NOVEL APPROACH FOR ROBOT PATH PLANNING BASED ON NUMERICAL ARTIFICIAL POTENTIAL FIELD AND GENETIC ALGORITHM

A novel approach for collision-free path planning of a multiple degree-of-freedom （DOF） articulated robot in a complex environment is proposed. Firstly, based on visual neighbor point （VNP）, a numerical artificial potential field is constructed in Cartesian space, which provides the heuristic information, effective distance to the goal and the motion direction for the motion of the robot joints. Secondly, a genetic algorithm, combined with the heuristic rules, is used in joint space to determine a series of contiguous configurations piecewise from initial configuration until the goal configuration is attained. A simulation shows that the method can not only handle issues on path planning of the articulated robots in environment with complex obstacles, but also improve the efficiency and quality of path planning.

Mobile robot path planning method combined improved artificial potential field with optimization algorithm

To overcome the shortcomings of the traditional artificial potential field method in mobile robot path planning, an improved artificial potential field model （IAPFM） was established, then a new path planning method combining the IAPFM with optimization algorithm （trust region algorithm） is proposed. Attractive force between the robot and the target location, and repulsive force between the robot and the obstacles are both converted to the potential field intensity; and filled potential field is used to guide the robot to go out of the local minimum points ; on this basis, the effect of dynamic obstacles velocity and the robot＇s velocity is consid thers and the IAPFM is established, then both the expressions of the attractive potential field and the repulsive potential field are obtained. The trust region algorithm is used to search the minimum value of the sum of all the potential field inten- sities within the movement scope which the robot can arrive in a sampling period. Connecting of all the points which hare the minimum intensity in every sampling period constitutes the global optimization path. Experiment result shows that the method can meet the real-time requirement, and is able to execute the mobile robot path planning task effectively in the dynamic environment.

基于改进APF-QRRT^(*)策略的移动机器人路径规划

针对Q-RRT^(*)算法在路径规划过程中无法兼顾可达性和安全性的问题,提出一种改进APF-QRRT^(*)(IAPF-QRRT^(*))路径规划策略。IAPF-QRRT^(*)策略通过Q-RRT^(*)算法获得一组连接起点到终点的离散关键路径点,较传统的快速搜索随机树(RRT^(*))算法具备更好的初始解和更快的收敛速度。改进传统人工势场(APF)方法获得一种新的无势正交向量场,在一定条件下使整体排斥向量场与吸引向量场正交,并将其作用于关键路径点,从而提高路径的安全性。将IAPF-QRRT^(*)策略与其他算法比较,通过数值模拟实验证明了所提策略的有效性。

改进Informed-RRT^(*)算法的移动机器人路径规划

针对Informed-RRT^(*)算法初始路径形成缓慢、失败率高及路径质量差的问题,提出基于人工势场法的选点策略。首先,筛选出优质采样点,同时,引入双向直连的贪心策略和动态步长策略,快速获得初始路径并尽快进入遍历寻优阶段;其次,通过新的采样策略及评价函数,保证规划路径更优;最后,对路径优化处理,所得路径更适合移动机器人的行驶。仿真实验结果表明,改进算法相比于Informed-RRT^(*)算法性能更优,其中,改进算法在不同环境中的成功率均为100%,同时也证明了在限定采样次数下改进算法的收敛速度、路径质量均优于原算法。

基于改进APF-Informed-RRT^(*)的机械臂避障路径规划

针对Informed-RRT^(*)算法在避障路径规划中缺乏目的性和方向性,存在规划时间长、迭代效率低等问题,提出了结合人工势场法和Informed-RRT^(*)算法的避障规划算法。首先,针对传统人工势场法存在目标点不可达、易与障碍物碰撞的问题,提出了改进后的人工势场法,并将其融入Informed-RRT^(*)算法中,使随机树沿势场下降的方向生长,增强其方向性;其次,依据随机树与障碍物间的距离,提出了一种自适应生长步长策略,提高了对空间的探索能力;最后,引入贪心算法的思想,在生长时直接判断随机树能否直达目标点,提高了路径规划效率。在二维和三维环境下对改进后的算法与传统算法及其衍生算法进行对比实验,仿真结果表明改进后的Informed-RRT^(*)算法相较于原始算法规划的路径长度和规划耗时分别减少了17.42%和36.21%。

基于PIB-RRTstar的荔枝采摘机械臂运动规划方法

为解决非结构环境下,采摘机械臂路径规划时存在的效率低、采摘成功率不高的问题,提出一种结合人工势场法的四向搜索RRTstar算法。首先通过人工势场法对空间进行分割,获取空间分割点x_(split),进行四向搜索;其次结合人工势场法引导随机采样,提高采样节点质量;然后基于节点历史扩展信息,添加信息因子,实现自适应动态步长扩展。最后通过贪婪回溯对生成路径进行优化。通过二维模拟实验、6自由度机械臂下的仿真实验与采摘实验验证提出算法的有效性。二维仿真对比实验表明:相比于RRTstar算法,改进算法路径成本缩短2.01%,时间代价降低98.81%,迭代次数减少92.49%。在机器人操作系统(Robot operating system,ROS)中进行6自由度机械臂下的仿真实验,相比于RRTstar算法,规划时间减少93.17%,路径成本降低36.62%,扩展节点数减少97.00%。最后使用6自由度机械臂进行采摘实验,实验结果表明本文算法有效提升采摘成功率,采摘成功率达85%,并在结合姿态约束方法后,采摘成功率达95%。所提出的路径规划方法在路径规划时间上存在一定优势,采摘实验证明本文算法可提高荔枝采摘成功率。

应用于无人机全局航迹规划的改进双向RRTs算法

针对无人机复杂环境下的全局航迹规划问题,将人工势场法与双向RRTs(Rapidly-exploring random trees)算法结合,提出一种改进双向RRTs算法。首先,目标偏置策略引导采样点以一定概率顺着目标点生成,同时随机树新节点受到障碍物斥力和目标点引力的合力影响有效避开障碍物生长,提高航迹搜寻效率,其次对随机树的节点扩展考虑了无人机飞行性能约束条件,最后采用3阶贝塞尔函数进一步航迹优化。仿真结果表明:二维和三维复杂环境中改进双向RRTs算法相比传统RRT、双向RRTs算法航迹搜索耗时减少了71.3%、24.7%和41.0%、18.6%,验证了改进算法全局搜索能力的快速性和有效性,能很好的应用于无人机离线全局航迹规划场合。

改进RRT^(*)-APF-DP融合算法的机械臂路径规划

针对基本的快速拓展随机树算法(rapidly-exploring random tree,RRT^(*))存在搜索随机性大、效率低、路径非最优的缺点,提出一种引入人工势场法算法(artificial potential field method,APF)和Douglas-Peucker算法的改进RRT^(*)-APF-DP路径规划算法.在RRT*算法的采样点生成阶段引入变采样范围偏置搜索与步长自适应调整策略,融合重新设计的APF算法的引力与斥力函数,增强路径扩展导向性与绕过障碍物能力.采用重采样策略改进DP算法,优化避障代价与控制点数量.实验结果表明,本算法规划的避障路径满足机械臂的运动要求,且算法规划的避障路径代价、规划时间和路径控制节点数均得到有效改善.

基于人工势场算法和RRT算法的多无人机路径规划

为了解决在城市和山区复杂环境中的多无人机任务分配及路径规划问题,提出了一种基于人工势场算法和RRT融合算法的多无人机协同路径规划方法。基于人工势场算法基础优化斥力函数,加入机间斥力因子,实现了协同避撞。引入RRT算法进行拓展搜索,解决了无人机陷入局部极值点时单一人工势场算法目标不可达的问题。通过三维路径规划仿真实验和算法对比实验验证该方法的可行性,结果表明,融合路径规划算法可以在约束条件下找到全局最优路径。

复杂环境下基于改进Informed RRT*的无人机路径规划算法

针对无人机在复杂环境中进行路径规划时,快速搜索随机树(RRT)算法易出现规划时间长、路径冗余、狭窄空间中易陷入局部约束导致规划失败的问题,提出一种改进的Informed RRT*算法.首先,引入人工势场法使采样点按照势场下降的方式向目标点移动,以提高RRT树扩展的目的性和方向性.然后,考虑随机树在扩展过程中全局环境的复杂度,引入自适应步长调整策略以增加随机树在无障碍环境下的扩展速度,并在随机树扩展的过程中加入相关约束条件以确保生成路径的可行性.在找到第一条可达路径后,采用变化的椭圆或椭球采样域限制采样点选取和自适应步长的扩展范围,加快算法收敛到渐进最优的速度.最后,在复杂二维和三维环境下进行传统算法和改进算法的对比实验,仿真分析表明:改进算法可以在很少的迭代次数下找到更优的初始路径,更快地锁定椭圆或椭球采样域,从而给路径优化留出更多时间,算法规划效果更好.

Ant Colony Optimization with Potential Field Based on Grid Map for Mobile Robot Path Planning

For the mobile robot path planning under the complex environment,ant colony optimization with artificial potential field based on grid map is proposed to avoid traditional ant colony algorithm's poor convergence and local optimum.Firstly,the pheromone updating mechanism of ant colony is designed by a hybrid strategy of global map updating and local grids updating.Then,some angles between the vectors of artificial potential field and the orientations of current grid are introduced to calculate the visibility of eight-neighbor cells of cellular automata,which are adopted as ant colony's inspiring factor to calculate the transition probability based on the pseudo-random transition rule cellular automata.Finally,mobile robot dynamic path planning and the simulation experiments are completed by this algorithm,and the experimental results show that the method is feasible and effective.

Path Planning for AUVs Based on Improved APF-AC Algorithm

With the increase in ocean exploration activities and underwater development,the autonomous underwater vehicle(AUV)has been widely used as a type of underwater automation equipment in the detection of underwater environments.However,nowadays AUVs generally have drawbacks such as weak endurance,low intelligence,and poor detection ability.The research and implementation of path-planning methods are the premise of AUVs to achieve actual tasks.To improve the underwater operation ability of the AUV,this paper studies the typical problems of path-planning for the ant colony algorithm and the artificial potential field algorithm.In response to the limitations of a single algorithm,an optimization scheme is proposed to improve the artificial potential field ant colony(APF-AC)algorithm.Compared with traditional ant colony and comparative algorithms,the APF-AC reduced the path length by 1.57%and 0.63%(in the simple environment),8.92%and 3.46%(in the complex environment).The iteration time has been reduced by approximately 28.48%and 18.05%(in the simple environment),18.53%and 9.24%(in the complex environment).Finally,the improved APF-AC algorithm has been validated on the AUV platform,and the experiment is consistent with the simulation.Improved APF-AC algorithm can effectively reduce the underwater operation time and overall power consumption of the AUV,and shows a higher safety.

基于改进RRT^(*)算法的无人机在线航迹规划

针对快速扩展随机树(rapidly-exploring random tree,RRT)^(*)算法应用于无人机(unmanned aerial vehicle,UAV)航迹规划时采样效率低、收敛速度慢、航迹代价大的问题,采用势场法引导树扩展加快算法收敛速度。采用优化算法重选父节点及重新布线,生成比RRT^(*)算法代价更小的初始航迹。基于初始航迹构建启发式采样区域,以更有效地优化初始航迹,给出一种改进RRT^(*)算法;基于模型预测控制,设计航迹规划策略,UAV在飞行中能良好地应对环境中的动态威胁。数学仿真实验结果表明,改进算法能快速地生成代价更小的初始航迹,并在后续航迹优化的过程中更有效地减少航迹代价,可被应用于无人机在线规划任务。

改进RRT的复杂障碍物环境路径规划算法研究

为解决传统的RRT算法在复杂障碍物环境下时,存在采样随机性大,搜索效率低,规划路径曲折等问题,提出一种改进的RRT算法。将传统RRT算法的随机树扩展引入自适应步长目标偏置策略,加快向目标点的收敛速度;结合人工势场法的概念,在随机树中引入目标点的引力分量和障碍物周围的斥力分量,并限制障碍物附近的搜索区域;对改进RRT算法规划的路径引入局部定向剪枝概念,对路径关键点择优剪枝选取,快速逃离局部振荡阶段;加入三次B样条曲线对路径进行平滑处理,降低路径曲折程度,以保证路径的可行性。通过实验表明,改进的RRT算法在保证对同一路径搜索的同时,显著地减少了68.5%的搜索时间,并且减少了20%的节点迭代次数,更适用于复杂障碍物环境下的路径规划。

基于AGD-RRT算法的机械臂路径规划

针对RRT(rapidly-exploring random tree)算法在进行机械臂路径规划过程中存在的拓展导向性差、冗余节点多、路径质量差等问题,提出了一种基于AGD-RRT(adaptive goal-directed RRT)的算法。首先,该算法构建了一种动态的目标偏向概率函数,实时调整对目标点进行采样的概率以达到自适应目标导向的效果,减少了无用节点的生成,提高了收敛速度。其次,采用贪婪收敛策略,防止了随机树在目标周围时的盲目扩张。搜索结束后,采用节点剔除法剔除路径中的冗余节点,并用B样条曲线对轨迹进行平滑处理,提高了路径质量。然后在二维、三维环境中进行了对比仿真实验,验证了该算法的可行性与优越性。最后进行了样机实验,验证了所提算法在机械臂关节空间进行路径规划的可行性。

LSDA-APF:A Local Obstacle Avoidance Algorithm for Unmanned Surface Vehicles Based on 5G Communication Environment

In view of the complex marine environment of navigation,especially in the case of multiple static and dynamic obstacles,the traditional obstacle avoidance algorithms applied to unmanned surface vehicles(USV)are prone to fall into the trap of local optimization.Therefore,this paper proposes an improved artificial potential field(APF)algorithm,which uses 5G communication technology to communicate between the USV and the control center.The algorithm introduces the USV discrimination mechanism to avoid the USV falling into local optimization when the USV encounter different obstacles in different scenarios.Considering the various scenarios between the USV and other dynamic obstacles such as vessels in the process of performing tasks,the algorithm introduces the concept of dynamic artificial potential field.For the multiple obstacles encountered in the process of USV sailing,based on the International Regulations for Preventing Collisions at Sea(COLREGS),the USV determines whether the next step will fall into local optimization through the discriminationmechanism.The local potential field of the USV will dynamically adjust,and the reverse virtual gravitational potential field will be added to prevent it from falling into the local optimization and avoid collisions.The objective function and cost function are designed at the same time,so that the USV can smoothly switch between the global path and the local obstacle avoidance.The simulation results show that the improved APF algorithm proposed in this paper can successfully avoid various obstacles in the complex marine environment,and take navigation time and economic cost into account.

融合RRT^(*)与APF算法的机器人路径规划研究

针对传统快速扩展随机树(rapidly-exploring random tree star,RRT^(*))算法在全局路径规划过程中存在收敛速度慢、搜索路径不平滑、内存占用多等问题,提出了一种RRT^(*)与人工势场法(artificial potential field,APF)的融合搜索算法。为了加快RRT^(*)算法在搜索过程中的收敛速度,在算法中利用人工势场法的思想引导扩展随机树快速向目标点生长;对融合算法在空间中的采样范围做出改进,使算法在APF产生的合力特定范围内进行采样,提高算法在空间中的搜索效率,减少无用节点的扩展。仿真结果表明:相比传统的RRT和RRT^(*)算法以及APF-RRT融合算法,APF-RRT^(*)融合算法能够规划出更短、更平滑的路径,路径距离缩短了1.5%~10.83%;算法的搜索时间也显著缩短了1.97%~49.78%;与其他算法相比,APF-RRT^(*)融合算法的路径节点数量减少了4.66%~41.95%,路径平滑性也得到了提高。

基于RRT的机械臂路径规划改进算法

针对快速随机树(Rapidly-exploring Random Trees,RRT)算法在复杂环境下规划效率低的问题,提出一种基于RRT的机械臂路径规划改进算法。首先,在初始采样时应用角度约束采样策略限制采样区域,提升采样质量。然后,在扩展节点时融合人工势场法的思想,设定动态步长加快算法的收敛,提升算法在障碍物空间的探索效率,当算法陷入局部极小值时,采用节点拒绝策略快速脱离。最后,将规划路径进行简化处理,并利用B样条曲线平滑拐点提高路径质量。仿真结果表明,改进算法相比传统RRT算法,扩展更具导向性,收敛速度更快,可以有效避免局部极小值。

基于优化RRT算法的智能车辆路径规划策略

为了解决快速搜索随机树(RRT)算法存在收敛速度慢、生成路径质量差等问题,提出一种融合算法,即将人工势场法以及A*算法的代价函数融合到RRT算法中。使用改进后的RRT算法生成路径,并将其所搜寻到的路径作为初始解传递给A*算法,进而重新规划出一条更优路径,并结合车辆运动学约束,利用贝塞尔曲线平滑路径。选取两种地图场景进行仿真实验,实验结果表明,提出的改进策略可以更快地生成质量更高且更符合汽车实际行驶的路径。仿真结果证明了该算法能有效提升RRT算法的收敛速度和路径质量。

煤矿环境下改进Informed-RRT*路径规划算法研究

为解决煤矿机器人视觉SLAM地图中传统路径规划Informed-RRT*算法收敛速度慢、生成路径代价高、甚至无法找到路径等问题,对视觉SLAM建图进行了研究,提出了一种基于八叉树地图的路径规划方法。首先构建稠密点云地图并生成八叉树地图,将八叉树地图转化为易于路径规划的二维栅格地图,在此地图基础上结合人工势场和Informed-RRT*路径规划方法,提出了一种改进Informed-RRT*路径规划算法。仿真结果表明,该算法比传统Informed-RRT*算法速度提升了29.74%,具有更高的搜索效率和更快的收敛速度,获得了更好的路径规划效果。