Efficient Balancing A* Search for Multi-robot Collaboration With Blockchain Consensus
Erteng Liu, Tianchi Lu, Rui Shen, Jianhai Chen, Butian Huang · 2023
The multiple target point search is an important and universal problem in multi-robot systems. However, many traditional multi-robot systems can not ensure the reliability of collaboration and can not be able to avoid robot collision accident causing huge economic losses, due to lack of efficient multi-robot point to point (P2P) communication, data security protection and error handling mechanism, and lack of considering the equilibrium of multi-target point search task allocation mechanism. To solve this problem, in this paper, we propose an efficient multi-robot collaboration mechanism method based on blockchain consensus and balancing allocation of multi-target search tasks. At first, for a given scale size of multi-robot and multiple target search tasks, we ensure the data information consistency between distributed multiple robots, the data security and collaboration reliability through utilizing blockchain consensus. Then, we put forward a novel robot search task allocation balancing mechanism to avoid the reduction of overall collaboration performance due to excessive time and too long of some robot search tasks. Finally, we implement a multi-robot collaboration simulation system and the result of a lot of simulation experiments demonstrates the effectiveness of our method.