Mobile Robot Localization in Outdoor Environments Based on Near-infrared Vision

Abstract

Multi-robot systems have increasingly become instrumental in tackling search and coverage problems. However, the challenge of optimizing task efficiency without compromising task success still persists, particularly in expansive, unstructured environments with dense obstacles. This paper presents an innovative, decentralized Voronoi-based approach for search and coverage to reactively navigate these complexities while maintaining safety. This approach leverages the active sensing capabilities of multi-robot systems to supplement GIS (Geographic Information System), offering a more comprehensive and real-time understanding of the environment. Based on point cloud data, which is inherently non-convex and unstructured, this method efficiently generates collision-free Voronoi regions using only local sensing information through spatial decomposition and spherical mirroring techniques. Then, deadlockaware guided map integrated with a gradient-optimized, centroid Voronoi-based coverage control policy, is constructed to improve efficiency by avoiding exhaustive searches and local sensing pitfalls. The effectiveness of our algorithm has been validated through extensive numerical simulations in highfidelity environments, demonstrating significant improvements in both task success rate, coverage ratio, and task execution time compared with others.

Robotic Slag Offloading and Process Improvement of Magnesium Smelting in Pidgeon Process with Faster Region-based Convolutional Neural Network

“…the ability to locate the high-temperature targets (slags). An important way to improve this ability is to couple infrared thermal imaging sensor with high-precision laser/ultrasonic depth sensor [4][5][6]. Below is a brief review of the relevant studies in China.…”