Deployment of mobile robots with energy and timing constraints

• Published in: IEEE transactions on robotics Vol. 22; no. 3; pp. 507 - 522
• Main Authors: Yongguo Mei, Yung-Hsiang Lu, Hu, Y.C., Lee, C.S.G.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01-06-2006; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Applied sciences; Batteries; Cleaning; Communication system control; Computer science; control theory; systems; Construction; Control systems; Control theory. Systems; Coverage; deployment; Earthquakes; energy constraints; Exact sciences and technology; Heuristic; Intelligent robots; Landmine detection; Mathematical models; Mobile robots; Obstacles; Power consumption; Power supply; Rectangles; Robot sensing systems; Robotics; Robotics and automation; Robots; Tasks; Time measurements; Timing; timing constraints; Energy consumption; Rescue; Obstacle; Probabilistic approach; Moving robot; Unfolding; Cleaning; Modeling; Robotics; Multiagent system; Heuristic method; Timing; Localization
• ISSN: 1552-3098; 1941-0468
• DOI: 10.1109/TRO.2006.875494

Mobile robots can be used in many applications, such as carpet cleaning, search and rescue, and exploration. Many studies have been devoted to the control, sensing, and communication of robots. However, the deployment of robots has not been fully addressed. The deployment problem is to determine the number of groups unloaded by a carrier, the number of robots in each group, and the initial locations of those robots. This paper investigates robot deployment for coverage tasks. Both timing and energy constraints are considered; the robots carry limited energy and need to finish the tasks before deadlines. We build power models for mobile robots and calculate the robots' power consumption at different speeds. A speed-management method is proposed to decide the traveling speeds to maximize the traveling distance under both energy and timing constraints. Our method uses rectangle scanlines as the coverage routes, and solves the deployment problem using fewer robots. Finally, we provide an approach to consider areas with random obstacles. Compared with two simple heuristics, our solution uses 36% fewer robots for open areas and 32% fewer robots for areas with obstacles.