Decentralized Control of Autonomous Swarm Systems Using Artificial Potential Functions: Analytical Design Guidelines

This paper presents a framework for decentralized control of self-organizing swarm systems based on the artificial potential functions (APFs). In this scheme, multiple agents in a swarm self-organize to flock and achieve formation control through attractive and repulsive forces among themselves using APFs. In particular, this paper presents a set of analytical guidelines for designing potential functions to avoid local minima for a number of representative scenarios. Specifically the following cases are addressed: 1) A non-reachable goal problem (a case that the potential of the goal is overwhelmed by the potential of an obstacle, 2) an obstacle collision problem (a case that the potential of the obstacle is overwhelmed by the potential of the goal), 3) an obstacle collision problem in swarm (a case that the potential of the obstacle is overwhelmed by potential of other robots in a group formation) and 4) an inter-robot collision problem (a case that the potential of the robot in a formation is overwhelmed by potential of the goal). The simulation results showed that the proposed scheme can effectively construct a self-organized swarm system with the capability of group formation, navigation and migration in the presence of obstacles.Category (5) – Intelligent Systems/Intelligent Control/Fuzzy Control/Prosthetics/Robot Motion Planning     

New repulsive potential functions with angle distributions for local path planning

This paper presents new repulsive potential functions (RPFs) for point robot path planning. In this scheme, the RPF for path planning has a different magnitude at each direction of a RPF based on the angle between a goal and an obstacle, unlike a conventional RPF in which the same magnitude at each direction is obtained. In doing so, the RPF attempts to overcome some of the typical problems that may arise with the conventional RPF. In particular, this paper presents a set of analyses for designing potential functions to avoid local minima for a number of representative scenarios. Specifically, the following cases are addressed: (i) a non-reachable goal problem (a case in which the potential of the goal is overwhelmed by the potential of an obstacle), (ii) an obstacle collision problem (a case in which the potential of the obstacle is overwhelmed by the potential of the goal) and (iii) a narrow passage problem (a case in which the potential of the goal is overwhelmed by the potential of two obstacles). The proposed RPF scheme eliminates the non-feasible area for the three cases by the help of an angle-varying magnitude between a goal and an obstacle. The example results show that the proposed RPF scheme can effectively construct a path-planning system with the capability of reaching a goal and avoiding obstacles despite possible local minima.     

基于APF的AGV局部路径规划改进算法研究

针对原有人工势场法（artificial potential field,APF）在局部路径规划时的避障效果不良问题,提出一种APF-PSO的改进算法改善原算法优化路径规划的效果。将速度势场引入位置势场中使AGV(automated guided vehicle)动态避开不同速度的移动障碍物;当算法陷入局部最小值时,采取PSO(particle swarm optimization)算法,并对其惯性权重因子和学习因子做出调整,通过三次样条曲线插值来平滑路径,使得AGV找到最短路径。结果表明APFPSO改进算法可根据障碍物速度不同动态避障,解决了APF算法运算中避障效果不良问题。     

Escaping route method for a trap situation in local path planning

This paper introduces a new framework for escaping from a local minimum in path planning based on artificial potential functions (APFs). In particular, this paper presents a set of analytical guidelines for designing potential functions to avoid local minima in a trap situation (in this case, the robot is trapped in a local minimum by the potential of obstacles). The virtual escaping route method is proposed to allow a robot to escape from a local minimum in a trap situation where the total forces are composed of repulsive forces by obstacles and attractive force by a goal are zero. The example results show that the proposed scheme can effectively construct a path planning system with the capability of reaching a goal and avoiding obstacles, despite a trapped situation under possible local minima. Recommended by Editorial Board member Sooyong Lee under the direction of Editor Jae-Bok Song. This work was supported by the Kyungnam University Foundation Grant, 2007. Dong Hun Kim received the B.S., M.S. and Ph.D. degrees from the Department of Electrical Engineering, Hanyang University, Korea, in 1995, 1997 and 2001, respectively. From 2001 to 2003, he was a Research Associate under several grants in the Department of Electrical and Computer Engineering, Duke University, NC, USA. In 2003, he joined Boston University, MA, USA, as a Visiting Assistant Professor under several grants in the Department of Aerospace and Mechanical Engineering. In 2004, he was engaged in postdoctoral research at the School of Information Science and Technology, the University of Tokyo, Japan. Since 2005, he has been an Associate Professor with the Department of Electrical Engineering, Kyungnam University, Korea. His research interests include swarm intelligence, self-organization of swarm systems, mobile robot path planning, decentralized control of autonomous vehicles, intelligent control, and adaptive nonlinear control.     

面向未知环境及动态障碍的人工势场路径规划算法

实际战场环境错综复杂,很多隐蔽、动态的障碍无法通过高空手段预先探测得知,因而对智能体执行任务的安全性产生威胁.针对未知且障碍形态多样的战场环境,以躲避动、静障碍,追踪目标为研究对象,提出一种面向未知环境及动态障碍的改进人工势场(Artificial Potential Field,APF)路径规划算法.在该算法中,智能...     

基于人工势场法的移动机器人路径规划研究

人工势场法是机器人局部路径规划常用的一种方法,具有反应速度快、计算量小和实时性等优点。但这种方法容易产生局部极点,导致机器人停止移动,达不到目标。文章利用传统的人工势场法对移动机器人避障行为进行了仿真实验并成功地规划出一条光滑路径,并对人工势场法研究现状进行了分析讨论。     

Robust neuro-fuzzy sensor-based motion control among dynamic obstacles for robot manipulators

A new robust neuro-fuzzy controller for autonomous and intelligent robot manipulators in dynamic and partially known environments containing moving obstacles is presented. The navigation is based on a fuzzy technique for the idea of artificial potential fields (APFs) using analytic harmonic functions. Unlike the fuzzy technique, the development of APFs is computationally intensive. A computationally efficient processing scheme for fuzzy navigation to reasoning about obstacle avoidance using APF is described, namely, the intelligent dynamic motion planning. An integration of a robust controller and a modified Elman neural networks (MENNs) approximation-based computed-torque controller is proposed to deal with unmodeled bounded disturbances and/or unstructured unmodeled dynamics of the robot arm. The MENN weights are tuned online, with no off-line learning phase required. The stability of the overall closed-loop system, composed by the nonlinear robot dynamics and the robust neuro-fuzzy controller, is guaranteed by the Lyapunov theory. The purpose of the robust neuro-fuzzy controller is to generate the commands for the servo-systems of the robot so it may choose its way to its goal autonomously, while reacting in real-time to unexpected events. The proposed scheme has been successfully tested. The controller also demonstrates remarkable performance in adaptation to changes in manipulator dynamics. Sensor-based motion control is an essential feature for dealing with model uncertainties and unexpected obstacles in real-time world systems.     

New technique of mobile robot navigation using a hybrid adaptive fuzzy potential field approach

This paper presents a summary of the research aimed at developing a new reliable methodology for robot navigation and obstacle avoidance. This new approach is based on the artificial potential field (APF) method, which is used extensively for obstacle avoidance. The classical APF is dependent only on the separation distance between the robot and the surrounding obstacles. The new scheme introduces a variable, which is used to determine the importance that each obstacle has on the robot's future path. The importance variable is dependent on the obstacles position, both angle and distance, with respect to the robot. Simulation results are presented demonstrating the ability of the algorithm to perform successfully in simple environments.     

基于人工力场的移动机器人路径规划研究

针对传统人工势场法中存在的一些局部极小点问题,文中提出了一种基于人工力场的移动机器人路径规划方法.该方法将机器人与目标的相对距离引入斥力函数,定义斥力的一个分力方向与障碍物的影响范围相切,另一个分力方向与引力方向一致,规定斥力向量与引力向量的内积始终大于等于零.这样,克服了传统人工势场法中存在的一些局部极小点问题,尤其是三个典型的、具有代表性的局部极小点问题.MATLAB仿真结果表明该人工力场法可以避开上述局部极小点,有效的规划出从起点到目标点的无碰路径.     

基于模型预测控制与改进人工势场法的多无人机路径规划

模型预测控制(model predictive control,MPC)已成功地应用于无人机集群的路径规划.但其存在计算量大及单步运算时间长等不足,在实时运行中往往难以获得较高的控制频率.而离线的MPC需要准确的地图信息,难以处理地图中无法预测的动态障碍物.对此,提出一种结合离线MPC全局规划与在线改进人工势场法局部规划的方法.在利用MPC方法生成安全、平滑轨迹的同时,提高无人机在动态障碍物影响下的避障能力.通过引入调节力来处理传统人工势场法的局部极小值问题,并将目标与无人机的相对距离引入斥力函数,同时改进引力函数,以改善无人机在目标点处低速徘徊的问题.此外,设计一种事件触发的无人机轨迹变更与轨迹恢复策略,使无人机仅在必要时实施动态避障行为.在此基础上,最大化利用原来的规划轨迹.仿真验证结果表明,所提出的路径规划方法能够使无人机集群安全飞行至目标点,并且具有良好的动态避障能力.     

基于遗传算法和B 样条曲线的平滑避障路径规划

传统的避障路径规划中常常存在不连续点,提出一种新的平滑避障路径规划的方法—遗传算法和B样条曲线法。首先,先通过碰撞侦测,能够侦测出前进路径中的障碍物,然后通过遗传算法再结合B样条曲线规划出平滑的避障路径。该算法可以避免运动过程因打滑而造成与目标位置的误差,解决机器人在静态环境中全局、局部路径规划和规划路径中存在不连续点的问题。仿真和实验结果证明了算法的有效性。     

复杂环境下改进APF的机器人路径规划

针对移动机器人在有大型障碍物和运动空间相对狭窄的复杂环境中,人工势场法（APF）容易出现反复震荡、路径规划时间较长以及大型障碍物附近避障困难的问题,提出了在结合边缘探测法的APF路径规划基础上,加入自适应动态步长调整算法来克服APF的上述缺陷,实现移动机器人在复杂环境下的平滑路径规划,在确保路径近似最优的同时提高APF算法的收敛速度和路经规划的避障性能。实验结果证明了上述方法的有效性。     

Path planners based on the wave expansion neural network

In the potential field approach to path planning, the development of the artificial potential field (APF) is a computationally intensive operation. The realization that the APFs can be developed by parallel distributed techniques has prompted interest in using neural networks for developing APFs. This paper describes a neural network called the wave expansion neural network (WENN) and shows that it is capable of developing a variety of APFs that are useful for path planning. The discretized environment including information about the target configuration (position and orientations) and the obstacles are applied to the WENN as input. Activity is then propagated in the form of waves throughout the WENN neural field and at equilibrium, the resulting neural activity distribution forms the desired APF. We analyze the computational complexity of the WENN based APF generation and compare it with conventional development of APFs. We also describe different path planners which use these APFs to plan paths for moving objects with two and three degrees of freedom.     

一种新基于混沌优化算法的机器人路径规划方法

提出了一种基于混沌优化算法的机器人路径规划方法,即混沌人工势场法，该方法能够在动态环境下实时、有效地产生避碰局部最优路径,避免了传统人工势场法容易陷入局部最优和在比较靠近的两个障碍物之间找不到通道的缺陷.仿真试验表明:提出的方法具有较强的路径规划能力，克服了传统人工势场法的缺点,具有较强的实用性.     

Velocity potential approach to path planning for avoiding moving obstacles

This paper describes the theory and an experiment of a velocity potential approach to path planning and avoiding moving obstacles for an autonomous mobile robot by use of the Laplace potential. This new navigation function for path planning is feasible for guiding a mobile robot avoiding arbitrarily moving obstacles and reaching the goal in real time. The essential feature of the navigation function comes from the introduction of fluid flow dynamics into the path planning. The experiment is conducted to verify the effectiveness of the navigation function for obstacle avoidance in a real world. Two examples of the experiment are presented; first, the avoidance of a moving obstacle in parallel line-bounded space, and second, the avoidance of one moving obstacle and another standing obstacle. The robot can reach the goal after successfully avoiding the obstacles in these cases.     

Obstacle Avoidance for Spatial Hyper‐Redundant Manipulators Using Harmonic Potential Functions and the Mode Shape Technique

This paper deals with the obstacle avoidance problem for spatial hyper‐redundant manipulators in known environments. The manipulator is divided into two sections, a proximal section that has not entered the space among the obstacles and a distal section among the obstacles. Harmonic potential functions are employed to achieve obstacle avoidance for the distal section in three‐dimensional space in order to avoid local minima in cluttered environments. A modified panel method is used to generate the potential of any arbitrary shaped obstacle in three‐dimensional space. An alternative backbone curve concept and an efficient fitting method are introduced to control the trajectory of proximal links. The fitting method is recursive and avoids the complications involved with solving large systems of nonlinear algebraic equations. The combination of a three‐dimensional safe path derived from the harmonic potential field and the backbone curve concept leads to an elegant kinematic control strategy that guarantees obstacle avoidance. © 2003 Wiley Periodicals, Inc.     

固定翼无人机在线航迹规划方法

在不确定环境下,针对固定翼无人机（UAV）航迹规划问题,提出了一种基于滚动时域控制的模糊粒子群优化算法与改进人工势场法相结合的在线航迹规划方法。首先,对凸多边形障碍物进行最小外接圆拟合;然后,根据静态威胁,将规划问题转化为一系列时域窗口内的在线子问题,利用模糊粒子群算法实时优化求解以实现静态避障;当环境中存在动态威胁时,使用改进人工势场法对航迹进行调整完成动态避障。为了满足固定翼无人机的动态约束,同时提出固定翼UAV的碰撞检测法,可提前判断障碍物是否为真正威胁源,以此减少转弯频率和幅度,降低飞行代价。仿真实验结果表明,所提方法在固定翼UAV航迹规划中能有效提升规划速度、稳定性与实时避障能力,且克服了传统人工势场容易陷入局部最优的缺点。     

A vision-based collision avoidance technique for micro air vehicles using local-level frame mapping and path planning

This paper presents a vision-based collision avoidance technique for small and miniature air vehicles (MAVs) using local-level frame mapping and path planning. Using computer vision algorithms, a depth map that represents the range and bearing to obstacles is obtained. Based on the depth map, we estimate the range, azimuth to, and height of obstacles using an extended Kalman filter that takes into account the correlations between obstacles. We then construct maps in the local-level frame using cylindrical coordinates for three dimensional path planning and plan Dubins paths using the rapidly-exploring random tree algorithm. The behavior of our approach is analyzed and the characteristics of the environments where the local path planning technique guarantees collision-free paths and maneuvers the MAV to a specific goal region are described. Numerical results show the proposed technique is successful in solving path planning and multiple obstacle avoidance problems for fixed wing MAVs.     

Avoiding the local-minimum problem in multi-agent systems with limited sensing and communication

In this paper, we consider a control problem for nonholonomic multi-agent systems in which agents and obstacles operate within a circular-shaped work area. We assume that agents only have limited sensing and communication ranges. We propose a novel control scheme using potential functions that drives agents from the initial to the goal configuration while avoiding collision with other agents, obstacles, and the boundary of the work area. The control scheme employs an avoidance strategy that ensures that the agents are never trapped at local minima that are typically encountered with most potential function-based approaches. A numerical simulation is presented to demonstrate the validity and effectiveness of the proposed control scheme.     

实现机器人动态路径规划的仿真系统

针对机器人动态路径规划问题,提出了在动态环境中移动机器人的一种路径规划方法,适用于环境中同时存在已知和未知,静止和运动障碍物的复杂情况。采用栅格法建立机器人空间模型,整个系统由全局路径规划和局部避碰规划两部分组成。在全局路径规划中,用快速搜索随机树算法规划出初步全局优化路径,局部避碰规划是在全局优化路径的同时,通过基于滚动窗口的环境探测和碰撞规则,对动态障碍物实施有效的局部避碰策略,从而使机器人安全顺利地到达目的地。仿真实验结果说明该方法具有可行性。