Multiagent coverage search based on Voronoi and sparse heteroscedastic Gaussian process

Abstract

This work is concerned with multiagent cooperative and autonomous coverage search in an unknown environment. A novel Voronoi and sparse heteroscedastic Gaussian process (SHGP)-based search method is proposed to improve the target search efficiency. By means of heteroscedastic Gaussian process (HGP), each agent can estimate the probability of target existence in given locations online according to the local probability map. Then, these local probability maps are fused together to obtain a posterior probability distribution for target prediction. Based on that, the agents' search paths within Voronoi cells will be adjusted adaptively online to make sure all the agents can search for targets more efficiently. A challenging issue then falls in the computational complexity of the HGP. To solve this problem, we propose an adaptive ranked-set sampling algorithm based on the weighted sampling process and K-means clustering algorithm. Theoretical analysis on the discrepancy of the kernel matrix between the original probability points and the sampling points is also given. Simulation results demonstrate that our proposed coverage search method can improve the performance of the area search compared with the existing Voronoi-based one.

Multi‐AGV route planning in automated warehouse system based on shortest‐time Q‐learning algorithm

“…Multi-AGV collision-free route planning belongs to the multi-agent path finding (MAPF) problem [9], which has received extensive attention in the field of robot path planning [10,11]. Most literature takes multi-AGV collision-free route planning as an extension of single-AGV route planning and determines the optimal route for AGV in advance offline before the AGV travels.…”