Real-time Velocity Alteration Strategy for Collision-free Trajectory Planning of Two Articulated Robot Manipulators

Two articulated robots working in a shared workspace can be programmed by planning the tip trajectory of each robot independently. To account for collision avoidance between links, a real-time velocity alteration strategy based on fast and accurate collision detection is proposed in this paper to determine the step of next motion of slave (low priority) robot for collision-free trajectory planning of two robots with priorities. The effectiveness of the method depends largely on a newly developed method of accurate estimate of distance between links. By using the enclosing and enclosed ellipsoids representations of polyhedral models of links of robots, the minimum distance estimate and collision detection between the links can be performed more efficiently and accurately. The proposed strategy is implemented in an environment where the geometric paths of robots are pre-planned and the preprogrammed velocities are piecewise constant but adjustable. Under the control of the proposed strategy, the master robot always moves at a constant speed. The slave robot moves at the selected velocity, selected by a tradeoff between collision trend index and velocity reduction in one collision checking time, to keep moving as far as possible and as fast as possible while avoid possible collisions along the path. The collision trend index is a fusion of distance and relative velocity between links of two robots to reflect the possibility of collision at present and in the future. Graphic simulations of two PUMA560 robot arms working in common workspace but with independent goals are conducted. Simulations demonstrate the collision avoidance capability of the proposed approach as compared to the approach based on bounding volumes. It shows that advantage of our approach is less number of speed alterations required to react to potential collisions.     

Collision detection between the wrists of two robot arms in a common workspace

Detecting collisions for planning collision-free motion of the wrists of two robot arms in a common workspace is discussed in this paper. A collision-free motion can be obtained by detecting collisions along the preplanned trajectories using a sphere model for the wrist of each robot and then modifying the paths and/or trajectories of one or both robots to avoid the collision. In this paper, a collision detection algorithm is described and its role in collision avoidance is discussed. Collision detection is based on the premise that (1) the wrists of robots move monotonically on their preplanned straight line trajectories and (2) collisions never occur between the two wrists at the beginning points or end points.Research supported by the NASA-Langley Research Center under Grants #NAG-1-632 and #NAG-1-772 and the AT&T Foundation.     

Real-Time Collision-Free Path Planning for Robots in Configuration Space

Collision-free path planning for an industrial robot in configuration space requires mapping obstacles from robot‘s workspace into its configuration space.In this paper,an approach to real-time collision-free path planning for robots in configuration space is presented.Obstacle mapping is carried out by fundamental obstacles defined in the workspace and their images in the configuration space.In order to avoid dealing with unimportant parts of the configuration space that do not affect searching a collision-free path between starting and goal configurations,we construct a free subspace by slice configuration obstacles.In this free subspace,the collision-free path is determined by the A^* algorithm.Finally,graphical simulations show the effectiveness of the proposed approach.     

A Sequential Bi-criteria Search Algorithm for Robot Path Planning in the Box Pushing Problem

The collision-free trajectory planning method subject to control constraints for mobile manipulators is presented. The robot task is to move from the current configuration to a given final position in the workspace. The motions are planned in order to maximise an instantaneous manipulability measure to avoid manipulator singularities. Inequality constraints on state variables i.e. collision avoidance conditions and mechanical constraints are taken into consideration. The collision avoidance is accomplished by local perturbation of the mobile manipulator motion in the obstacles neighbourhood. The fulfilment of mechanical constraints is ensured by using a penalty function approach. The proposed method guarantees satisfying control limitations resulting from capabilities of robot actuators by applying the trajectory scaling approach. Nonholonomic constraints in a Pfaffian form are explicitly incorporated into the control algorithm. A computer example involving a mobile manipulator consisting of nonholonomic platform (2,0) class and 3DOF RPR type holonomic manipulator operating in a three-dimensional task space is also presented.     

Human-robot collaboration while sharing production activities in dynamic environment: SPADER system

Interactive robot doing collaborative work in hybrid work cell need adaptive trajectory planning strategy. Indeed, systems must be able to generate their own trajectories without colliding with dynamic obstacles like humans and assembly components moving inside the robot workspace. The aim of this paper is to improve collision-free motion planning in dynamic environment in order to insure human safety during collaborative tasks such as sharing production activities between human and robot. Our system proposes a trajectory generating method for an industrial manipulator in a shared workspace. A neural network using a supervised learning is applied to create the waypoints required for dynamic obstacles avoidance. These points are linked with a quintic polynomial function for smooth motion which is optimized using least-square to compute an optimal trajectory. Moreover, the evaluation of human motion forms has been taken into consideration in the proposed strategy. According to the results, the proposed approach is an effective solution for trajectories generation in a dynamic environment like a hybrid workspace.     

Obstacle Modelling Oriented to Safe Motion Planning and Control for Planar Rigid Robot Manipulators

Trajectory planning and tracking are crucial tasks in any application using robot manipulators. These tasks become particularly challenging when obstacles are present in the manipulator workspace. In this paper a n-joint planar robot manipulator is considered and it is assumed that obstacles located in its workspace can be approximated in a conservative way with circles. The goal is to represent the obstacles in the robot configuration space. The representation allows to obtain an efficient and accurate trajectory planning and tracking. A simple but effective path planning strategy is proposed in the paper. Since path planning depends on tracking accuracy, in this paper an adequate tracking accuracy is guaranteed by means of a suitably designed Second Order Sliding Mode Controller (SOSMC). The proposed approach guarantees a collision-free motion of the manipulator in its workspace in spite of the presence of obstacles, as confirmed by experimental results.     

A simple algorithm for intelligent manipulator collision-free motion

The collision-free planning of motion is a fundamental problem for artificial intelligence applications in robotics. The ability to compute a continuous safe path for a robot in a given environment will make possible the development of task-level robot planning systems so that the implementation details and the particular robot motion sequence will be ignored by the programmer.A new approach to planning collision-free motions for general real-life six degrees of freedom (d.o.f.) manipulators is presented. It is based on a simple object model previously developed. The complexity of the general collision detection problem is reduced, and realistic collision-free paths are efficiently found onCS planes. A heuristic evaluation function with a real physical sense is introduced, and computational cost is reduced to the strictly necessary by selecting the most adequate level of representation. A general algorithm is defined for 6 d.o.f. robots that yields good results for actual robot models with complex design structures with the aid of various heuristic techniques. The problem of adaptive motion is also considered.     

基于引力自适应步长RRT的双臂机器人协同路径规划

快速扩展随机树（RRT）方法的步长确定过分依赖于程序调试,而且固定的步长会导致碰撞检测失效问题．针对此问题,本文提出一种适用于双臂机器人协同路径规划的引力自适应步长RRT．首先,通过建立构型空间与工作空间的步长范数不等式,对双臂机器人在工作空间中所产生的步长进行约束,进而确保实现有效的碰撞检测;然后,提出随机树被动生长方法,在保证双臂机器人协同运动的基础上,降低规划空间的维度．最后,在随机树的节点处引入引力函数,加快算法的融合速度．仿真结果表明,引力自适应步长RRT方法可对工作空间中的步长进行有效约束,确保算法碰撞检测的有效性．在无碰撞的前提下,引力自适应步长RRT方法相比于其他算法减少了迭代次数,降低了运行时间并缩短了路径长度．将所提算法应用于双臂机器人的样机实验,结果表明双臂机器人可在保持位置协同的前提下,完成避障运动,验证了算法的可行性．     

Autonomous mobile robot dynamic motion planning using hybrid fuzzy potential field

A new fuzzy-based potential field method is presented in this paper for autonomous mobile robot motion planning with dynamic environments including static or moving target and obstacles. Two fuzzy Mamdani and TSK models have been used to develop the total attractive and repulsive forces acting on the mobile robot. The attractive and repulsive forces were estimated using four inputs representing the relative position and velocity between the target and the robot in the x and y directions, in one hand, and between the obstacle and the robot, on the other hand. The proposed fuzzy potential field motion planning was investigated based on several conducted MATLAB simulation scenarios for robot motion planning within realistic dynamic environments. As it was noticed from these simulations that the proposed approach was able to provide the robot with collision-free path to softly land on the moving target and solve the local minimum problem within any stationary or dynamic environment compared to other potential field-based approaches.     

基于速度变化空间的移动机器人动态避碰规划

研究了移动机器人对运动障碍物的动态避碰．针对以往速度障碍法在动态避碰应用中存在的问题,制 订了相应的改进方法．综合考虑障碍物速度的动态变化和碰撞时间、碰撞距离,在速度变化空间中,基于避碰行为 动力学原理,设计了新的优化评价函数,采用双障碍物检测窗口进行动态避碰规划．仿真实验表明,该方法有效地 克服了避碰规划的保守性,提高了机器人运动的安全性,并能实现对运动目标的及时追踪．     

The complexity of the free space for motion planning amidst fat obstacles

The complexity of motion planning algorithms highly depends on the complexity of the robot's free space, i.e., the set of all collision-free placements of the robot. Theoretically, the complexity of the free space can be very high, resulting in bad worst-case time bounds for motion planning algorithms. In practice, the complexity of the free space tends to be much smaller than the worst-case complexity. Motion planning algorithms with a running time that is determined by the complexity of the free space therefore become feasible in practical situations. We show that, under some realistic assumptions, the complexity of the free space of a robot with any fixed number of degrees of freedom moving around in ad-dimensional Euclidean workspace with fat obstacles is linear in the number of obstacles. The complexity results lead to highly efficient algorithms for motion planning amidst fat obstacles.Research is supported by the Dutch Organization for Scientific Research (NWO) and partially supported by the ESPRIT III BRA Project 6546 (PROMotion).     

在障碍时变工作空间中机器人无碰撞平滑路径规划的一种算法研究

本文应用在障碍时变工作空间中把固定障碍和时变障碍分解的思想.首先就固定障碍问题,为机器人规划一条无碰撞路径,然后通过规划机器人的速度来达到避开活动障碍的目的.本文接着提出在时间-路径空间中以忽略可动障碍时机器人的运动策略为基准策略,根据障碍约束和机器人速度或加速度约束,用有理二次函数来规划机器人避开可动障碍的运动策略.     

一种基于遗传算法的欠驱动机器人避障运动规划新方法*

对平面欠驱动机器人的避障运动规划问题进行了研究,提出了一种利用遗传算法解决此类系统避障问题的新方法。通过引入虚拟弹簧—阻尼系统,在障碍物存在的情况下对系统非完整约束方程的广义力进行修正,采用部分稳定规划器进行运动规划,建立了基于能量的适应度函数,并利用遗传算法对提出的适应度函数进行全局优化,得到了部分稳定规划器的最优切换顺序,进而实现了欠驱动机器人的无碰撞路径规划。最后以平面3R欠驱动机器人为例进行了仿真实验,验证了该方法的有效性。     

改进人工势场法的移动机器人避障轨迹研究

针对传统人工势场法在多障碍物复杂环境的全局路径规划中出现的目标不可达、易陷入陷阱区域以及局部极小点问题,提出一种简化障碍物预测碰撞人工势场法(simplified obstacles and predict collision of artificial potential field method,SOPC-APF)...     

Neural networks based reinforcement learning for mobile robots obstacle avoidance

This study proposes a new approach for solving the problem of autonomous movement of robots in environments that contain both static and dynamic obstacles. The purpose of this research is to provide mobile robots a collision-free trajectory within an uncertain workspace which contains both stationary and moving entities. The developed solution uses Q-learning and a neural network planner to solve path planning problems. The algorithm presented proves to be effective in navigation scenarios where global information is available. The speed of the robot can be set prior to the computation of the trajectory, which provides a great advantage in time-constrained applications. The solution is deployed in both Virtual Reality (VR) for easier visualization and safer testing activities, and on a real mobile robot for experimental validation. The algorithm is compared with Powerbot's ARNL proprietary navigation algorithm. Results show that the proposed solution has a good conversion rate computed at a satisfying speed.     

Plan-N-Scan: A Robotic System for Collision-Free Autonomous Exploration and Workspace Mapping

This paper presents a sensor-based robotic system, called Plan-N-Scan, for collision-free, autonomous exploration and workspace mapping, using a wrist-mounted laser range camera. This system involves gaze planning with collision-free sensor positioning in a static environment, resulting in a 3-D map suitable for real-time collision detection. This work was initially motivated by the great demand for autonomous exploration systems in the remediation of buried but leaking tanks containing hazardous nuclear waste. Plan-N-Scan uses two types of representations: a spherical model of the manipulator and a weighted voxel map of the workspace. In addition to providing efficient collision detection, the voxel map allows the incorporation of different types of spatial occupancy information. The mapping of unknown sections of the workspace is achieved by either target or volume scanning. Target scanning incorporates a powerful A*-based search, along with a viewing position selection strategy, to incrementally acquire scans of the scene and use them to capture targets, even if they are not immediately viewable by the range camera. Volume scanning is implemented as an iterative process which automatically selects scan targets, then employs the target scanning process to scan these targets and explore the selected workspace volume. The performance and reliability of the system was demonstrated through simulation and a number of experiments involving a real robot system. The ability of the Plan-N-Scan system to incrementally acquire range information and successfully scan both targets and workspace volumes was demonstrated.     

基于有序二叉决策图的路径规划可行性研究

对全局环境未知且存在障碍物情况下的移动机器人路径规划问题进行了研究.借助有序二又决策图的原理,首次采用有序二叉决策图数据结构来表示机器人工作空间中的信息环境模型,并对它们进行了二进制编码,建立一个有效紧凑的OBDD环境模型.利用该OBDD模型能自动规划了免碰撞路径,获取一条从起始状态(包括位置及姿态)到达目标状态的安全、高效的无碰路径.实验仿真结果表明,所提出的方法是正确和有效的.     

Acquisition of obstacle motion patterns to improve mobile robot motion planning

Mobile robots for advanced applications have to act in environments which contain moving obstacles (humans). Even though the motions of such obstacles are not precisely predictable, usually they are not completely random; long-term observation of obstacle behavior may thus yield valuable knowledge about prevailing motion patterns. By incorporating such knowledge as statistical data, a new approach called statistical motion planning yields robot motions which are better adapted to the dynamic environment. To put these ideas into practice, an experimental system has been developed. Cameras observe the workspace in order to detect obstacle motion. Statistical data is derived and represented as a set of stochastic trajectories. This data can be directly employed in order to calculate collision probability, i.e. the probability of encountering an obstacle during the robot's motion. Further aspects of motion planning are addressed: path planning which minimizes collision probability, estimation of expected time to reach the goal and reactive planning.     

基于路标点追踪的差速驱动机器人运动规划

首先,针对三角网格路径规划方法采用D-P算法提取路标点时,其最大阈值不易确定等问题,提出基于碰撞检测的抽取路标点方法.同时采用Pure Pursuit算法跟踪路标点,对差速驱动机器人进行运动规划;然后,通过实验对比分析表明,在提取路标点时,与D-P算法相比,碰撞检测方法更加优越;最后,通过差速驱动机器人的运动规划实验表明:Pure Pursuit算法追踪路标点方法规划的运动轨迹为一条光滑曲线,能够有效地避开地图上的障碍物;机器人角速度和线速度均为光滑函数,变化平缓,在路标点附近出现较小波动,波动范围均在允许值内;运动规划时间为0.049s,完全能够满足实际需求.研究结果表明,基于路标点追踪的移动机器人运动规划是一种简单有效的运动规划方法.     

On-line Planning for Collision Avoidance on the Nominal Path

In this paper a solution to the obstacle avoidance problem for a mobile robot moving in the two-dimensional Cartesian plane is presented. The robot is modelled as a linear time-invariant dynamic system of finite size enclosed by a circle and the obstacles are modelled as circles travelling along rectilinear trajectories. This work deals with the avoidance problem when the obstacles move in known trajectories. The robot starts its journey on a nominal straight line path with a nominal velocity. When an obstacle is detected to be on a collision course with the robot, the robot must devise a plan to avoid the obstacle whilst minimising a cost index defined as the total sum squared of the magnitudes of the deviations of its velocity from the nominal velocity. The planning strategy adopted here is adjustment of the robot's velocity on the nominal path based on the time of collision between the robot and a moving obstacle, and determination of a desired final state such that its Euclidean distance from the nominal final state is minimal. Obstacle avoidance by deviation from the nominal path in deterministic and random environments is based on the work presented here and is investigated in another paper.