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。

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。

Cooperative Search of UAV Swarm Based on Ant Colony Optimization with Artificial Potential Field

An ant colony optimization with artificial potential field(ACOAPF)algorithm is proposed to solve the cooperative search mission planning problem of unmanned aerial vehicle(UAV)swarm.This algorithm adopts a distributed architecture where each UAV is considered as an ant and makes decision autonomously.At each decision step,the ants choose the next gird according to the state transition rule and update its own artificial potential field and pheromone map based on the current search results.Through iterations of this process,the cooperative search of UAV swarm for mission area is realized.The state transition rule is divided into two types.If the artificial potential force is larger than a threshold,the deterministic transition rule is adopted,otherwise a heuristic transition rule is used.The deterministic transition rule can ensure UAVs to avoid the threat or approach the target quickly.And the heuristics transition rule considering the pheromone and heuristic information ensures the continuous search of area with the goal of covering more unknown area and finding more targets.Finally,simulations are carried out to verify the effectiveness of the proposed ACOAPF algorithm for cooperative search mission of UAV swarm.

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.

基于双向A^(*)-APF算法的船舶路径规划研究

为解决目前船舶路径规划算法中存在的全局非最优解和局部最优解等问题,在双向A^(*)算法的基础上加入了优化函数PathOptimization和IsClear,以去除冗余拐点,缩短全局路径距离;在人工势场法(artificial potential field,APF)的基础上,设定离散化步长函数、斥力感应阈值和临时终点,以避免局部最优解和震荡问题;实现两种算法的融合算法(双向A^(*)-APF算法),在MATLAB模拟的相同栅格图中,对比算法改进前后的模拟试验数据。结果表明,融合算法平均减少了50%的冗余拐点,平均减少了47.5%的算法搜索时间,平均缩短了7%的路径距离,能够同时安全规避动态障碍物和静态障碍物。研究表明,双向A^(*)-APF算法可用于解决船舶路径全局非最优解和局部最优解等问题。

Collision avoidance planning in multi-robot system based on improved artificial potential field and rules

For real-time and distributed features of multi-robot system,the strategy of combining the improved artificial potential field method and the rules based on priority is proposed to study the collision avoidance planning in multi-robot systems. The improved artificial potential field based on simulated annealing algorithm satisfactorily overcomes the drawbacks of traditional artificial potential field method,so that robots can find a local collision-free path in the complex environment. According to the movement vector trail of robots,collisions between robots can be detected,thereby the collision avoidance rules can be obtained. Coordination between robots by the priority based rules improves the real-time property of multi-robot system. The combination of these two methods can help a robot to find a collision-free path from a starting point to the goal quickly in an environment with many obstacles. The feasibility of the proposed method is validated in the VC-based simulated environment.

改进APF的无人机编队避障最优一致性控制方法

针对传统人工势场(Artificial Potential Field,APF)解决避障问题时出现的局部极小值、目标不可达等缺点,提出了一种结合APF和具有协同避障效果的最优一致性控制方法。基于固定无向通信拓扑的双积分器无人机编队模型,引入具有避障代价函数的最优一致性控制协议,解决APF避障的局限性问题,同时对多无人机进行编队控制,使无人机编队控制系统的一致性性能指标、控制消耗性能指标和避障性能指标达到最优解。此外,通过对每架无人机构建虚拟斥力势场,防止在避障过程中出现机间碰撞。仿真结果表明,与改进APF的非最优一致性控制相比,本文提出的改进APF的最优一致性控制能够缩短任务用时32%,且能够极大程度上保持队形完整性,减少避障所造成的一致性消耗和控制损耗。

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APF-RRT的6R机械臂避障路径规划

针对6R机械臂在复杂环境下进行避障路径规划时成功率低、效率低等问题,提出一种改进人工势场法(APF)与快速扩展随机树法(RRT)的融合算法。对于传统APF目标不可达问题,提出引入斥力调节因子优化斥力函数,使得机械臂靠近目标点时,障碍物对机械臂的斥力逐渐减小并顺利到达目标点;针对传统RRT算法随机性过强问题,提出目标导向策略进行优化,使得采样点有一定的概率向目标点扩展;当APF陷入局部最优时,采用改进RRT算法进行路径规划,当跳出局部最优时,切换为APF继续路径规划。仿真结果表明:改进APF-RRT算法能适应各种复杂环境,且相较于传统APF和RRT算法具有规划时间短、规划成功率高等优点,有效解决了APF目标不可达和局部最小值的问题。最后通过JAKA机器人实验平台进行实际环境实验,验证了改进APF-RRT融合算法的可行性。

APF与A*融合的多目标点路径规划算法

在智能餐厅环境下,针对移动机器人在多目标点路径规划时存在规划效率不高的问题,提出了一种基于A*算法与APF算法相结合的多目标点路径规划的方法。将多目标点路径规划问题转化成类旅行商问题,采用A*算法和人工势场法规划出多目标点的最优遍历顺序。首先,将若干个目标点用一个集合表示并应用在A*算法上面,实现A*算法多个目标点的路径规划;其次,引入人工势场法对多个目标点进行优先级判定,借助人工势场法计算各个目标点的势能值,之后利用人工势场法得到的势能值对目标点集进行排序,完成各个目标点之间的最优顺序;最后,对规划好的目标点集使用A*算法进行全局路径规划。为了验证该方法的有效性和先进性,将该算法进行消融实验,同时也与两种典型的多目标点规划算法进行对比。结果表明,该算法是有效的,能够在缩短路径规划时间和降低路径代价的同时规划出一条有效路径。

Stability-Considered Lane Keeping Control of Commercial Vehicles Based on Improved APF Algorithm

Regarding the lane keeping system,path tracking accuracy and lateral stability at high speeds need to be taken into account especially for commercial vehicles due to the characteristics of larger mass,longer wheelbase and higher mass center.To improve the performance mentioned above comprehensively,the control strategy based on improved artificial potential field(APF)algorithm is proposed.In the paper,time to lane crossing(TLC)is introduced into the potential field function to enhance the accuracy of path tracking,meanwhile the vehicle dynamics parameters including yaw rate and lateral acceleration are chosen as the repulsive force field source.The lane keeping controller based on improved APF algorithm is designed and the stability of the control system is proved based on Lyapunov theory.In addition,adaptive inertial weight particle swarm optimization algorithm(AIWPSO)is applied to optimize the gain of each potential field function.The co-simulation results indicate that the comprehensive evaluation index respecting lane tracking accuracy and lateral stability is reduced remarkably.Finally,the proposed control strategy is verified by the HiL test.It provides a beneficial reference for dynamics control of commercial vehicles and enriches the theoretical development and practical application of artificial potential field method in the field of intelligent driving.

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.

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.

基于APF-LSTM-DDPG算法的移动机器人局部路径规划

针对深度强化学习算法存在训练时间长、收敛速度慢的问题,将深度确定性策略梯度(deep deterministic policy gradient,DDPG)算法和人工势场(artifical potential field,APF)法相融合,引入长短期记忆(long short-term memory,LSTM)神经网络结构,提出了APF-LSTM-DDPG算法。首先在DDPG算法中添加LSTM,通过记忆单元和遗忘单元将奖励较高的样本优先学习,使模型更快地收敛;其次引入人工势场法,设计相应势场函数,解决环境奖励稀疏的缺点,加速模型收敛进程;然后通过人工势场法修正算法的动作选择,缩短路径长度;最后在机器人仿真平台(robot operating system,ROS)中搭建不同障碍物环境对算法进行了仿真验证。仿真结果表明,APF-LSTM-DDPG算法在搭建的仿真环境中训练时平均奖励能够更快地稳定,提高了算法的成功率并减少了规划路径中的冗余。

一种改进的SA-APF路径规划算法

针对基于模拟退火的人工势场算法存在局部最小值问题,引入一种随机逃逸力,提出一种改进的SA-APF(Simulated Annealing-Artificial Potential Field)算法,可使机器人快速逃离局部最优解。针对算法中仍存在目标不可达问题,根据模糊控制方法提出一种自适应斥力势场增益系数,在机器人遇到目标点附近有对称排列的障碍物时,使机器人能够到达目标点,并采用4次贝赛尔曲线拟合,使路径更光滑。仿真结果表明,在简单和复杂的障碍物的实验条件下,改进后的算法可以准确、高效地完成路径规划。

基于改进APF算法的水面无人艇局部路径规划

为解决传统人工势场法(APF)进行水面无人艇路径规划时出现局部最小和路径不平滑的问题,对斥力系数进行动态调整并引入逃逸力。为使水面无人艇在动态避碰中按国际海上避碰规则(international regulations for preventing collisions at sea,COLREGS)航行,结合无人艇性能制定了无人艇的避碰规则,并引入转向力。通过Matlab对静态未知障碍物的仿真实验,验证改进APF算法能解决局部最小问题且规划的路径更加安全平滑。通过对动态未知障碍物4种典型局面的仿真实验表明,改进APF算法在复杂环境下也能引导无人艇在符合避碰规则的前提下对多个动态障碍进行安全规避。

改进的APF算法在采摘机械手运动规划中的应用

针对六连杆采摘机械手运动规划的实时性,基于传统的人工势场(APF)算法,提出改进的人工势场(IAPF)优化算法。采用初等变换序列方法对机械手进行运动学建模,运用该模型可方便地得到机械手上各点相对于基底坐标系的雅可比矩阵。IAPF算法在APF算法原有的引力势场计算模型的基础上,加入了Sigmoid函数,以使得路径规划算法收敛,在斥力势场中引入了反S的Sigmoid函数,防止出现斥力爆炸现象。IAPF算法避免了传统算法的局部最小缺陷和目标不可达问题,同时有效地减少机械手运动规划的计算时间和关节误差。并通过实验验证了所提优化算法在采摘机械手避障运动规划中的实时性和准确性。

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.