Decentralized vehicle routing in a stochastic and dynamic environment with customer impatience
Proceedings of the 1st international conference on Robot communication and coordination
Brief paper: Optimal solutions to a class of power management problems in mobile robots
Automatica (Journal of IFAC)
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Power modeling of a skid steered wheeled robotic ground vehicle
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Robotics and Autonomous Systems
Mobile robotic surveying performance for planetary surface site characterization
PerMIS '08 Proceedings of the 8th Workshop on Performance Metrics for Intelligent Systems
Energy efficient swarm deployment for search in unknown environments
ANTS'10 Proceedings of the 7th international conference on Swarm intelligence
Searching energy-efficient route in rough terrain for mobile robot with ant algorithm
ICIRA'12 Proceedings of the 5th international conference on Intelligent Robotics and Applications - Volume Part III
Mobility increases the surface coverage of distributed sensor networks
Computer Networks: The International Journal of Computer and Telecommunications Networking
Robotics and Autonomous Systems
Hi-index | 0.00 |
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.