A real-time path planning algorithm for cable-driven parallel robots in dynamic environment based on artificial potential guided RRT

Microsystem Technologies - This paper deals with the collision-free path planning of cable-driven parallel robots (CDPRs) in a dynamic three-dimensional environment. The proposed algorithm is based...     

Collision-free motion planning of dual-arm reconfigurable robots

Dual-arm reconfigurable robot is a new type of robot. It can adapt to different tasks by changing its different end-effector modules which have standard connectors. Especially, in fast and flexible assembly, it is very important to research the collision-free planning of dual-arm reconfigurable robots. It is to find a continuous, collision-free path in an environment containing obstacles. A new approach to the real-time collision-free motion planning of dual-arm reconfigurable robots is used in the paper. This method is based on configuration space (C-Space). The method of configuration space and the concepts reachable manifold and contact manifold are successfully applied to the collision-free motion planning of dual-arm robot. The complexity of dual-arm robots’ collision-free planning will reduce to a search in a dispersed C-Space. With this algorithm, a real-time optimum path is found. And when the start point and the end point of the dual-arm robot are specified, the algorithm will successfully get the collision-free path real time. A verification of this algorithm is made in the dual-arm horizontal articulated robot SCARATES, and the simulation and experiment ascertain that the algorithm is feasible and effective.     

Collision free path-planning for cable-driven parallel robots

In this study, a path-planning method that has been developed for serial manipulators is adapted to cable-driven robots. The proposed method has two modes. The first one is active when the robot is far from an obstacle. In this mode, the robot moves toward the goal on a straight line. The second mode is active when the robot is near an obstacle. During this mode, the robot finds the best way to avoid the obstacle. Moreover, an algorithm is presented to detect the collision between the robot and the obstacle. A similar algorithm is also presented to avoid the collision of the cables with an obstacle. Some simulation results are shown, which are then validated experimentally using a built 4-cable-driven parallel manipulator. Although the path obtained between the initial and final poses may not be the shortest possible one, it guarantees finding a path, when it exists, no matter how cluttered the environment is.     

A robust online path planning approach in cluttered environments for micro rotorcraft drones

We present in this paper a robust online path planning method, which allows a micro rotorcraft drone to fly safely in GPS-denied and obstacle-strewn environments with limited onboard computational power. The approach is based on an efficiently managed grid map and a closed-form solution to the two point boundary value problem (TPBVP). The grid map assists trajectory evaluation whereas the solution to the TPBVP generates smooth trajectories. Finally, a top-level trajectory switching algorithm is utilized to minimize the computational cost. Advantages of the proposed approach include its conservation of computational resource, robustness of trajectory generation and agility of reaction to unknown environment. The result has been realized on actual drones platforms and successfully demonstrated in real flight tests. The video of flight tests can be found at: http://uav.ece.nus.edu.sg/robust-online-path-planning-Lai2015.html.     

未知环境下移动机器人遍历路径规划

为提高未知环境下移动机器人遍历路径规划的效率,提出了一种可动态调节启发式规则的滚动路径规划算法.该算法以生物激励神经网络为环境模型,通过在线识别环境信息特征,动态调用静态搜索算法和环绕障碍搜索算法,有效减少了路径的转弯次数.引入虚拟障碍和直接填充算法,解决了u型障碍区域的连续遍历问题.最后通过仿真实验表明了该方法在未知复杂环境下的有效性.     

Avoiding obstacles - multisensor navigation for nonholonomic robots in cluttered environments

This article introduces a navigation method for nonholonomic (differential drive) vehicles, based on odometry, regularly reset by a vision-based self-localization algorithm, and endowed with a sonar-based obstacle avoidance and guidance control algorithm that does not rely on path planning. The guidance controller is used in the soccer robots of the RoboCup middle-size league (MSL) ISocRob team, fully integrated in the state machine that coordinates task execution. The algorithm can be generally applied to structured indoor environments, provided that visual features can be observed by the self-localization method and that the visual information is not ambiguous.     

Moving obstacle avoidance for cable-driven parallel robots using improved RRT

Microsystem Technologies - Moving obstacle avoidance is one of the most challenging problems for cable-driven parallel robots (CDPRs) due to various constraints. In this work, the improved rapidly...     

Collision free cooperative navigation of multiple wheeled robots in unknown cluttered environments

When employing autonomous wheeled robots, it is desirable to use navigation approaches that always prevent collisions. In this paper, we consider the problem of navigating multiple vehicles through an unknown static environment with limited sensing and communication capabilities available. We propose a decentralized, cooperative, reactive, model predictive control based collision avoidance scheme that plans short range paths in the currently sensed part of the environment, and show that it is able to prevent collisions from occurring. An auxiliary controller is employed to follow previously planned paths whenever the main path planning system fails to update the path. Simulations and real-world testing in various scenarios confirm the methods validity.     

Design and evaluation of disturbance observer algorithm for cable-driven parallel robots

Recently, a cable-driven parallel robots (CDPRs) has been applied to radio telescope as the accurate actuator, that is the five-hundred-meter aperture spherical telescope. It can be affected by the disturbances such as wind, earthquake and so on. Therefore, this paper developed a disturbance observer (DOB)-based control suitable for CDPR. The propose of the control is to reduce the effects of disturbance while the end-effectors maintains the same position even though the disturbance affects it. The key component of the DOB controller is a disturbance observer, which includes inverse nominal plant for each cable. So, a system identification test was carried out firstly. Then, the simulation and experiment were also carried out to evaluate the performance of the algorithm in CDPR. The results showed that the designed DOB algorithm could effectively reduce disturbances.

     

Dyna-Q-based vector direction for path planning problem of autonomous mobile robots in unknown environments

Reinforcement learning (RL) is a popular method for solving the path planning problem of autonomous mobile robots in unknown environments. However, the primary difficulty faced by learning robots using the RL method is that they learn too slowly in obstacle-dense environments. To more efficiently solve the path planning problem of autonomous mobile robots in such environments, this paper presents a novel approach in which the robot’s learning process is divided into two phases. The first one is to accelerate the learning process for obtaining an optimal policy by developing the well-known Dyna-Q algorithm that trains the robot in learning actions for avoiding obstacles when following the vector direction. In this phase, the robot’s position is represented as a uniform grid. At each time step, the robot performs an action to move to one of its eight adjacent cells, so the path obtained from the optimal policy may be longer than the true shortest path. The second one is to train the robot in learning a collision-free smooth path for decreasing the number of the heading changes of the robot. The simulation results show that the proposed approach is efficient for the path planning problem of autonomous mobile robots in unknown environments with dense obstacles.     

Optimal path planning in cluttered environment using RRT*-AB

Rapidly exploring Random Tree Star (RRT*) has gained popularity due to its support for complex and high-dimensional problems. Its numerous applications in path planning have made it an active area of research. Although it ensures probabilistic completeness and asymptotic optimality, its slow convergence rate and large dense sampling space are proven problems. In this paper, an off-line planning algorithm based on RRT* named RRT*-adjustable bounds (RRT*-AB) is proposed to resolve these issues. The proposed approach rapidly targets the goal region with improved computational efficiency. Desired objectives are achieved through three novel strategies, i.e., connectivity region, goal-biased bounded sampling, and path optimization. Goal-biased bounded sampling is performed within boundary of connectivity region to find the initial path. Connectivity region is flexible enough to grow for complex environment. Once path is found, it is optimized gradually using node rejection and concentrated bounded sampling. Final path is further improved using global pruning to erode extra nodes. Robustness and efficiency of proposed algorithm is tested through experiments in different structured and unstructured environments cluttered with obstacles including narrow and complex maze cases. The proposed approach converges to shorter path with reduced time and memory requirements than conventional RRT* methods.     

Collision-free and smooth joint motion planning for six-axis industrial robots by redundancy optimization

Planning collision-free and smooth joint motion is crucial in robotic applications, such as welding, milling, and laser cutting. Kinematic redundancy exists when a six-axis industrial robot performs five-dimensional tasks, and there are infinite joint configurations for a six-axis industrial robot to realize a cutter location data of the tool path. The robot joint motion can be optimized by taking advantage of the kinematic redundancy, and the collision-free joint motion with minimum joint movement is determined as the optimal. However, most existing redundancy optimization methods do not fully exploit the redundancy of the six-axis industrial robots when they conduct five-dimensional tasks. In this paper, we present an optimization method to solve the problem of inverse kinematics for a six-axis industrial robot to synthesize the joint motion that follows a given tool path, while achieving smoothness and collision-free manipulation. B-spline is applied for the joint configuration interpolation, and the sum of the squares of the first, second, and third derivatives of the B-spline curves are adopted as the smoothness indicators. Besides, the oriented bounding boxes are adopted to simplify the shape of the robot joints, robot links, spindle unit, and fixtures to facilitate collision detections. Dijkstra's shortest path technique and Differential Evolution algorithm are combined to find the optimal joint motion efficiently and avoid getting into a local optimal solution. The proposed algorithm is validated by simulations on two six-axis industrial robots conducting five-axis flank milling tasks respectively.     

A path planning algorithm for industrial robots

Instead of using the tedious process of robot teaching, an off-line path planning algorithm has been developed for industrial robots to improve their accuracy and efficiency. Collision avoidance is the primary concept to achieve such goal. By use of the distance maps, the inspection of obstacle collision is completed and transformed to the configuration space in terms of the robot joint angles. On this configuration map, the relation between the obstacles and the robot arms is obvious. By checking the interference conditions, the collision points are indicated with marks and collected into the database. The path planning is obtained based on the assigned marked number of the passable region via wave expansion method. Depth-first search method is another approach to obtain minimum sequences to pass through. The proposed algorithm is experimented on a 6-DOF industrial robot. From the simulation results, not only the algorithm can achieve the goal of collision avoidance, but also save the manipulation steps.     

Motion planning of walking robots in environments with uncertainty

We present a general approach for coordinating the legs of a multi-legged statically stable walking machine on an uneven terrain. We are interested in an optimized motion plan for several “body-lengths” that specifies the footholds and the gait of the walking machine. We assume that a terrain map is available but that this map may be characterized by uncertainty. We also assume that the optimality of the motion plan can be measured by a suitable metric. The method of ordinal optimization is used to find a motion plan that is guaranteed to be in a desired percentile with a given confidence level. Depending on the available computational resources we can improve our confidence level and/or get closer to the optimal plan. Finally, our approach allows us to trade off speed with safety and speed with optimality. ©1999 John Wiley & Sons, Inc.     

一种未知环境下的快速路径规划方法*

为提高机器人在未知环境中的快速路径规划能力,引入自由路径表征可以通过的自由空间,引入风险函数评价机器人切入自由路径过程中发生碰撞的风险。通过搜索最优自由路径、评价碰撞风险压缩表示环境信息,使得未知环境中利用模糊控制器进行局部路径规划的实时性大为提高。与虚拟势场法等传统方法相比,其无局部最小,且极大缓解了狭窄环境中的振荡现象。实验及仿真均表明该方法实时性好、规划所得路径优于已有方法。     

Integrated and nonlinear dynamic model of a polymer cable for low-speed cable-driven parallel robots

Microsystem Technologies - Cable-driven parallel robots (CDPRs) have been widely used in various industrial applications requiring high sensitivity. These CDPRs mainly use high strength polymer...     

移动机器人路径规划强化学习的初始化

针对现有机器人路径规划强化学习算法收敛速度慢的问题,提出了一种基于人工势能场的移动机器人强化学习初始化方法.将机器人工作环境虚拟化为一个人工势能场,利用先验知识确定场中每点的势能值,它代表最优策略可获得的最大累积回报.例如障碍物区域势能值为零,目标点的势能值为全局最大.然后定义Q初始值为当前点的立即回报加上后继点的最大折算累积回报.改进算法通过Q值初始化,使得学习过程收敛速度更快,收敛过程更稳定.最后利用机器人在栅格地图中的路径对所提出的改进算法进行验证,结果表明该方法提高了初始阶段的学习效率,改善了算法性能.     

Complete coverage path planning for wheeled agricultural robots

In the agricultural industry, an evolutionary effort has been made over the last two decades to achieve precise autonomous systems to perform typical in-field tasks, including harvesting, mowing, and spraying. One of the main objectives of an autonomous system in agriculture is to improve the efficiency while reducing the environmental impact and cost. Due to the nature of these operations, complete coverage path planning (CCPP) approaches play an essential role to find an optimal path which covers the entire field while taking into account land topography, operation requirements, and robot characteristics. The aim of this paper is to propose a CCPP approach defining the optimal movements of mobile robots over an agricultural field. First, a method based on tree exploration is proposed to find all potential solutions satisfying some predefined constraints. Second, a similarity check and selection of optimal solutions method is proposed to eliminate similar solutions and find the best solutions. The optimization goals are to maximize the coverage area and to minimize overlaps, nonworking path length, and overall travel time. To explore a wide range of possible solutions, our approach is able to consider multiple entrances for the robot. For fields with a complex shape, different dividing lines to split them into simple polygons are also considered. Our approach also computes the headland zones and covers them automatically which leads to a high coverage rate of the field.     

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.     

基于RGB-D信息的移动机器人SLAM和路径规划方法研究与实现

移动机器人在各种辅助任务中需具备自主定位、建图、路径规划与运动控制的能力。本文利用RGB-D信息和ORB-SLAM算法进行自主定位,结合点云数据和GMapping算法建立环境栅格地图,基于二次规划方法进行平滑可解析的路径规划,并设计非线性控制器,实现了由一个运动底盘、一个RGB-D传感器和一个运算平台组成的自主移动机器人系统。经实验验证,这一系统实现了复杂室内环境下的实时定位与建图、自主移动和障碍物规避。由此,为移动机器人的推广应用提供了一个硬件结构简单、性能良好、易扩展、经济性好、开发维护方便的解决方案。