A Real-Time Rescheduling Algorithm for Multi-robot Plan Execution

• URL: http://arxiv.org/abs/2403.18145v1
• Date: Tue, 26 Mar 2024 23:10:41 GMT
• Title: A Real-Time Rescheduling Algorithm for Multi-robot Plan Execution
• Authors: Ying Feng, Adittyo Paul, Zhe Chen, Jiaoyang Li,
• Abstract summary: Switchable-Edge Search (SES) is an A*-style algorithm designed to find optimal passing orders. We prove the optimality of SES and evaluate its efficiency via simulations.
• Score: 9.839983977902671
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: One area of research in multi-agent path finding is to determine how replanning can be efficiently achieved in the case of agents being delayed during execution. One option is to reschedule the passing order of agents, i.e., the sequence in which agents visit the same location. In response, we propose Switchable-Edge Search (SES), an A*-style algorithm designed to find optimal passing orders. We prove the optimality of SES and evaluate its efficiency via simulations. The best variant of SES takes less than 1 second for small- and medium-sized problems and runs up to 4 times faster than baselines for large-sized problems.

Related papers

• Training Greedy Policy for Proposal Batch Selection in Expensive Multi-Objective Combinatorial Optimization [52.80408805368928]
  We introduce a novel greedy-style subset selection algorithm for batch acquisition. Our experiments on the red fluorescent proteins show that our proposed method achieves the baseline performance in 1.69x fewer queries.
  arXiv  Detail & Related papers  (2024-06-21T05:57:08Z)
• MGCBS: An Optimal and Efficient Algorithm for Solving Multi-Goal Multi-Agent Path Finding Problem [5.580214316179672]
  The MG-MAPF problem requires each agent to visit pre-assigned multiple goal points at least once without conflict. We propose the Multi-Goal Conflict-Based Search (MGCBS), which is based on Decoupling the goal Safe interval visiting order search and the Single-agent pathfinding. Our method can consistently obtain optimal results and execute up to 7 times faster than the state-of-the-art method in our evaluation.
  arXiv  Detail & Related papers  (2024-04-30T12:49:54Z)
• An Efficient Approach to the Online Multi-Agent Path Finding Problem by Using Sustainable Information [10.367412630626834]
  Multi-agent path finding (MAPF) is the problem of moving agents to the goal without collision. We propose a three-level approach to solve online MAPF utilizing sustainable information. Our algorithm can be 1.48 times faster than SOTA on average under different agent number settings.
  arXiv  Detail & Related papers  (2023-01-11T13:04:35Z)
• Efficient Non-Parametric Optimizer Search for Diverse Tasks [93.64739408827604]
  We present the first efficient scalable and general framework that can directly search on the tasks of interest. Inspired by the innate tree structure of the underlying math expressions, we re-arrange the spaces into a super-tree. We adopt an adaptation of the Monte Carlo method to tree search, equipped with rejection sampling and equivalent- form detection.
  arXiv  Detail & Related papers  (2022-09-27T17:51:31Z)
• Optimal Multi-Agent Path Finding for Precedence Constrained Planning Tasks [0.7742297876120561]
  We consider an extension to this problem, Precedence Constrained Multi-Agent Path Finding (PC-MAPF) We propose a novel algorithm, Precedence Constrained Conflict Based Search (PC-CBS), which finds makespan-optimal solutions for this class of problems. We benchmark the performance of this algorithm over various warehouse assembly, and multi-agent pickup and delivery tasks, and use it to evaluate the sub-optimality of a recently proposed efficient baseline.
  arXiv  Detail & Related papers  (2022-02-08T07:26:45Z)
• Machine Learning for Online Algorithm Selection under Censored Feedback [71.6879432974126]
  In online algorithm selection (OAS), instances of an algorithmic problem class are presented to an agent one after another, and the agent has to quickly select a presumably best algorithm from a fixed set of candidate algorithms. For decision problems such as satisfiability (SAT), quality typically refers to the algorithm's runtime. In this work, we revisit multi-armed bandit algorithms for OAS and discuss their capability of dealing with the problem. We adapt them towards runtime-oriented losses, allowing for partially censored data while keeping a space- and time-complexity independent of the time horizon.
  arXiv  Detail & Related papers  (2021-09-13T18:10:52Z)
• Prioritized SIPP for Multi-Agent Path Finding With Kinematic Constraints [0.0]
  Multi-Agent Path Finding (MAPF) is a long-standing problem in Robotics and Artificial Intelligence. We present a method that mitigates this issue to a certain extent.
  arXiv  Detail & Related papers  (2021-08-11T10:42:11Z)
• Lower Bounds and Optimal Algorithms for Smooth and Strongly Convex Decentralized Optimization Over Time-Varying Networks [79.16773494166644]
  We consider the task of minimizing the sum of smooth and strongly convex functions stored in a decentralized manner across the nodes of a communication network. We design two optimal algorithms that attain these lower bounds. We corroborate the theoretical efficiency of these algorithms by performing an experimental comparison with existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-06-08T15:54:44Z)
• Towards Optimally Efficient Tree Search with Deep Learning [76.64632985696237]
  This paper investigates the classical integer least-squares problem which estimates signals integer from linear models. The problem is NP-hard and often arises in diverse applications such as signal processing, bioinformatics, communications and machine learning. We propose a general hyper-accelerated tree search (HATS) algorithm by employing a deep neural network to estimate the optimal estimation for the underlying simplified memory-bounded A* algorithm.
  arXiv  Detail & Related papers  (2021-01-07T08:00:02Z)
• Dynamic Multi-Robot Task Allocation under Uncertainty and Temporal Constraints [52.58352707495122]
  We present a multi-robot allocation algorithm that decouples the key computational challenges of sequential decision-making under uncertainty and multi-agent coordination. We validate our results over a wide range of simulations on two distinct domains: multi-arm conveyor belt pick-and-place and multi-drone delivery dispatch in a city.
  arXiv  Detail & Related papers  (2020-05-27T01:10:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.