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.

Simulation research for mobile robot path planning based on improved artificial potential field method recommended by the AsiaSim

Mobile robot path planning is an important research branch in the field of mobile robots.The main disadvantage of the traditional artificial potential field(APF)method is prone to local minima problems.Improved artificial potential field(IAPF)method is presented in this paper to solve the problem in the traditional APF method for robot path planning in different conditions.We introduce the distance between the robot and the target point to the function of the original repulsive force field and change the original direction of the repulsive force to avoid the trap problem caused by the local minimum point.The IAPF method is suitable for mobile robot path planning in the complicated environment.Simulation and experiment results at the robot platform illustrated the superiority of the modified IAPF method.

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.

Optimisation-based Verification Process of Obstacle Avoidance Systems for Unicycle-like Mobile Robots

This paper presents an optimisatiombased verification process for obstacle avoidance systems of a unicycle-like mobile robot. It is a novel approach for the collision avoidance verification process. Local and global optimisation based verification processes are developed to find the worst-case parameters and the worst-case distance between the robot and an obstacle. The kinematic and dynamic model of the unicycle-like mobile robot is first introduced with force and torque as the inputs. The design of the control system is split into two parts. One is velocity and rotation using the robot dynamics, and the other is the incremental motion planning for robot kinematics. The artificial potential field method is chosen as a path planning and obstacle avoidance candidate technique for verification study as it is simple and widely used. Different optimisation algorithms are applied and compared for the purpose of verification. It is shown that even for a simple case study where only mass and inertia variations are considered, a local optimization based verification method may fail to identify the worst case. Two global optimisation methods have been investigated： genetic algorithms （GAs） and GLOBAL algorithms. Both of these methods successfully find the worst case. The verification process confirms that the obstacle avoidance algorithm functions correctly in the presence of all the possible parameter variations.

Novel Biological Based Method for Robot Navigation and Localization

The capability and reliability are crucial characteristics of mobile robots while navigating in complex environments. These robots are expected to perform many useful tasks which can improve the quality of life greatly. Robot localization and decisionmaking are the most important cognitive processes during navigation. However, most of these algorithms are not efficient and are challenging tasks while robots navigate through complex environments. In this paper,we propose a biologically inspired method for robot decision-making, based on rat’s brain signals. Rodents accurately and rapidly navigate in complex spaces by localizing themselves in reference to the surrounding environmental landmarks. Firstly, we analyzed the rats’ strategies while navigating in the complex Y-maze, and recorded local field potentials(LFPs), simultaneously.The recorded LFPs were processed and different features were extracted which were used as the input in the artificial neural network(ANN) to predict the rat’s decision-making in each junction. The ANN performance was tested in a real robot and good performance is achieved. The implementation of our method on a real robot, demonstrates its abilities to imitate the rat’s decision-making and integrate the internal states with external sensors, in order to perform reliable navigation in complex maze.

Solution to reinforcement learning problems with artificial potential field

A novel method was designed to solve reinforcement learning problems with artificial potential field.Firstly a reinforcement learning problem was transferred to a path planning problem by using artificial potential field(APF),which was a very appropriate method to model a reinforcement learning problem.Secondly,a new APF algorithm was proposed to overcome the local minimum problem in the potential field methods with a virtual water-flow concept.The performance of this new method was tested by a gridworld problem named as key and door maze.The experimental results show that within 45 trials,good and deterministic policies are found in almost all simulations.In comparison with WIERING's HQ-learning system which needs 20 000 trials for stable solution,the proposed new method can obtain optimal and stable policy far more quickly than HQ-learning.Therefore,the new method is simple and effective to give an optimal solution to the reinforcement learning problem.

基于改进人工势场算法的煤矿井下机器人路径规划

路径规划是煤矿机器人在煤矿井下狭小巷道空间中应用亟待解决的关键技术之一。针对传统人工势场(APF)算法在狭小巷道环境中规划出的路径可能离巷道边界过近,以及在障碍物附近易出现目标不可达和路径振荡等问题,提出了一种基于改进APF算法的煤矿机器人路径规划方法。参考《煤矿安全规程》有关规定建立了巷道两帮边界势场,将机器人行驶路径尽量规划在巷道中间,以提高机器人行驶安全性;在障碍物斥力势场中引入调节因子,以解决目标不可达问题;引入转角限制系数以平滑规划出的路径,减少振荡,提高规划效率,保证规划路径的安全性。仿真结果表明:当目标点离障碍物很近时,改进APF算法可成功规划出能够抵达目标点的路径;改进APF算法规划周期数较传统算法平均减少了14.48%,转向角度变化累计值平均减少了87.41%,曲率绝对值之和平均减少了78.09%,表明改进APF算法规划的路径更加平滑,路径长度更短,规划效率和安全性更高。

改进人工势场算法的路径规划

人工势场法是一种适用于局部路径规划的算法,针对人工势场法存在的目标不可达、局部极小值等固有缺陷,提出了一种改进后的人工势场法。首先针对目标不可达问题,将引力影响因子添加到斥力场的生成中,改进斥力场函数,从而避免引力与斥力合力为零的情况。针对局部极小值问题,通过设立虚拟目标点来引入额外外力,打破机器人的平衡问题。通过与其他算法的对比实验,仿真结果显示,改进人工势场法规划的路径长度和消耗时间都更短,稳定性更强。

室内环境下改进的混合路径规划算法

为了解决室内非结构化复杂环境下的机器人在路径规划时常常出现目标点不可达、规划过程产生折角偏移、规划过程无法及时规避动态障碍物等问题,提出一种改进的混合室内路径规划算法。该算法将改进的全局路径规划与改进的局部路径规划算法相融合。首先,优化传统A-Star算法的启发因子,减少搜索范围和节点,再通过角平分线切点法对传统A-Star算法进行平滑处理。其次,综合路径与环境信息,采用改进的人工势场算法进行局部路径规划,通过修正斥力场参数来解决目标点不可达问题,同时构造了动态的势力场函数,使其具备决解决动态障碍物的能力。最后,对混合算法进行实际环境的路径规划实验,比起传统的混合算法文中提出的混合算法在路径规划长度上减少11.4%,运行时间减少11.1%,少经过34个冗余节点,结果表明该融合算法可以有效解决室内非结构化复杂的路径规划问题。

基于改进APF-FMT^(*)的农业机器人路径规划算法

【目的】解决农业机器人在复杂农业环境下全局路径规划耗时过长、路径最优解求解困难的问题。【方法】提出一种基于改进人工势场法的快速行进树算法(APF-FMT^(*))。首先,在引力势场中引入相对距离,根据与目标点的距离改变引力大小,克服了人工势场法距离目标点过远时引力过大的问题;然后,将FMT^(*)算法与改进人工势场法相结合,采用三阶B样条曲线对路径进行平滑处理;最后,建立3个农业工作地图进行仿真试验。【结果】仿真结果表明,与FMT^(*)、RRT^(*)和Informed-RRT^(*)3种算法对比,在地图Map1和Map2中,APF-FMT^(*)都能快速找到良好的解,且随样本数量增加获得的路径解得到改善,搜索时间比其他3种算法减少45%以上;在有狭小通道的Map3中,APF-FMT^(*)、FMT^(*)搜索时间比RRT^(*)和Informed-RRT^(*)减少75%以上,并且获得更好的解。【结论】本研究提出的APF-FMT^(*)算法不仅克服了FMT^(*)算法冗余探索问题,还有效地解决了人工势场法目标点不可达的问题,提高了农业机器人路径规划效率和作业安全性。

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.

基于分布式参考校正的多机器人编队控制

针对避障、外部干扰和迟滞等实际的多机器人编队控制问题,提出了一种基于分布式参考校正的控制方法。通过采用人工势场法和干扰观测器,解决了非匹配不确定性的避障问题。定义不可达参考场景描述了多机器人在试图避开障碍物时的被动修正行为,并为每个机器人设计了分布式参考校正算法,减弱被动校正行为带来的不利影响,保证每个机器人位置跟踪误差的有界性。考虑已知的执行器迟滞效应,在电流控制律中添加了Bouc-Wen迟滞补偿器。利用Lyapunov稳定性理论验证了避障的有效性。最后,在MATLAB环境下基于多机器人系统进行了数值仿真和比较,以证明所提出的算法和控制器的有效性,实验结果表明该方法可以在具有外部干扰和迟滞等实际问题下形成稳定的多机器人编队控制,对路径上的障碍物进行无碰撞避障,提出的分布式参考校正算法减弱了被动校正行为,提高了系统的稳定性。

基于改进D*Lite⁃APF算法的巡检机器人路径规划

针对巡检机器人在动态场景下路径规划存在非全局最优、路径不平滑及局部避障效果不佳的问题,提出一种将改进D*Lite算法和人工势场法融合的算法。首先优化D*Lite算法启发代价函数,提升规划效率,并引入Dubins曲线平滑生成的全局路径;其次改进人工势场法势场函数并添加随机半径扰动点,解决局部碰撞问题,提高避障性能;最后将两种优化算法有效融合,实现全局规划和局部避障。仿真实验结果表明,相较于单一D*Lite算法,融合算法在路径长度、时间花销、路径拐点及扩展节点数方面均表现更优,能在确保全局路径最优的情况下有效避障。

基于含避障角人工势场法的机器人路径规划

针对基于距离的人工势场法(Artificial potential field, APF)存在的局部极小值问题,提出了一种含避障角的人工势场法的避障路径规划方法。在平面环境中,采用斜率判定路径规划过程中的位置关系,通过机器人当前点到障碍物的距离与障碍物的影响半径二者之差得出人工势场法中的排斥力,并对排斥力的偏转角进行调整;另外,在空间环境中利用圆弧插补理论将机器人平面避障问题转换为空间避障问题;基于机器人构型配置对改进人工势场法进一步完善以满足实际避障要求。仿真和实验研究结果表明,含避障角的人工势场法,在单个或多个障碍物环境中进行避障路径规划时解决了局部极小值的问题,同时实现了6自由度机器人末端在避障时的轨迹曲线平滑无振荡,验证了所提出避障路径规划方法的可行性。

多移动机器人混合避障算法的编队策略

针对多移动机器人系统在未知静态障碍物环境下的编队避障问题,提出了一种多移动机器人混合避障算法的编队策略,使多移动机器人系统在整个运行过程中保证系统内不发生碰撞,并且在未知静态障碍物环境中能最大程度地保持队形进行有效避障,以及能够在较短时间到达指定目标点。该编队策略基于领航跟随法和人工势场法,将系统内机器人划分为领航机器人及跟随机器人,并根据各自角色任务的差异,对其采用了不同改进方法的人工势场法进行避障,形成一种混合避障算法,其中针对领航机器人提出了LAPF(leader artificial potential field)避障算法,该算法改进了传统人工势场法的斥力函数,解决了传统人工势场法极易陷入局部极值困境的问题,并有效缩短了避障过程所用时间。为保证整个系统运行过程及避障行为之后能够恢复队形保持系统稳定性,该编队策略利用一致性模型控制机器人的速度,使领航机器人与跟随机器人的状态趋于一致,进而保持队形。仿真结果验证了LAPF算法及该多移动机器人混合避障算法编队策略的有效性。

基于领航-跟随法和人工势场法的巡检机器人编队

机器人编队技术正不断发展并逐步投入应用,为了提升巡检机器人的工作效率,提出了一种基于领航-跟随法和人工势场法的编队控制方法,保留了两方法的优点,使机器人编队在躲避障碍物的同时尽可能保持队形,通过施加随机扰动的方法改善人工势场法中容易出现的局部最小值问题。实验结果表明,提出的方法完成任务消耗的时间相较于领航-跟随法和基于行为法分别下降了28.5%和41.4%,在执行特定任务如协同围捕和依次排开时采用该方法的编队也能优秀完成。通过ROS(robot operating system)平台进行模拟环境仿真,进一步验证了方法的可行性。

移动机器人避障路径多激光传感器融合规划技术

为控制移动机器人成功躲避静、动障碍物,安全快速运行至目的地,研究移动机器人避障路径多激光传感器融合规划技术。此技术在机器人身上安装2个激光雷达传感器,感知机器人所处环境障碍信息,由基于多激光传感器融合的障碍物定位方法,将2个激光雷达传感器感知信息加权融合,提取静态障碍物位置数据后,使用基于动态避障的机器人路径规划方法,改进A∗算法结合静态障碍物位置数据,规划机器人避障的全局避障路径;考虑到全局避障路径中会有可能出现动态障碍物,由人工势场法,计算动态障碍物与目的地对机器人的斥力、引力、合力以及合力方向,调节机器人在全局避障路径中,动态避障的运行方向。实验中,本方法下对障碍物定位偏差在0.1 m内,此技术对静、动障碍物的避障能力合格,运行至目的地时,与障碍物0次碰撞,规划路径和运行时间较短,分别为158 m和9.2 min,效率较高。

基于改进人工势场法的医疗配送机器人路径规划

随着科技的高速发展和近几年新冠疫情的影响,医疗配送机器人开始逐步出现在各大医疗机构中,然而传统医疗配送机器人在使用人工势场算法进行路径规划时存在局部最优解和目标不可达问题。因此,针对局部最优解问题,提出了设计虚拟目标点的方法,将机器人从局部最优状态解救出来;针对目标不可达问题,在障碍物斥力势场函数中引入了目标距离函数对障碍物斥力进行限制,从而解决目标不可达问题。最后将该方法在多种复杂环境中与传统算法进行比较验证,实验结果表明,该改进算法能够解决传统算法存在的局部最优解和目标不可达问题,在算法效率上也提高了5%~9%,并且能够有效运用于实际场景。

水下机器人移动式对接的全局路径规划方法

针对无人自主式水下机器人与母艇动态对接时面临的自主式水下机器人欠驱动操纵特性对全局路径的多几何约束要求,以及母艇运动带来的交会点动态变化导致路径的终端约束动态变化等难题,本文提出了一种自主式水下机器人的自主对接路径规划方法。该方法主要分为约束求解、运动规划和状态估计3部分。利用自主式水下机器人的动力学模型分析了在对接过程中的运动约束与终端约束;在运动约束和终端约束条件的前提下,基于势场法的思想分别设计了位置约束和姿态约束下的全局路径规划算法,同时提出母艇的位置、速度和姿态估计方法,并依此开展基于全局预测的路径规划方法的仿真。仿真结果表明:本文方法运算速度快、对母艇的状态具有很好的鲁棒性,并且满足运动约束和终端约束,可以在试验模拟真实自主式水下机器人归航时得到一条满足约束条件的光滑路径的路径规划算法。