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 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 performance
degradation lower than 7.9% for safety and 1.84% for accuracy.
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This content will become publicly available on May 29, 2024
An Open Approach to Energy-Efficient Autonomous Mobile Robots
Autonomous mobile robots (AMRs) have the capability to execute a wide range of tasks with minimal human intervention. However, one of the major limitations of AMRs is their limited battery life, which often results in interruptions to their task execution and the need to reach the nearest charging station. Optimizing energy consumption in AMRs has become a critical challenge in their deployment. Through empirical studies on real AMRs, we have identified a lack of coordination between computation and control as a major source of energy inefficiency. In this paper, we propose a comprehensive energy prediction model that provides real-time energy consumption for each component of the AMR. Additionally, we propose three path models to address the obstacle avoidance problem for AMRs. To evaluate the performance of our energy prediction and path models, we have developed a customized AMR called Donkey, which has the capability for fine-grained (millisecond-level) end-to-end power profiling. Our energy prediction model demonstrated an accuracy of over 90% in our evaluations. Finally, we applied our energy prediction model to obstacle avoidance and guided energy-efficient path selection, resulting in up to a 44.8% reduction in energy consumption compared to the baseline.
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- Award ID(s):
- 1724227
- NSF-PAR ID:
- 10487735
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- 2023 IEEE International Conference on Robotics and Automation (ICRA)
- Page Range / eLocation ID:
- 11569 to 11575
- Subject(s) / Keyword(s):
- ["Energy consumption","Computational modeling","Predictive models","Charging stations","Energy efficiency","Real-time systems","Batteries"]
- Format(s):
- Medium: X
- Location:
- London, United Kingdom
- Sponsoring Org:
- National Science Foundation
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