Resilient Task Allocation in Heterogeneous Multi-Robot Systems
| Autor: | Siddharth Mayya, Vijay Kumar, David Saldaña, Diego S. D'Antonio |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
0209 industrial biotechnology Control and Optimization Computer science Distributed computing Biomedical Engineering 02 engineering and technology Task (project management) Computer Science - Robotics 020901 industrial engineering & automation Resource (project management) Artificial Intelligence 0202 electrical engineering electronic engineering information engineering Resource management Computer Science - Multiagent Systems Robot kinematics Mechanical Engineering Computer Science Applications Human-Computer Interaction Control and Systems Engineering Face (geometry) Task analysis Robot 020201 artificial intelligence & image processing Computer Vision and Pattern Recognition Robotics (cs.RO) Multiagent Systems (cs.MA) |
| Popis: | For a multi-robot system equipped with heterogeneous capabilities, this paper presents a mechanism to allocate robots to tasks in a resilient manner when anomalous environmental conditions such as weather events or adversarial attacks affect the performance of robots within the tasks. Our primary objective is to ensure that each task is assigned the requisite level of resources, measured as the aggregated capabilities of the robots allocated to the task. By keeping track of task performance deviations under external perturbations, our framework quantifies the extent to which robot capabilities (e.g., visual sensing or aerial mobility) are affected by environmental conditions. This enables an optimization-based framework to flexibly reallocate robots to tasks based on the most degraded capabilities within each task. In the face of resource limitations and adverse environmental conditions, our algorithm minimally relaxes the resource constraints corresponding to some tasks, thus exhibiting a graceful degradation of performance. Simulated experiments in a multi-robot coverage and target tracking scenario demonstrate the efficacy of the proposed approach. A modified version has been submitted to IEEE Robotics and Automation Letters |
| Databáze: | OpenAIRE |
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