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
- NSF-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
-
Advances in vision processing have ignited a proliferation of mobile vision applications, including augmented reality. However, limited by the inability to rapidly reconfigure sensor operation for performance-efficiency tradeoffs, high power consumption causes vision applications to drain the device's battery. To explore the potential impact of enabling rapid reconfiguration, we use a case study around marker-based pose estimation to understand the relationship between image frame resolution, task accuracy, and energy efficiency. Our case study motivates that to balance energy efficiency and task accuracy, the application needs to dynamically and frequently reconfigure sensor resolution. To explore the latency bottlenecks to sensor resolution reconfiguration, we define and profile the end-to-end reconfiguration latency and frame-to-frame latency of changing capture resolution on a Google LG Nexus 5X device. We identify three major sources of sensor resolution reconfiguration latency in current Android systems: (i) sequential configuration patterns, (ii) expensive system calls, and (iii) imaging pipeline delay. Based on our intuitions, we propose a redesign of the Android camera system to mitigate the sources of latency. Enabling smooth transitions between sensor configurations will unlock new classes of adaptive-resolution vision applications.more » « less
-
Energy-efficient visual sensing is of paramount importance to enable battery-backed low power IoT and mobile applications. Unfortunately, modern image sensors still consume hundreds of milliwatts of power, mainly due to analog readout. This is because current systems always supply a fixed voltage to the sensor’s analog circuitry, leading to higher power profiles. In this work, we propose to aggressively scale the analog voltage supplied to the camera as a means to significantly reduce sensor power consumption. To that end, we characterize the power and fidelity implications of analog voltage scaling on three off-the-shelf image sensors. Our characterization reveals that analog voltage scaling reduces sensor power but also degrades image quality. Furthermore, the degradation in image quality situationally affects the task accuracy of vision applications. We develop a visual streaming pipeline that flexibly allows application developers to dynamically adapt sensor voltage on a frame-by-frame basis. We develop a voltage controller that programmatically generates desired sensor voltage based on application request. We integrate our voltage controller into the existing RPi-based video streaming IoT pipeline. On top of this, we develop runtime support for flexible voltage specification from vision applications. Evaluating the system over a wide range of voltage scaling policies on popular vision tasks reveals that Squint imaging can deliver up to 73% sensor power savings, while maintaining reasonable task fidelity. Our artifacts are available at: https://gitlab.com/squint1/squint-ae-publicmore » « less
-
Monitoring localization safety will be necessary to certify the performance of robots that operate in life-critical applications, such as autonomous passenger vehicles or delivery drones because many current localization safety methods do not account for the risk of undetected sensor faults. One type of fault, misassociation, occurs when a feature extracted from a mapped landmark is associated to a non-corresponding landmark and is a common source of error in feature-based navigation applications. This paper accounts for the probability of misassociation when quantifying landmark-based mobile robot localization safety for fixed-lag smoothing estimators. We derive a mobile robot localization safety bound and evaluate it using simulations and experimental data in an urban environment. Results show that localization safety suffers when landmark density is relatively low such that there are not enough landmarks to adequately localize and when landmark density is relatively high because of the high risk of feature misassociation.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3318216.3363302
