Multi-Robot Collaborative Hunting in Cluttered Environments With Obstacle-Avoiding Voronoi Cells

This work proposes an online collaborative hunting strategy for multi-robot systems based on obstacle-avoiding Voronoi cells in a complex dynamic environment. This involves firstly designing the construction method using a support vector machine(SVM) based on the definition of buffered Voronoi cells(BVCs). Based on the safe collision-free region of the robots, the boundary weights between the robots and the obstacles are dynamically updated such that the robots are tangent to the buffered Voronoi safety areas without intersecting with the obstacles. Then, the robots are controlled to move within their own buffered Voronoi safety area to achieve collision-avoidance with other robots and obstacles. The next step involves proposing a hunting method that optimizes collaboration between the pursuers and evaders. Some hunting points are generated and distributed evenly around a circle. Next, the pursuers are assigned to match the optimal points based on the Hungarian algorithm.Then, a hunting controller is designed to improve the containment capability and minimize containment time based on collision risk. Finally, simulation results have demonstrated that the proposed cooperative hunting method is more competitive in terms of time and travel distance.

Swarm intelligence based dynamic obstacle avoidance for mobile robots under unknown environment using WSN

To solve dynamic obstacle avoidance problems, a novel algorithm was put forward with the advantages of wireless sensor network (WSN). In view of moving velocity and direction of both the obstacles and robots, a mathematic model was built based on the exposure model, exposure direction and critical speeds of sensors. Ant colony optimization (ACO) algorithm based on bionic swarm intelligence was used for solution of the multi-objective optimization. Energy consumption and topology of the WSN were also discussed. A practical implementation with real WSN and real mobile robots were carried out. In environment with multiple obstacles, the convergence curve of the shortest path length shows that as iterative generation grows, the length of the shortest path decreases and finally reaches a stable and optimal value. Comparisons show that using sensor information fusion can greatly improve the accuracy in comparison with single sensor. The successful path of robots without collision validates the efficiency, stability and accuracy of the proposed algorithm, which is proved to be better than tradition genetic algorithm (GA) for dynamic obstacle avoidance in real time.

Dynamic A^*path finding algorithm and 3D lidar based obstacle avoidance strategy for autonomous vehicles

This paper presents a novel dynamic A^*path finding algorithm and 3D lidar based local obstacle avoidance strategy for an autonomous vehicle.3D point cloud data is collected and analyzed in real time.Local obstacles are detected online and a 2D local obstacle grid map is constructed at 10 Hz/s.The A^*path finding algorithm is employed to generate a local path in this local obstacle grid map by considering both the target position and obstacles.The vehicle avoids obstacles under the guidance of the generated local path.Experiment results have shown the effectiveness of the obstacle avoidance navigation algorithm proposed.

Application of A* Algorithm for Real-time Path Re-planning of an Unmanned Surface Vehicle Avoiding Underwater Obstacles

This paper describes path re-planning techniques and underwater obstacle avoidance for unmanned surface vehicle(USV) based on multi-beam forward looking sonar(FLS). Near-optimal paths in static and dynamic environments with underwater obstacles are computed using a numerical solution procedure based on an A* algorithm. The USV is modeled with a circular shape in 2 degrees of freedom(surge and yaw). In this paper, two-dimensional(2-D) underwater obstacle avoidance and the robust real-time path re-planning technique for actual USV using multi-beam FLS are developed. Our real-time path re-planning algorithm has been tested to regenerate the optimal path for several updated frames in the field of view of the sonar with a proper update frequency of the FLS. The performance of the proposed method was verified through simulations, and sea experiments. For simulations, the USV model can avoid both a single stationary obstacle, multiple stationary obstacles and moving obstacles with the near-optimal trajectory that are performed both in the vehicle and the world reference frame. For sea experiments, the proposed method for an underwater obstacle avoidance system is implemented with a USV test platform. The actual USV is automatically controlled and succeeded in its real-time avoidance against the stationary undersea obstacle in the field of view of the FLS together with the Global Positioning System(GPS) of the USV.

Dynamic collision avoidance for cooperative fixed-wing UAV swarm based on normalized artificial potential field optimization

Cooperative path planning is an important area in fixed-wing UAV swarm.However,avoiding multiple timevarying obstacles and avoiding local optimum are two challenges for existing approaches in a dynamic environment.Firstly,a normalized artificial potential field optimization is proposed by reconstructing a novel function with anisotropy in each dimension,which can make the flight speed of a fixed UAV swarm independent of the repulsive/attractive gain coefficient and avoid trapping into local optimization and local oscillation.Then,taking into account minimum velocity and turning angular velocity of fixed-wing UAV swarm,a strategy of decomposing target vector to avoid moving obstacles and pop-up threats is proposed.Finally,several simulations are carried out to illustrate superiority and effectiveness.

On-line real-time path planning of mobile robots in dynamic uncertain environment

A new path planning method for mobile robots in globally unknown environment with moving obstacles is pre- sented. With an autoregressive (AR) model to predict the future positions of moving obstacles, and the predicted position taken as the next position of moving obstacles, a motion path in dynamic uncertain environment is planned by means of an on-line real-time path planning technique based on polar coordinates in which the desirable direction angle is taken into consideration as an optimization index. The effectiveness, feasibility, high stability, perfect performance of obstacle avoidance, real-time and optimization capability are demonstrated by simulation examples.

A dynamic fusion path planning algorithm for mobile robots incorporating improved IB-RRT∗and deep reinforcement learning

Dynamic path planning is crucial for mobile robots to navigate successfully in unstructured envi-ronments.To achieve globally optimal path and real-time dynamic obstacle avoidance during the movement,a dynamic path planning algorithm incorporating improved IB-RRT∗and deep reinforce-ment learning(DRL)is proposed.Firstly,an improved IB-RRT∗algorithm is proposed for global path planning by combining double elliptic subset sampling and probabilistic central circle target bi-as.Then,to tackle the slow response to dynamic obstacles and inadequate obstacle avoidance of tra-ditional local path planning algorithms,deep reinforcement learning is utilized to predict the move-ment trend of dynamic obstacles,leading to a dynamic fusion path planning.Finally,the simulation and experiment results demonstrate that the proposed improved IB-RRT∗algorithm has higher con-vergence speed and search efficiency compared with traditional Bi-RRT∗,Informed-RRT∗,and IB-RRT∗algorithms.Furthermore,the proposed fusion algorithm can effectively perform real-time obsta-cle avoidance and navigation tasks for mobile robots in unstructured environments.

Dynamic Frontier-Led Swarming:Multi-Robot Repeated Coverage in Dynamic Environments

A common assumption of coverage path planning research is a static environment.Such environments require only a single visit to each area to achieve coverage.However,some real-world environments are characterised by the presence of unexpected,dynamic obstacles.They require areas to be revisited periodically to maintain an accurate coverage map,as well as reactive obstacle avoidance.This paper proposes a novel swarmbased control algorithm for multi-robot exploration and repeated coverage in environments with unknown,dynamic obstacles.The algorithm combines two elements:frontier-led swarming for driving exploration by a group of robots,and pheromone-based stigmergy for controlling repeated coverage while avoiding obstacles.We tested the performance of our approach on heterogeneous and homogeneous groups of mobile robots in different environments.We measure both repeated coverage performance and obstacle avoidance ability.Through a series of comparison experiments,we demonstrate that our proposed strategy has superior performance to recently presented multi-robot repeated coverage methodologies.

Real-time dynamic system to path tracking and collision avoidance for redundant robotic arms

An dynamic system for real-time obstacle avoidance path planning of redundant robots is constructed in this paper. Firstly, the inter-frame difference method is used to identify the moving target and to calculate the target area, then on the basis of color features and gradient features extracted from the target area, the feature fusion Cam-Shift mean shift algorithm is used to track target, improving the robustness of the tracking algorithm. Secondly, a parallel two-channel target identification and location method based on binocular vision is proposed, updating the target＇s three-dimensional information in real time. Then, a dynamic collision-free path planning method is implemented： the safety rods are removed through the intersection test, and the minimum distance is derived directly by using the coordinate values of the target in the local coordinate system of the rod. On this basis, the obstacle avoidance gain and escape velocity related to the minimum distance is established, and obstacle avoidance path planning is implemented by using the zero space mapping matrix of redundant robot. Experiments are performed to Study the efficiency of the proposed system.

Dynamic Obstacle Avoidance for Application of Human-Robot Cooperative Dispensing Medicines

For safety reasons,in the automated dispensing medicines process,robots and humans cooperate to accomplish the task of drug sorting and distribution.In this dynamic unstructured environment,such as a humanrobot collaboration scenario,the safety of human,robot,and equipment in the environment is paramount.In this work,a practical and effective robot motion planning method is proposed for dynamic unstructured environments.To figure out the problems of blind zones of single depth sensor and dynamic obstacle avoidance,we first propose a method for establishing offline mapping and online fusion of multi-sensor depth images and 3D grids of the robot workspace,which is used to determine the occupation states of the 3D grids occluded by robots and obstacles and to conduct real-time estimation of the minimum distance between the robot and obstacles.Then,based on the reactive control method,the attractive and repulsive forces are calculated and transformed into robot joint velocities to avoid obstacles in real time.Finally,the robot’s dynamic obstacle avoidance ability is evaluated on an experimental platform with a UR5 robot and two KinectV2 RGB-D sensors,and the effectiveness of the proposed method is verified.

采摘机器人的路径规划系统动态性优化研究

为进一步改善采摘机器人的工作性能,提出以动态调控为主导的理念,针对整机的路径规划系统展开优化研究。以当前果园采摘机器人的通用性结构组成为前提,将云平台数据处理与路径规划核心算法有效融合后搭建动态控制模型,分别针对路径规划系统的硬件配置与软件控制进行合理设计,得到可应用于采摘实践且布局完整的路径规划系统。展开动态性优化下的采摘作业试验,结果表明:优化后采摘机器人路径规划系统的整体路径搜索率与路径平滑性得到明显提升,相对提升度分别为10.93%和9.71%,路径偏离率相对降低了50%左右,很好地优化了机器人的避障能力,满足系统稳定性需求,具有较高的实用价值。

融合改进A^(*)算法和动态窗口法的自动驾驶路径规划

针对自动驾驶汽车路径规划全局最优、耗时最优和避障的需求,提出一种改进A^(*)算法和动态窗口法的融合算法。A^(*)算法主要从启发函数、权重系数、搜索邻域和搜索策略4个方面进行改进,动态窗口法主要改进评价函数。利用改进后的A^(*)算法和双向A^(*)算法完成栅格地图上的全局路径规划,去除冗余节点并平滑处理优化全局路径,利用融合动态窗口算法进行局部路径规划,完成避障。与传统的A^(*)算法相比,改进的A^(*)算法和双向A^(*)算法搜索全局路径耗时和节点显著减少,优化的A^(*)算法与动态窗口法的融合算法具有更高的效率、更好的路径规划能力和避障能力。

基于改进动态窗口的车库AGV路径规划及仿真

针对在智能车库环境下,移动机器人(AGV)在作业过程中面临着复杂路径环境下的路径规划和实时避障,提出一种改进的蚁群算法与动态窗口法(DWA)相结合的混合算法。在全局规划中引入自适应调整信息素挥发系数,融合角度参数建立车库方向信息素矩阵,增大目标点引导能力,扩大蚂蚁的方向选择性;在局部规划中设计基于椭圆方程的障碍物距离评价子函数的改进动态窗口法,并提取改进蚁群算法的全局关键节点作为新增的路径评价子函数,在全局路径的约束下,保持动态规划的最优性。仿真结果表明:改进算法较传统算法能够较好地应对突发的动静态障碍物,更符合AGV实际作业过程中在多重环境因素的影响下的动态规划要求。

改进Q-Learning的路径规划算法研究

针对Q-Learning算法学习效率低、收敛速度慢且在动态障碍物的环境下路径规划效果不佳的问题,本文提出一种改进Q-Learning的移动机器人路径规划算法.针对该问题,算法根据概率的突变性引入探索因子来平衡探索和利用以加快学习效率;通过在更新函数中设计深度学习因子以保证算法探索概率;融合遗传算法,避免陷入局部路径最优同时按阶段探索最优迭代步长次数,以减少动态地图探索重复率;最后提取输出的最优路径关键节点采用贝塞尔曲线进行平滑处理,进一步保证路径平滑度和可行性.实验通过栅格法构建地图,对比实验结果表明,改进后的算法效率相较于传统算法在迭代次数和路径上均有较大优化,且能够较好的实现动态地图下的路径规划,进一步验证所提方法的有效性和实用性.

复杂环境下DWA与RRT算法融合的AUV局部路径规划

针对复杂水下环境下的自主水下航行器(autonomous underwater vehicle,AUV)局部路径规划问题,传统动态窗口法(dynamic window approach,DWA)存在复杂障碍物中陷入局部停滞,动态避障性能不佳等问题,本文提出了一种基于DWA与快速随机搜索树(rapid-exploration random tree,RRT)算法融合的路径规划算法。改进的DWA算法速度空间根据整个动态窗口的周期生成,重设了评价函数并结合AUV任务环境引入洋流能耗评价函数;改进的RRT算法在局部已知空间内规划导引点,帮助DWA脱离局部停滞状态并实现更安全的动态避障。将2种算法融合,实现了AUV在复杂水下环境中的局部路径规划。仿真表明,该融合算法能够降低AUV在洋流中的能耗代价,解决了DWA在复杂障碍物中陷入局部停滞的问题,能够安全有效地躲避动态避障物。

近海复杂环境下UUV动态路径规划方法研究

为解决近海环境下水下无人航行器(unmanned underwater vehicle,UUV)的动态路径规划问题,本文提出一种结合全局和局部动态路径规划的算法。首先,本文提出一种基于自适应目标引导的快速拓展随机树算法,以增加随机树生长的方向性,并通过转向和重选策略减少无效拓展加快算法的收敛速度。接着,获得全局路径之后使用自适应子节点选取策略获取动态窗口法的子目标点,将复杂的全局动态任务规划分解为多个简单的动态路劲规划,从而防止动态窗口法陷入局部极小值。最后,通过UUV出港任务仿真实验验证了算法的有效性和实用性。

煤矿履带式定向钻机路径规划算法

煤矿履带式定向钻机路径规划过程中存在机身体积约束和实际场景下的行驶效率需求,而常用的A^(*)算法搜索速度慢、冗余节点多,且规划路径贴近障碍物、平滑性较差。提出一种以改进A^(*)算法规划全局路径、融合动态窗口法(DWA)规划局部路径的煤矿履带式定向钻机路径规划算法。考虑定向钻机尺寸影响,在传统A^(*)算法中引入安全扩展策略,即在定向钻机和巷道壁、障碍物之间加入安全距离约束,以提高规划路径的安全性;对传统A^(*)算法的启发函数进行自适应权重优化,同时将父节点的影响加入到启发函数中,以提高全局路径搜索效率;利用障碍物检测原理对经上述改进后的A^(*)算法规划路径剔除冗余节点,并使用分段三次Hermite插值进行二次平滑处理,得到全局最优路径。将改进A^(*)算法与DWA融合,进行煤矿井下定向钻机路径规划。利用Matlab对不同工况环境下定向钻机路径规划算法进行仿真对比分析,结果表明:与Dijkstra算法和传统A^(*)算法相比,改进A^(*)算法在保证安全距离的前提下,加快了搜索速度,搜索时间分别平均减少88.5%和63.2%,且在一定程度上缩短了规划路径的长度,路径更加平滑;改进A^(*)算法与DWA融合算法可有效躲避改进A^(*)算法规划路径上的未知障碍物,路径长度较PRM算法和RRT^(*)算法规划的路径分别平均减小5.5%和2.9%。

基于改进哈里斯鹰优化算法的动态路径规划研究

针对传统栅格地图下的路径规划算法存在多峰值优化、无法实时避障等问题,提出了一种基于改进哈里斯鹰优化算法的动态路径规划方法。首先,提出方形邻格邻近扩散方法初始化哈里斯鹰种群位置,在路径规划问题模型下增加种群多样性;然后,提出一种非线性能量因子优化算法在搜索和开发之间的更新比例,提高全局搜索性能;最后,引入动态窗口法提高机器人实际运行路径的平滑程度,构造结合全局路径的动态窗口评价函数以改善动态窗口法前瞻性不足的问题。实验结果表明,所提方法可以兼顾实时避障和路径最优的需求。

改进粒子群算法的机器人避障偏差控制方法

为针对巡检机器人避障偏差进行良好控制,提升避障效果,提出改进粒子群算法的机器人避障偏差控制方法设计。先分析巡检机器人正向动力学和逆向动力学,获取双走轮坐标系下机器人的运动情况,建立机器人运动学方程。然后以此为基础,在双走轮坐标系中,通过改进粒子群算法确定巡检机器人全局避障最优路径,采用改进人工势场法完成局部避障路径规划,最后采用前馈补偿控制器为动力学方程和最优路径建立动态补偿,根据控制器输出的训练结果,实现巡检机器人避障偏差自动控制。实验结果表明:所提方法避障规划能力及避障运动控制能力均较强,避障偏差控制效果好,具有一定应用价值。

基于视觉定位的仓储搬运机器人避障控制方法

为保证仓储搬运机器人的工作效率和安全,在动态环境下,提出基于视觉定位的仓储搬运机器人避障控制方法。利用视觉定位技术,通过环境图像生成、预处理、特征提取、障碍物识别等步骤,确定动态环境中的障碍物位置。检测仓储搬运机器人实时位姿,考虑障碍物实时位置和搬运目标地点,规划出机器人移动路径。根据机器人位姿与规划路径之间的偏差,计算仓储搬运机器人避障控制量,在控制器的支持下,实现仓储搬运机器人避障控制工作。实验结果表明:与传统方法相比,采用优化设计的避障控制方法后,仓储搬运机器人在动态环境中的碰撞次数明显减少,位置和姿态角控制误差均值分别为0.08 m和0.029°,所提方法的控制数据优于传统控制方法。