M^3RS: Multi-robot, Multi-objective, and Multi-mode Routing and Scheduling
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866915539206537216 |
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| author | Mehta, Ishaan Kim, Junseo Taghipour, Sharareh Saeedi, Sajad |
| author_facet | Mehta, Ishaan Kim, Junseo Taghipour, Sharareh Saeedi, Sajad |
| contents | Task execution quality significantly impacts multi-robot missions, yet existing task allocation frameworks rarely consider quality of service as a decision variable, despite its importance in applications like robotic disinfection and cleaning. We introduce the multi-robot, multi-objective, and multi-mode routing and scheduling (M3RS) problem, designed for time-constrained missions. In M3RS, each task offers multiple execution modes with varying resource needs, durations, and quality levels, allowing trade-offs across mission objectives. M3RS is modeled as a mixed-integer linear programming (MIP) problem and optimizes task sequencing and execution modes for each agent. We apply M3RS to multi-robot disinfection in healthcare and public spaces, optimizing disinfection quality and task completion rates. Through synthetic case studies, M3RS demonstrates 3-46$\%$ performance improvements over the standard task allocation method across various metrics. Further, to improve compute time, we propose a clustering-based column generation algorithm that achieves solutions comparable to or better than the baseline MIP solver while reducing computation time by 60$\%$. We also conduct case studies with simulated and real robots. Experimental videos are available on the project page: \href{https://sites.google.com/view/g-robot/m3rs/}{https://sites.google.com/view/g-robot/m3rs/}. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_16275 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | M^3RS: Multi-robot, Multi-objective, and Multi-mode Routing and Scheduling Mehta, Ishaan Kim, Junseo Taghipour, Sharareh Saeedi, Sajad Robotics Task execution quality significantly impacts multi-robot missions, yet existing task allocation frameworks rarely consider quality of service as a decision variable, despite its importance in applications like robotic disinfection and cleaning. We introduce the multi-robot, multi-objective, and multi-mode routing and scheduling (M3RS) problem, designed for time-constrained missions. In M3RS, each task offers multiple execution modes with varying resource needs, durations, and quality levels, allowing trade-offs across mission objectives. M3RS is modeled as a mixed-integer linear programming (MIP) problem and optimizes task sequencing and execution modes for each agent. We apply M3RS to multi-robot disinfection in healthcare and public spaces, optimizing disinfection quality and task completion rates. Through synthetic case studies, M3RS demonstrates 3-46$\%$ performance improvements over the standard task allocation method across various metrics. Further, to improve compute time, we propose a clustering-based column generation algorithm that achieves solutions comparable to or better than the baseline MIP solver while reducing computation time by 60$\%$. We also conduct case studies with simulated and real robots. Experimental videos are available on the project page: \href{https://sites.google.com/view/g-robot/m3rs/}{https://sites.google.com/view/g-robot/m3rs/}. |
| title | M^3RS: Multi-robot, Multi-objective, and Multi-mode Routing and Scheduling |
| topic | Robotics |
| url | https://arxiv.org/abs/2403.16275 |