A Multi-Sensory Robot Used for Searching Toxic Gas Leak Source

Abstract

For filling the application requirement of searching toxic gas leak source, a multi-sensory robot, which is used for searching toxic gas leak source, is developed. This robot consists of humanoid multiple degree of freedom head system and tracked walking mechanism, with the sense of smell, hearing and stereo vision and other functions. The robot’s bionic olfaction system can detect 5 kinds of toxic gas in the same time, and the hearing system consisted of four microphones can get all kinds of sound in the general environment. Meanwhile, its binocular stereo vision system can get the 3D information of the work environmental. Distributed controll system is constructed for the robot, and comprehensive utilization of the multi-sensory function can increase the efficiency and accuracy of searching leakage. This robot can give full play the advantage of multiple degrees of freedom to expand the searching scope of the robot, and enhance the flexibility of the searching behavior. This robot system has wide application prospects in the environment of toxic gas.

Multi-Target Coordinated Search Algorithm for Swarm Robotics Considering Practical Constraints

“…The common research contents of swarm robot systems include target search (Alfeo et al, 2019 ; Booth et al, 2020 ), task assignment (Liang et al, 2018 ), cluster avoidance (Khan et al, 2019 ), path planning (Ryan, 2008 ; Luo et al, 2017 ), and cluster formation (Anonymous, 1993 ; Alsamman, 2011 ). In this paper, the target search problem of swarm robots in unknown complex environments is mainly studied, such as forest fire detection (Yao et al, 2018 ; Marzaeva, 2019 ), toxic gas leak detection (Zhang et al, 2010 ; Moshayedi and Gharpure, 2013 ), search and rescue of missing personnel (Goodrich et al, 2009 ; Kamegawa et al, 2020 ), military target detection (Ha and Cho, 2018 ; Jiong et al, 2019 ) and so on. In order to solve this type of search problem, there are mainly composed of two main categories of design strategies, namely, behavior-based search and learning-based search (Cizek and Faigl, 2019 ; Berscheid et al, 2020 ; Suzuki et al, 2020 ), and this article mainly discusses the former.…”