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.     

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.     

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.     

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.

     

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.     

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.     

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...     

移动机器人的智能路径规划算法综述

针对目前移动机器人路径规划所处的的环境复杂度高、随机性强等情况,难以有效地实现最优路径规划的问题,从移动机器人的实际应用出发,对点对点路径规划和遍历路径规划、全局路径规划和局部路径规划进行综述,对各类规划方法进行分析与归纳;重点分析强化学习算法的路径规划技术;针对目前路径规划算法存在的问题,提出类脑智能算法应用于路径规划的探索,同时给出路径规划在农业装备应用的新思路.     

Inference-based surface reconstruction of cluttered environments

We present an inference-based surface reconstruction algorithm that is capable of identifying objects of interest among a cluttered scene, and reconstructing solid model representations even in the presence of occluded surfaces. Our proposed approach incorporates a predictive modeling framework that uses a set of user-provided models for prior knowledge, and applies this knowledge to the iterative identification and construction process. Our approach uses a local to global construction process guided by rules for fitting high-quality surface patches obtained from these prior models. We demonstrate the application of this algorithm on several example data sets containing heavy clutter and occlusion.     

Vibration analysis of cable-driven parallel manipulators

A cable-driven parallel manipulator is a manipulator whose end-effector is driven by a number of parallel cables instead of rigid links. Since cables always have more flexibility than rigid links, a cable manipulator bears a concern of possible vibration. Thus, investigation of vibration of cable manipulators caused by cable flexibility is important for applications requiring high system stiffness or bandwidth. This paper provides a vibration analysis of general 6-DOF cable-driven parallel manipulators. Based on the analysis of the natural frequencies of the multibody system, the study demonstrates that a cable manipulator can be designed stiff enough for special applications like the cable-manipulator based hardware-in-the-loop simulation of contact dynamics. Moreover, under an excitation, a cable may vibrate not only in its axial direction, but also in its transversal direction. The paper also analyzes the vibration of cable manipulators caused by cable flexibilities in both axial and transversal directions. It is shown that the vibration of a cable manipulator due to the transversal vibration of cables can be ignored comparing to that due to the axial flexibility of cables.     

Recursive method of smoothing curvature of path in path planning problems for wheeled robots

Path planning problem is considered as follows. Wheeled robot is manually guided over the required path; coordinates of the path are measured by GNSS receiver. Following the initial path in automatic mode makes it necessary to construct a path which satisfies certain smoothness requirements and curvature constraints. Small measurement errors for the coordinates of the corresponding points may substantially modify the curvature of the constructed path (thus, making it inapplicable for control). A recursive method of smoothing curvature of the curve (composed of uniform cubic B-splines) is proposed; the method can be used in real-time under constraints imposed on available random-access memory.     

A chaotic path planning generator for autonomous mobile robots

This work presents a chaotic path planning generator which is used in autonomous mobile robots, in order to cover a terrain. The proposed generator is based on a nonlinear circuit, which shows chaotic behavior. The bit sequence, produced by the chaotic generator, is converted to a sequence of planned positions, which satisfies the requirements for unpredictability and fast scanning of the entire terrain. The nonlinear circuit and the trajectory-planner are described thoroughly. Simulation tests confirm that with the proposed path planning generator better results can be obtained with regard to previous works.     

Dynamic path planning of mobile robots with improved genetic algorithm

In this study, a new mutation operator is proposed for the genetic algorithm (GA) and applied to the path planning problem of mobile robots in dynamic environments. Path planning for a mobile robot finds a feasible path from a starting node to a target node in an environment with obstacles. GA has been widely used to generate an optimal path by taking advantage of its strong optimization ability. While conventional random mutation operator in simple GA or some other improved mutation operators can cause infeasible paths, the proposed mutation operator does not and avoids premature convergence. In order to demonstrate the success of the proposed method, it is applied to two different dynamic environments and compared with previous improved GA studies in the literature. A GA with the proposed mutation operator finds the optimal path far too many times and converges more rapidly than the other methods do.     

Global path planning of mobile robots using a memetic algorithm

In this paper, a memetic algorithm for global path planning (MAGPP) of mobile robots is proposed. MAGPP is a synergy of genetic algorithm (GA) based global path planning and a local path refinement. Particularly, candidate path solutions are represented as GA individuals and evolved with evolutionary operators. In each GA generation, the local path refinement is applied to the GA individuals to rectify and improve the paths encoded. MAGPP is characterised by a flexible path encoding scheme, which is introduced to encode the obstacles bypassed by a path. Both path length and smoothness are considered as fitness evaluation criteria. MAGPP is tested on simulated maps and compared with other counterpart algorithms. The experimental results demonstrate the efficiency of MAGPP and it is shown to obtain better solutions than the other compared algorithms.     

Multi-UAV trajectory planning with field-of-view sharing mechanism in cluttered environments:application to target tracking

Multi-unmanned aerial vehicle(multi-UAV) target tracking requires coordinated trajectory planning that simultaneously ensures formation safety, target visibility, and motion smoothness. To address this challenge, we propose a distributed framework integrating heuristic-guided path search with spatiotemporal trajectory optimization. In the front end, each UAV constructs a safe area that accounts for both tracking distance and visibility considerations, and selects feasible expansion points for path search based on the desired tracking position. Subsequently, a unified trajectory optimizer is employed to derive an executable trajectory that complies with extensive constraints, predicated on the initial path configurations. Considering that UAVs may lose visibility of the target due to cluttered obstacles, a field-of-view sharing mechanism is proposed, enabling occluded UAVs to leverage teammates' visual data for robust collaborative planning in cluttered environments. Finally, simulations and comparative results are conducted through a visualization platform to validate the superior performance of the proposed multi-UAV trajectory planning framework for target tracking.