Autonomous Mobile Robots (AMRs) rely on rechargeable batteries to execute several objective tasks during navigation. Previous research has focused on minimizing task downtime by coordinating task allocation and/or charge scheduling across multiple AMRs. However, they do not jointly ensure low task downtime and high-quality battery life.In this paper, we present TCM, a Task allocation and Charging Manager for AMR fleets. TCM allocates objective tasks to AMRs and schedules their charging times at the available charging stations for minimized task downtime and maximized AMR batteries’ quality of life. We formulate the TCM problem as an MINLP problem and propose a polynomial-time multi-period TCM greedy algorithm that periodically adapts its decisions for high robustness to energy modeling errors. We experimentally show that, compared to the MINLP implementation in Gurobi solver, the designed algorithm provides solutions with a performance ratio of 1.15 at a fraction of the execution time. Furthermore, compared to representative baselines that only focus on task downtime, TCM achieves similar task allocation results while providing much higher battery quality of life.
E2M: an energy-efficient middleware for computer vision applications on autonomous mobile robots
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 more »
- Award ID(s):
- 1724227
- Publication Date:
- NSF-PAR ID:
- 10183406
- Journal Name:
- SEC '19: Proceedings of the 4th ACM/IEEE Symposium on Edge Computing
- Page Range or eLocation-ID:
- 59 to 73
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Publisher's Version of Record
- https://doi.org/10.1145/3318216.3363302
