being the starting point, the method has been conceived by executing mask operations. So the method requires less memory space and allows an admissible trajectory to be quickly found.     

A heuristic approach to robot path planning based on task requirements using a genetic algorithm

In order to enhance integration between CAD and robots, wer propose a scheme to plan kinematically feasible paths in the presence of obstacles based on task requirements. Thus, the feasibility of a planned path from a CAD system is assured before the path is sent for execution. The proposed scheme uses a heuristic approach to deal with a rather complex search space, involving high-dimensional C-space obstacles and task requirements specified in Cartesian space. When the robot is trapped by the local minimum in the potential field related to the heuristic, a genetic algorithm is then used to find a proper intermediate location that will guide it to escape out of the local minimum. For demonstration, simulations based on using a PUMA-typed robot manipulator to perform different tasks in the presence of obstacles were conducted. The proposed scheme can also be used for mobile robot planning. The paper falls into Category (5). Please address correspondence to the second author. This work was supported in part by the National Science Council, Taiwan, R.O.C., under grant NSC 82-0422-E-009-403.     

判断两个凸多面体是否相交的一个快速算法

在机器人路径规划中,碰撞检测算法占有十分重要的地位.在智能机器人仿真系统中,碰撞检测耗用的时间在整个路径规划过程所用时间中占有相当大的比例.于是,如何进一步提高碰撞检测的速度在智能机器人路径规划系统中就起到了非常关键的作用.而碰撞检测问题最终转化为判断三维空间中两个凸多面体是否相交的问题.就这一问题,给出了一种新的算法,其思想是取一个从一个凸多面体指向另一个多面体的向量,根据两个多面体中的面与这一向量的相对位置关系来寻找相交的平面.即有两个多面体的交点位于这一平面,若能找到一个相交平面则可以断定两个多面体     

基于遗传算法的变电站巡检机器人任务路径规划方法研究

近年来,随着变电站巡检机器人在变电站中的广泛使用,巡检机器人路径规划问题越来越成为亟待解决的问题。巡检机器人在已知的拓扑地图中标记了待执行巡检任务的停靠点,不同任务需要从初始点出发经过不同的一系列停靠点再返回初始点,如何规划路径是机器人面临的问题。首先分析了路径规划面临的问题,然后通过分析拓扑地图的特征,对地图进行等价简化,再对问题进行建模使用遗传算法求解巡检任务路径规划的近似最优解。通过仿真实验证明,提出的基于遗传算法的路径规划方法是可行有效的,为变电站巡检机器人任务路径规划提供了一种有效方法。     

关节型工业机器人轨迹规划研究综述

关节型工业机器人凭借其良好的灵活性和高效率的工作模式被广泛地应用于现代工业自动化生产之中,例如搬运、码垛、焊接、切割等。轨迹规划是工业机器人运动控制的基础研究领域,决定着其作业效率和运动性能。工业机器人的轨迹规划是指综合考虑作业需求和机器人性能,在笛卡尔空间或关节空间内得出指导机器人末端执行器运动的轨迹。阐述了工业机器人轨迹规划的概念及其分类,就各个领域的轨迹规划算法进行了全面综述,包括基本轨迹规划和最优轨迹规划,指出了各类轨迹规划算法中所存在的问题和未来的发展方向。