Study on Robot Path Planning Based on an Improved Artificial Potential Field Method

The method of artificial potential field has obvious advantages among the robot path planning methods including simple structure, small amount of calculation and relatively mature in theory. This paper puts forward the ＂Integral method＂ focusing on solving the problem of local minimization. The method analyses the distribution of obstructions in a given environment and regards adjacent obstacles as a whole, By changing the parameters of the repulsive force field, robots can quickly get out of the minimum point and move to the target point. This paper uses the Simurosot platform to carry on the simulation experiment on the improved artificial potential field method, which projects a feasible path successfully and verifies this method.     

Influence of Data Clouds Fusion From 3D Real-Time Vision System on Robotic Group Dead Reckoning in Unknown Terrain

This paper proposes the solution of tasks set required for autonomous robotic group behavior optimization during the mission on a distributed area in a cluttered hazardous terrain.The navigation scheme uses the benefits of the original real-time technical vision system(TVS)based on a dynamic triangulation principle.The method uses TVS output data with fuzzy logic rules processing for resolution stabilization.Based on previous researches,the dynamic communication network model is modified to implement the propagation of information with a feedback method for more stable data exchange inside the robotic group.According to the comparative analysis of approximation methods,in this paper authors are proposing to use two-steps post-processing path planning aiming to get a smooth and energy-saving trajectory.The article provides a wide range of studies and computational experiment results for different scenarios for evaluation of common cloud point influence on robotic motion planning.     

Sub-optimality analysis of mobile robot rolling path planning

Rolling planning is an efficient method for path planning in uncertain environment. In this paper, the general principle and algorithm of mobile robot path planning based on rolling windows are studied. The sub-optimality of rolling path planning is analyzed in details and explained with a concrete example.     

Fuzzy Identification Based on Tire/Road Adhesion Feature

For identifying the tire/road friction coefficient accurately in real-time to meet the needs of automobile electronic control system and then improving the active safety performance of automobile, the road recognition method based on fuzzy control algorithm was studied in this paper. Adopt a 7-DOF vehicle dynamic model, wheel slip ratio λ and longitudinal forces Fx as the input of fuzzy controller with fuzzy rules was proposed. The output is the weight coefficient of μ-λ functional expression which is related to c1, c2 and c3 proposed by Burckhardt etc. By a simulation experiment of automobile brake on the condition of driving straight or veering on a single road and docking pavement, to some extent, indicates that this method is able to guarantee the real-time and accuracy of the road identification.     

A new path planning method for bevel-tip flexible needle insertion in 3D space with multiple targets and obstacles

In this paper, a new bevel-tip flexible needle path planning method based on the bee-foraging learning particle swarm optimization (BFL-PSO) algorithm and the needle retraction strategy in 3D space is proposed to improve the puncture accuracy and shorten the puncture distance in the case of multiple puncture targets. First, the movement of the needle after penetrating the human body is analyzed, and the objective function which includes puncture path error, puncture path length, and collision function is established. Then, the BFL-PSO algorithm and the needle retraction strategy are analyzed. Finally, medical images of the tissue to be punctured are obtained by medical imaging instruments, i.e., magnetic resonance (MR), and the 3D model of the punctured environment is constructed by 3D Slicer to obtain the environment information on targets and obstacles, and the path of flexible needle is carried out based on the BFL-PSO optimization algorithm and the needle retraction strategy. The simulation results show that, compared with other path planning methods in the related literature, the new path planning method proposed in this paper has higher path planning accuracy, shorter puncture distance, and good adaptability to multi-target path planning problems.     

Fuzzy Logic Based Behavior Fusion for Navigation of an Intelligent Mobile Robot

This paper presents a new method for behavior fusion control of a mobile robot in uncertain environments.Using behavior fusion by fuzzy logic,a mobile robot is able to directly execute its motion according to range information about environments,acquired by ultrasonic sensors,without the need for trajectory planning.Based on low-level behavior control,an efficient strategy for integrating high-level global planning for robot motion can be formulated,since,in most applications,some information on environments is prior knowledge.A global planner,therefore,only to generate some subgoal positions rather than exact geometric paths.Because such subgoals can be easily removed from or added into the plannes,this strategy reduces computational time for global planning and is flexible for replanning in dynamic environments.Simulation results demonstrate that the proposed strategy can be applied to robot motion in complex and dynamic environments.     

一类欠驱动机械系统的动态及其稳定控制

Abstract The control of underactuated mechanical systems is very complex for the loss of its control inputs. The model of underactuated mechanical systems in a potential field is built with Lagrangian method and its structural properties are analyzed in detail. A stable control approach is proposed for the class of underactuated mechanical systems. This approach is applied to an unde ractuated double-pendulum-type overhead crane and the simulation results illustrate the correctness of dynamics analysis and validity of the proposed control algorithm.     

Automated postoperative blood pressure control

It is very important to maintain the level of mean arterial pressure （MAP）. The MAP control is applied in many clinical situations, including limiting bleeding during cardiac surgery and promoting healing for patient＇ s post-surgery. This paper presents a fuzzy controller-based multiple-model adaptive control system for postoperative blood pressure management. Multiple-model adaptive control （MMAC） algorithm is used to identify the patient model, and it is a feasible system identification method even in the presence of large noise. Fuzzy control （FC） method is used to design controller bank. Each fuzzy controller in the controller bank is in fact a nonlinear proportional-integral （PI） controller,whose proportional gain and integral gain are adjusted continuously according to error and rate of change of error of the plant output, resulting in better dynamic and stable control performance than the regular PI controller, especially when a nonlinear process is involved. For demonstration, a nonlinear, pulsatile-flow patient model is used for simulation, and the results show that the adaptive control system can effectively handle the changes in patient＇s dynamics and provide satisfactory performance in regulation of blood pressure of hypertension patients.     

Local minima-free design of artificial coordinating fields

In order to overcome the drawbacks of conventional artificial potential fields (APF) based methods for the motion planning problems of mobile robots in dynamic uncertain environments, an aruficial coordinating fields (ACF) based method has been proposed recently. This paper deals with the reachability problem of the ACF, that is, how to design and choose the parameters of the ACF and how the environment should be such that the robot can reach its goal without being trapped in local minima. Some sufficient conditions for these purposes are developed theoretically. Theoretical analyses show that, the ACF can effectively remove local minima in dynamic uncertain environments with V-shape or U-shape obstacles, and guide the mobile robot to reach its goal with some necessary environment constraints and based on the methods provided in this paper to properly choose the parameters of the ACF. Comparisons between the ACF and APF, and simulations are provided to illustrate the advantages of the ACF.     

A Multi-pipe Path Planning by Modified Ant Colony Optimization

Path planning in 3D geometry space is used to find an optimal path in the restricted environment,according to a certain evaluation criteria.To solve the problem of long searching time and slow solving speed in 3D path planning,a modified ant colony optimization is proposed in this paper.Firstly,the grid method for environment modeling is adopted.Heuristic information is connected with the planning space.A semi-iterative global pheromone update mechanism is proposed.Secondly,the optimal ants mutate the paths to improve the diversity of the algorithm after a defined iterative number.Thirdly,co-evolutionary algorithm is used.Finally,the simulation result shows the effectiveness of the proposed algorithm in solving the problem of 3D pipe path planning.     

改进人工势场法的移动机器人避障轨迹研究

针对传统人工势场法在多障碍物复杂环境的全局路径规划中出现的目标不可达、易陷入陷阱区域以及局部极小点问题,提出一种简化障碍物预测碰撞人工势场法（simplified obstacles and predict collision of artificial potential field method,SOPC-APF）,算法引入预测碰撞思想,在机器人未进入陷阱区域或者极小点问题前做出决策;对于多障碍物的斥力与目标点的引力产生的合力使机器人陷入震荡,提出简化障碍物,即简化为影响范围内目标点一侧的受限障碍物;针对目标不可达问题,在碰撞预测基础上,设定虚拟目标点,经改进的斥力函数引导机器人快速生成一条平滑、平稳、无碰撞的路径。通过与传统算法、改进APF算法以及改进蚁群算法的仿真对比实验表明,SOPC-APF有效解决了人工势场法不适用于多障碍物复杂环境的问题,以及传统算法容易陷入陷阱区域和局部极小点问题。     

改进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.分析了传统的人工势场法由于局部最小问题而导致规划失败的原因.提出了一种改进的势场函数,并对改进势场函数的规划方法进行分析,发现该方法并不能完全解决局部极小问题.通过在改进势场函数基础上采用添加附加控制力的方法,使机器人尽快跳出局部极小点.仿真结果表明,该方法是有效的.     

基于动态模糊人工势场法的移动机器人路径规划

传统人工势场法在路径规划中存在局部极小值问题,而且不能满足动态环境中移动机器人路径规划对实时性、安全性和可达性的要求.针对传统人工势场法存在的问题,通过引入速度矢量,改势场力函数,并与模糊控制方法相结合,实时调节斥力势场系数,克服人工势场法的缺陷.在MATLAB平台中验证了方法的有效性,实验结果表明,该方法优于人工势场法模型的路径规划.     

UAV path planning using artificial potential field method updated by optimal control theory

The unmanned aerial vehicle (UAV) path planning problem is an important assignment in the UAV mission planning. Based on the artificial potential field (APF) UAV path planning method, it is reconstructed into the constrained optimisation problem by introducing an additional control force. The constrained optimisation problem is translated into the unconstrained optimisation problem with the help of slack variables in this paper. The functional optimisation method is applied to reform this problem into an optimal control problem. The whole transformation process is deduced in detail, based on a discrete UAV dynamic model. Then, the path planning problem is solved with the help of the optimal control method. The path following process based on the six degrees of freedom simulation model of the quadrotor helicopters is introduced to verify the practicability of this method. Finally, the simulation results show that the improved method is more effective in planning path. In the planning space, the length of the calculated path is shorter and smoother than that using traditional APF method. In addition, the improved method can solve the dead point problem effectively.     

移动机器人未知环境避障研究

针对移动机器人的避障问题,以AS-R移动机器人为实验平台,提出了一种改进人工势场和模糊逻辑相结合的路径规划方法.对于未知障碍物环境采用人工势场法进行实时路径规划,对于动态近距离动态障碍物采用模糊逻辑方法引导机器人做出避障行为.为了有效将2种方法结合,根据传感器信息对于人工势场方法引入转角的信任度,机器人运行方向由上述2...     

非完整移动机器人的人工势场法路径规划

基于人工势场的移动机器人路径规划方法在最近20多年里受到了广泛关注.然而研究者主要将目光集中于解决其各种理论问题,在研究中大都将机器人看作无约束的质点或刚体,通常无法直接应用于受到非完整约束限制的轮式移动机器人.针对人工势场法在轮式移动机器人上的实现问题,本文对两种已有实现方法进行了理论分析,指出其存在目标不可达的隐患和无法在不同环境下兼顾路径规划性能的问题,并提出一种基于模糊规则的新方法,通过在不同的情况下调整控制方式和参数解决前述问题.仿真研究表明,该方法在保证目标可达的前提下能够在多种环境中获得更好的总体规划性能.     

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。     

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,智能体构建了以目标点为中心的引力势场,以及以障碍物为中心的斥力势场,在智能体行进路途中感知局部障碍及目标点的运动信息,并且将信息加入势场函数的计算中达到动态避障与追踪的效果;另一方面,引入距离因子及动态临时目标点来消除APF算法常见的无解问题——极小解情况及路径抖动现象。通过建立不同数量的随机障碍场景,进行多次仿真对比实验,结果表明:所提算法能够在未知环境中灵活躲避动态障碍并进行目标点的追踪,可以有效消除死解及路径抖动问题。将所提算法与传统APF算法及添加了动态避障机制的文献[19]所述算法进行对比实验,结果表明所提算法能成功化解两种对比算法路径规划失败的情况,顺利完成路径规划任务,且成功率在95%以上。     

POMDP-APF：一种基于POMDP模型的APF无人机路径规划策略

针对无人机在路径规划过程中会遇到静态或者动态的障碍物,从而导致路径规划失败的问题,提出一种基于部分可观测马尔可夫决策过程（partially observable markov decision process,POMDP）模型的人工势场（artificial potential field,APF）无人机路径规划策略（POMDP-APF）。首先使用传感器获得的障碍物信息结合POMDP模型预测障碍物的未来位置,为无人机的路径规划做准备;其次,提出一种新的基于障碍物的正方体外接球的模型,保障无人机在路径规划过程中的安全性;最后,结合改进的APF算法实现无人机的路径规划。仿真结果表明,POMDP-APF策略在无人机实时路径规划中具有良好的可行性和有效性,使无人机能够有效避开障碍物,同时路径长度以及耗费时间更短。     

基于模糊人工势场法的智能全向车路径规划

针对于移动机器人在传统人工势场法路径规划中易于陷入局部最小点而无法抵达目标点的问题,同时考虑到实际环境中人工势场法相关参数的不确定性,提出了一种基于模糊人工势场法的动态路径规划方法。借助于专家经验进行模糊决策,调整移动机器人在各个时刻的合力大小和方向,进而解决斥力常数、引力方向偏角以及机器人行驶速度的不确定性问题。为了验证该方法的有效性,在智能全向车平台进行了应用,结果表明,智能全向车运动轨迹平滑,避免了实际应用中的震荡问题。