DIBNN: A Dual-Improved-BNN Based Algorithm for Multi-Robot Cooperative Area Search in Complex Obstacle Environments

Chen, Bo; Zhang, Hui; Zhang, Fangfang; Jiang, Yiming; Miao, Zhiqiang; Yu, Hongnian; Wang, Yaonan

Abstract

Aiming at the area search task of a multi-robot system in an unknown complex obstacle environment, we propose a cooperative area search algorithm based on a dual improved bio-inspired neural network (DIBNN). First, we improve the BNN model to reduce the interference of the complex obstacle environment on robot decision making. Each robot generally chooses the neuron with the largest sum of surrounding activity values among adjacent neurons as its next movement position. Then, we propose a collaborative search mechanism. When a robot falls into a local deadlock state in the complex obstacle environment, the mechanism will guide the robot to quickly find unsearched areas. Finally, we conduct multi-robot area search simulation experiments under different obstacle environments and compare them with three baseline algorithms in this field. The simulation results verify that the proposed algorithm can efficiently guide the multi-robot to complete the area search task in the complex obstacle environment. Note to Practitioners —The motivation of this article arises from the need to develop fast and effective area search algorithms for practical applications such as UAV swarm reconnaissance and multiple mobile robots area search and rescue. The algorithms based on BNN has been widely used in search tasks under unknown environments due to its good scalability and efficiency. However, the efficiency of area search in complex obstacle environments cannot be guaranteed. In order to achieve efficient area search in unknown complex obstacle environments, the DIBNN algorithm is proposed. It utilizes a cooperative search mechanism and achieves better performance. DIBNN can also be applied to multi-robot systems in different scenarios, demonstrating strong scalability.

Citation

Chen, B., Zhang, H., Zhang, F., Jiang, Y., Miao, Z., Yu, H., & Wang, Y. (in press). DIBNN: A Dual-Improved-BNN Based Algorithm for Multi-Robot Cooperative Area Search in Complex Obstacle Environments. IEEE Transactions on Automation Science and Engineering, https://doi.org/10.1109/tase.2024.3379166

• Journal Article Type: Article
• Acceptance Date: Mar 10, 2024
• Online Publication Date: Mar 21, 2024
• Deposit Date: Mar 25, 2024
• Journal: IEEE Transactions on Automation Science and Engineering
• Print ISSN: 1545-5955
• Electronic ISSN: 1558-3783
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• DOI: https://doi.org/10.1109/tase.2024.3379166
• Keywords: Multi-robot, area search, improved bio-inspired neural network, complex obstacle environment, collaborative search mechanism
• Public URL: http://researchrepository.napier.ac.uk/Output/3574134