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Autonomous mobile robots (AMRs) have been widely utilized in industry to execute various on-board computer-vision applications including autonomous guidance, security patrol, object detection, and face recognition. Most of the applications executed by an AMR involve the analysis of camera images through trained machine learning models. Many research studies on machine learning focus either on performance without considering energy efficiency or on techniques such as pruning and compression to make the model more energy-efficient. However, most previous work do not study the root causes of energy inefficiency for the execution of those applications on AMRs. The computing stack on an AMR accounts for 33% of the total energy consumption and can thus highly impact the battery life of the robot. Because recharging an AMR may disrupt the application execution, it is important to efficiently utilize the available energy for maximized battery life. In this paper, we first analyze the breakdown of power dissipation for the execution of computer-vision applications on AMRs and discover three main root causes of energy inefficiency: uncoordinated access to sensor data, performance-oriented model inference execution, and uncoordinated execution of concurrent jobs. In order to fix these three inefficiencies, we propose E2M, an energy-efficient middleware software stack for autonomous mobile robots. First, E2M regulates the access of different processes to sensor data, e.g., camera frames, so that the amount of data actually captured by concurrently executing jobs can be minimized. Second, based on a predefined per-process performance metric (e.g., safety, accuracy) and desired target, E2M manipulates the process execution period to find the best energy-performance trade off. Third, E2M coordinates the execution of the concurrent processes to maximize the total contiguous sleep time of the computing hardware for maximized energy savings. We have implemented a prototype of E2M on HydraOne, a real-world AMR. Our experimental results show that, compared to several baselines, E2M leads to 24% energy savings for the computing platform, which translates into an extra 11.5% of battery time and 14 extra minutes of robot runtime, with a

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