Multi-UAVs Collaborative Path Planning in the Cramped Environment

Due to its flexibility and complementarity, the multiUAVs system is well adapted to complex and cramped workspaces, with great application potential in the search and rescue(SAR) and indoor goods delivery fields. However, safe and effective path planning of multiple unmanned aerial vehicles(UAVs)in the cramped environment is always challenging: conflicts with each other are frequent because of high-density flight paths, collision probability increases because of space constraints, and the search space increases significantly, including time scale, 3D scale and model scale. Thus, this paper proposes a hierarchical collaborative planning framework with a conflict avoidance module at the high level and a path generation module at the low level. The enhanced conflict-base search(ECBS) in our framework is improved to handle the conflicts in the global path planning and avoid the occurrence of local deadlock. And both the collision and kinematic models of UAVs are considered to improve path smoothness and flight safety. Moreover, we specifically designed and published the cramped environment test set containing various unique obstacles to evaluating our framework performance thoroughly. Experiments are carried out relying on Rviz, with multiple flight missions: random, opposite, and staggered, which showed that the proposed method can generate smooth cooperative paths without conflict for at least 60 UAVs in a few minutes.The benchmark and source code are released in https://github.com/inin-xingtian/multi-UAVs-path-planner.

MUTS-Based Cooperative Target Stalking for A Multi-USV System

This paper is concerned with the cooperative target stalking for a multi-unmanned surface vehicle(multi-USV)system.Based on the multi-agent deep deterministic policy gradient(MADDPG)algorithm,a multi-USV target stalking(MUTS)algorithm is proposed.Firstly,a V-type probabilistic data extraction method is proposed for the first time to overcome shortcomings of the MADDPG algorithm.The advantages of the proposed method are twofold:1)it can reduce the amount of data and shorten training time;2)it can filter out more important data in the experience buffer for training.Secondly,in order to avoid the collisions of USVs during the stalking process,an action constraint method called Safe DDPG is introduced.Finally,the MUTS algorithm and some existing algorithms are compared in cooperative target stalking scenarios.In order to demonstrate the effectiveness of the proposed MUTS algorithm in stalking tasks,mission operating scenarios and reward functions are well designed in this paper.The proposed MUTS algorithm can help the multi-USV system avoid internal collisions during the mission execution.Moreover,compared with some existing algorithms,the newly proposed one can provide a higher convergence speed and a narrower convergence domain.

Heterogeneous coverage method with multiple unmanned aerial vehicle assisted sink nodes

A heterogeneous coverage method with multiple unmanned aerial vehicle assisted sink nodes(MUAVSs)for multi-objective optimization problem(MOP)is proposed,which is based on quantum wolf pack evolution algorithm(QWPEA)and power law entropy(PLE)theory.The method is composed of preset move and autonomous coordination stages for satisfying non-repeated coverage,connectedness,and energy balance of sink layer critical requirements,which is actualized to cover sensors layer in large-scale outside wireless sensor networks(WSNs).Simulation results show that the performance of the proposed technique is better than the existing related coverage technique.