- Award ID(s):
- Publication Date:
- NSF-PAR ID:
- 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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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.
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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.
The objective of this research is to evaluate vision-based pose estimation methods for on-site construction robots. The prospect of human-robot collaborative work on construction sites introduces new workplace hazards that must be mitigated to ensure safety. Human workers working on tasks alongside construction robots must perceive the interaction to be safe to ensure team identification and trust. Detecting the robot pose in real-time is thus a key requirement in order to inform the workers and to enable autonomous operation. Vision-based (marker-less, marker-based) and sensor-based (IMU, UWB) are two of the main methods for estimating robot pose. The marker-based and sensor-based methods require some additional preinstalled sensors or markers, whereas the marker-less method only requires an on-site camera system, which is common on modern construction sites. In this research, we develop a marker-less pose estimation system, which is based on a convolutional neural network (CNN) human pose estimation algorithm: stacked hourglass networks. The system is trained with image data collected from a factory setup environment and labels of excavator pose. We use a KUKA robot arm with a bucket mounted on the end-effector to represent a robotic excavator in our experiment. We evaluate the marker-less method and compare the result withmore »
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