Immunized Token-Based Approach for Autonomous Deployment of Multiple Mobile Robots in Burnt Area

Abstract

Collaborative exploration, sensing and communication in previously unknown environments with high network latency, such as outer space, battlefields and disaster hit areas are promising in multi-agent applications. When disasters such as large fires or natural disasters occur, previously established networks might be destroyed or incapacitated. In these cases, multiple autonomous mobile robots (AMR) or autonomous unmanned ground vehicles carrying wireless devices and/or thermal sensors can be deployed to create an end-to-end communication and sensing coverage to support rescue efforts or access the severity of damage. However, a fundamental problem is how to rapidly deploy these mobile agents in such complex and dynamic environments. The uncertainties introduced by the operational environment and wide range of scheduling problem have made solving them as a whole challenging. In this paper, we present an efficient decentralized approach for practical mobile agents deployment in unknown, burnt or disaster hit areas. Specifically, we propose an approach that combines methods from Artificial Immune System (AIS) with special token messages passing for a team of interconnected AMR to decide who, when and how to act during deployment process. A distributed scheme is adopted, where each AMR makes its movement decisions based on its local observation and a special token it receives from its neighbors. Empirical evidence of robustness and effectiveness of the proposed approach is demonstrated through simulation.

Bioinspired Perception and Navigation of Service Robots in Indoor Environments: A Review

“…More precisely, Table 1 lists the vision-based approaches with their contributions, sensors, and real-time performance, and the most popular method is the optic flow, which is used by bees. The vision-based applications include transport [ 9 , 18 , 39 , 46 , 50 , 60 ], exploration [ 8 , 19 , 24 , 26 , 40 , 41 , 45 , 49 ], tracking and assist [ 31 , 56 , 58 ], security and surveillance [ 7 , 52 ], homing and searching [ 5 , 54 , 55 ], floor cleaning [ 6 , 43 ], and search and rescue [ 44 ]. Only 43.75% of the cited papers indicate they can operate in real time, which is achieved by processing or computation speed, minimal computation load, or predefined parameters.…”

“…Machine-learning-based or olfactory sensing techniques were the next step. An efficient decentralized approach, an immunized token-based approach [ 44 ], was proposed for an autonomous deployment in burnt or unknown environments to estimate the severity of damage or support rescue efforts. It used SLAM to detect the environment, and the robots carried wireless devices to create communication and sensing coverage.…”