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Abstract-Mobile robots are used in many applications, such as carpet cleaning, pickup and delivery, search and rescue, and entertainment. Energy limitation is one of the most important challenges for mobile robots. Most existing studies on mobile robots focus on motion planning to reduce motion power. However, motion is not the only power consumer. In this paper, we present a case study of a mobile robot called Pioneer 3DX. We analyze the energy consumers. We build power models for motion, sonar sensing and control based on experimental results. The results show that motion consume less than 50% power on average. Therefore, it is important to consider the other components in energy-efficient designs. We introduce two energy-conservation techniques: dynamic power management and real-time scheduling. We provide several examples showing how these techniques can be applied to robots. These techniques together with motion planning provide greater opportunities to achieve better energy efficiency for mobile robots. Although our study is based on a specific robot, the approach can be applied to other types of robots.

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Abstract-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.

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