An official website of the United States government
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.
more »
« less
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 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.
more »
« less
- Award ID(s):
- 1724227
- PAR ID:
- 10183406
- Date Published:
- Journal Name:
- SEC '19: Proceedings of the 4th ACM/IEEE Symposium on Edge Computing
- Page Range / eLocation ID:
- 59 to 73
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
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.more » « less
-
Autonomous mobile robots (AMRs) are capable of carrying out operations continuously for 24/7, which enables them to optimize tasks, increase throughput, and meet demanding operational requirements. To ensure seamless and uninterrupted operations, an effective coordination of task allocation and charging schedules is crucial while considering the preservation of battery sustainability. Moreover, regular preventive main- tenance plays an important role in enhancing the robustness of AMRs against hardware failures and abnormalities during task execution. However, existing works do not consider the influence of properly scheduling AMR maintenance on both task downtime and battery lifespan. In this paper, we propose MTC, a maintenance-aware task and charging scheduler designed for fleets of AMR operating continuously in highly automated envi- ronments. MTC leverages Linear Programming (LP) to first help decide the best time to schedule maintenance for a given set of AMRs. Subsequently, the Kuhn-Munkres algorithm, a variant of the Hungarian algorithm, is used to finalize task assignments and carry out the charge scheduling to minimize the combined cost of task downtime and battery degradation. Experimental results demonstrate the effectiveness of MTC, reducing the combined total cost up to 3.45 times and providing up to 68% improvement in battery capacity degradation compared to the baselines.more » « less
-
The next ubiquitous computing platform, after personal computers and smartphones, is likely one of the autonomous natures, such as drones, robots, and self-driving cars, which have moved from mere lab concepts to permeating almost every aspect of our soci- ety [16, 20, 25]. Behind the proliferation of autonomous machines is the critical need to ensure reliability [7, 22–24]. Almost every vendor, be it in the software, hardware, or systems segment, has to conform to functional safety standards when shipping products for automotives. Today’s resiliency solutions to autonomous machines, however, all make fundamental trade-offs between resiliency and cost, which manifests as high overhead in performance, energy, and chip area. For instance, hardware modular redundancy provides high safety but more than doubles the area and energy cost [1]. The reason is that today’s solutions are of the “one-size-fits-all” nature: they use the same protection scheme throughout the entire computing stack of autonomous machines. As a result, they have to accommodate the least robust component, leading to a high protection overhead. The insight of this paper is that for a resiliency solution to pro- vide high protection coverage while introducing little cost, we must exploit the inherent robustness variations in the domain-specific autonomous machine computing. In particular, we show that the different autonomous machine kernels differ significantly in their inherent robustness and performance. Building on top of that, we propose a Vulnerable-Proportional Protection (VPP) design paradigm, in which the protection budget, be it spatially (e.g., modular re- dundancy) or temporally (e.g., re-execution), should be inversely proportional to the inherent robustness of a task in the autonomous machine system. In stark contrast to the existing “one-size-fits-all” strategy, VPP wisely allocates the protection budget, thus achieving the same protection coverage with little overhead, which provides a blueprint design paradigm towards reliable autonomous machinesmore » « less
-
ADARE-HD: Adaptive-Resolution Framework for Efficient Object Detection and Tracking via HD-ComputingEfficient and low-energy camera signal processing is critical for battery-supported sensing and surveillance applications. In this research, we develop a video object detection and tracking framework which adaptively down-samples frame pixels to minimize computation and memory costs, and thereby the energy consumed, while maintaining a high level of accuracy. Instead of always operating with the highest sensor pixel resolution (compute-intensive), video frame (pixel) content is down-sampled spatially, to adapt to changing camera environments (size of object tracked, peak-signal-tonoise- ratio (i.e, PSNR) of video frames). Object detection and tracking is supported by a novel video resolution-aware adaptive hyperdimensional computing framework. This leverages a low memory overhead non-linear hypervector encoding scheme specifically tailored for handling multiple degrees of resolution. Previous classification decisions of a moving object based on its tracking label are used to improve tracking robustness. Energy savings of up to 1.6 orders of magnitude and up to an order of magnitude compute speedup is obtained on a range of experiments performed on benchmark systems.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3318216.3363302
