This paper proposes a state-machine model for a multi-modal, multi-robot environmental sensing algorithm. This multi-modal algorithm integrates two different exploration algorithms: (1) coverage path planning using variable formations and (2) collaborative active sensing using multi-robot swarms. The state machine provides the logic for when to switch between these different sensing algorithms. We evaluate the performance of the proposed approach on a gas source localisation and mapping task. We use hardware-in-the-loop experiments and real-time experiments with a radio source simulating a real gas field. We compare the proposed approach with a single-mode, state-of-the-art collaborative active sensing approach. Our results indicate that our multi-modal switching approach can converge more rapidly than single-mode active sensing.
Multi-Depth Uniform Coverage Path Planning for Unmanned Surface Vehicle Surveying
AZTI Foundation（AZTI基金会）；Tsinghua University（清华大学）；University of the Basque Country (UPV/EHU)（巴斯克国家大学（UPV/EHU））；University of Technology Sydney（悉尼技术大学）；IKERBASQUE
Continuous World Coverage Path Planning for Fixed-Wing UAVs using Deep Reinforcement Learning
TUM School of Engineering and Design, Technical University of Munich（慕尼黑技术大学工程与设计学院）；Dept. of Electrical Engineering and Computer Sciences, University of California, Berkeley（加州大学伯克利分校电气工程与计算机科学系）
Connectivity-Aware UAV Path Planning with Aerial Coverage Maps
Department of ECE, Hong Kong University of Science and Technology, Hong Kong（香港科技大学电子工程系）；Department of EIE, The Hong Kong Polytechnic University, Hong Kong（香港理工大学电子工程系）
Geofenced Unmanned Aerial Robotic Defender for Deer Detection and Deterrence (GUARD)
Dept. of Computer Science & Engineering, University of Minnesota（计算机科学与工程系，明尼苏达大学）；Dept. of Mechanical Engineering, University of Minnesota（机械工程系，明尼苏达大学）
