We propose an energy-efficient controller to minimize the energy consumption of a mobile robot by dynamically manipulating the mechanical and computational actuators of the robot. The mobile robot performs real-time vision-based applications based on an event-based camera. The actuators of the controller are CPU voltage/frequency for the computation part and motor voltage for the mechanical part. We show that independently considering speed control of the robot and voltage/frequency control of the CPU does not necessarily result in an energy-efficient solution. In fact, to obtain the highest efficiency, the computation and mechanical parts should be controlled together in synergy. We propose a fast hill-climbing optimization algorithm to allow the controller to find the best CPU/motor configuration at run-time and whenever the mobile robot is facing a new environment during its travel. Experimental results on a robot with Brushless DC Motors, Jetson TX2 board as the computing unit, and a DAVIS-346 event-based camera show that the proposed control algorithm can save battery energy by an average of 50.5%, 41%, and 30%, in low-complexity, medium-complexity, and high-complexity environments, over baselines.

翻译：我们建议一个节能控制器,通过动态操纵机器人的机械和计算驱动器,最大限度地减少移动机器人的能源消耗。 移动机器人使用基于事件相机的实时视觉应用。 控制器的驱动器是计算部件的CPU电压/频率和机械部分的机动电压。 我们显示,独立考虑机器人和电压/频率控制的速度控制并不一定导致一个节能解决方案。 事实上,为了获得最高效率,计算和机械部件应当协同控制。 我们提议一个快速的山坡优化算法,允许控制器在运行时和每当移动机器人在旅行中面临新环境时找到最佳的CPU/机型配置。 与Brushless DC Motors, Jetson TX2 董事会作为计算单位的机器人的实验结果,以及DAVIS-346事件相机显示,拟议的控制算法可以在低弹性、低弹性、低弹性、中等弹性、中等弹性、中等弹性、中等弹性、中等弹性环境中平均50.5%、41%和30%的情况下节省电池能源。