Autonomous multi-robot allocation and formation control for remote sensing in environmental exploration

Abstract

Multi-robot systems have distinct advantages over single-robot systems in terms of operating speed, dependability, and efficiency. They are commonly used for a variety of environmental exploration applications in conjunction with remote sensing. In this paper, a self-organizing map (SOM) neural network algorithm is developed for multirobot task allocation and formation control. Multi-robot task allocation involves controlling a swarm of mobile robots to achieve designated task locations with cooperation and coordination of each robot. This framework is applicable to autonomous remote sensing by a swarm of robots, improving the efficiency and reliability of remote sensing in large environmental exploration areas. In addition to global task allocation, it is also important to control the formation of multi-robots. Each robot has a moving speed, and its field of view describes the visual range perpendicular to the direction of movement. The proposed framework utilizes a particle swarm optimization algorithm to generate the shortest planned trajectory for the multi-robot system. Additionally, a path smoothing technique is used to reduce the overall path and stress on the robots. The simulation studies validate the effectiveness of the SOM-based task allocation and formation control. It can deal with complicated cases such as that the formation changes its geometrical shape to adapt to the new environment in exploration.

Digital twin‐based multi‐objective autonomous vehicle navigation approach as applied in infrastructure construction

“…Recently, a variety of approaches have been proposed to achieve reliable autonomous vehicles motion planning, such as nature-inspired optimisation algorithms [6][7][8][8][9][10][11][12], graphbased models [13][14][15][16], sampling-based methods [17][18][19] and neural networks [20].…”