Distributed collaborative complete coverage path planning based on hybrid strategy

Collaborative coverage path planning(CCPP) refers to obtaining the shortest paths passing over all places except obstacles in a certain area or space. A multi-unmanned aerial vehicle(UAV) collaborative CCPP algorithm is proposed for the urban rescue search or military search in outdoor environment.Due to flexible control of small UAVs, it can be considered that all UAVs fly at the same altitude, that is, they perform search tasks on a two-dimensional plane. Based on the agents’ motion characteristics and environmental information, a mathematical model of CCPP problem is established. The minimum time for UAVs to complete the CCPP is the objective function, and complete coverage constraint, no-fly constraint, collision avoidance constraint, and communication constraint are considered. Four motion strategies and two communication strategies are designed. Then a distributed CCPP algorithm is designed based on hybrid strategies. Simulation results compared with patternbased genetic algorithm(PBGA) and random search method show that the proposed method has stronger real-time performance and better scalability and can complete the complete CCPP task more efficiently and stably.

An Improved Bounded Conflict-Based Search for Multi-AGV Pathfinding in Automated Container Terminals

As the number of automated guided vehicles(AGVs)within automated container terminals(ACT)continues to rise,conflicts have becomemore frequent.Addressing point and edge conflicts ofAGVs,amulti-AGVconflict-free path planning model has been formulated to minimize the total path length of AGVs between shore bridges and yards.For larger terminalmaps and complex environments,the grid method is employed to model AGVs’road networks.An improved bounded conflict-based search(IBCBS)algorithmtailored to ACT is proposed,leveraging the binary tree principle to resolve conflicts and employing focal search to expand the search range.Comparative experiments involving 60 AGVs indicate a reduction in computing time by 37.397%to 64.06%while maintaining the over cost within 1.019%.Numerical experiments validate the proposed algorithm’s efficacy in enhancing efficiency and ensuring solution quality.

Applications and Challenges of Deep Reinforcement Learning in Multi-robot Path Planning

With the rapid advancement of deep reinforcement learning(DRL)in multi-agent systems,a variety of practical application challenges and solutions in the direction of multi-agent deep reinforcement learning(MADRL)are surfacing.Path planning in a collision-free environment is essential for many robots to do tasks quickly and efficiently,and path planning for multiple robots using deep reinforcement learning is a new research area in the field of robotics and artificial intelligence.In this paper,we sort out the training methods for multi-robot path planning,as well as summarize the practical applications in the field of DRL-based multi-robot path planning based on the methods;finally,we suggest possible research directions for researchers.

Multi-Agent Path Planning Method Based on Improved Deep Q-Network in Dynamic Environments

The multi-agent path planning problem presents significant challenges in dynamic environments,primarily due to the ever-changing positions of obstacles and the complex interactions between agents’actions.These factors contribute to a tendency for the solution to converge slowly,and in some cases,diverge altogether.In addressing this issue,this paper introduces a novel approach utilizing a double dueling deep Q-network(D3QN),tailored for dynamic multi-agent environments.A novel reward function based on multi-agent positional constraints is designed,and a training strategy based on incremental learning is performed to achieve collaborative path planning of multiple agents.Moreover,the greedy and Boltzmann probability selection policy is introduced for action selection and avoiding convergence to local extremum.To match radar and image sensors,a convolutional neural network-long short-term memory(CNN-LSTM)architecture is constructed to extract the feature of multi-source measurement as the input of the D3QN.The algorithm’s efficacy and reliability are validated in a simulated environment,utilizing robot operating system and Gazebo.The simulation results show that the proposed algorithm provides a real-time solution for path planning tasks in dynamic scenarios.In terms of the average success rate and accuracy,the proposed method is superior to other deep learning algorithms,and the convergence speed is also improved.

Mobile sensors’patrol path planning in unobservable border region

Purpose-The border control becomes challenging when a protected region is large and there is a limited number of border patrols.This research paper proposes a novel heuristic-based patrol path planning scheme in order to efficiently patrol with resource scarcity.Design/methodology/approach-The trespasser influencing score,which is determined from the environmental characteristics and trespassing statistic of the region,is used as a heuristic for measuring a chance of approaching a trespasser.The patrol plan is occasionally updated with a new trespassing statistic during a border operation.The performance of the proposed patrol path planning scheme was evaluated and compared with other patrol path planning schemes by the empirical experiment under different scenarios.Findings-The result from the experiment indicates that the proposed patrol planning outperforms other patrol path planning schemes in terms of the trespasser detection rate,when more environment-aware trespassers are in the region.Research limitations/implications-The experiment was conducted through simulated agents in simulated environment,which were assumed to mimic real behavior and environment.Originality/value-This research paper contributes a heuristic-based patrol path planning scheme that applies the environmental characteristics and dynamic statistic of the region,as well as a border surveillance problem model that would be useful for mobile sensor planning in a border surveillance application.

基于精英族系遗传算法的AUV集群路径规划

针对传统路径规划算法仅能规划单一最短路径且不能调节路径宽度而难以适用于自主式水下航行器(autonomous underwater vehicle,AUV)集群航路规划的缺陷,提出了精英族系遗传算法(elite family genetic algorithm,EFGA)。该算法将基因适应度加入适应度评价函数中,同时在进化过程中标记精英个体作为多路径规划结果,并在该算法基础上针对AUV集群路径规划问题设计了一种多智能体路径规划(multi-agent path planning,MAPP)方法。仿真结果表明,该算法可以求解无冲突路径集合实现MAPP,通过实现AUV集群的最优多路径航行方案减少集群的航行耗时,且能够满足不同AUV编队规模对可调路径宽度的需求。

多智能体路径规划研究进展

多智能体路径规划是一类寻找多个智能体从起始位置到目标位置且无冲突的最优路径集合的问题,针对该问题的研究在物流、军事和安防等领域有着大量的应用场景.对国内外关于多智能体路径规划问题的研究进展进行系统整理和分类,按照结果最优性的不同,多智能体路径规划算法被分为最优算法和近似算法2类.最优的多智能体路径规划算法主要分为基于A*搜索、基于代价增长树、基于冲突搜索和基于规约的4种算法.近似的多智能体路径规划算法主要分为无边界次优的算法和有边界次优的算法2类.基于上述分类,分析各种算法的特点,介绍近年来具有代表性的研究成果,并对多智能体路径规划问题未来的研究方向进行展望.

Multi-Objective Loosely Synchronized Search for Multi-Objective Multi-Agent Path Finding with Asynchronous Actions

In recent years,the path planning for multi-agent technology has gradually matured,and has made breakthrough progress.The main difficulties in path planning for multi-agent are large state space,long algorithm running time,multiple optimization objectives,and asynchronous action of multiple agents.To solve the above problems,this paper first introduces the main problem of the research:multi-objective multi-agent path finding with asynchronous action,and proposes the algorithm framework of multi-objective loose synchronous(MO-LS)search.By combining A*and M*,MO-LS-A*and MO-LS-M*algorithms are respectively proposed.The completeness and optimality of the algorithm are proved,and a series of comparative experiments are designed to analyze the factors affecting the performance of the algorithm,verifying that the proposed MO-LS-M*algorithm has certain advantages.