Scheduling of autonomous mobile robots with conflict-free routes utilising contextual-bandit-based local search. Issue 13 (3rd July 2022)

• Record Type: Journal Article
• Authors: Jun, Sungbum; Choi, Chul Hun; Lee, Seokcheon
• Is Part Of: International journal of production research. Volume 60:Issue 13(2022)
• Page Start: 4090
• Page End: 4116
• Publication Date: 2022-07-03
• Subjects: Autonomous mobile robots -- material handling -- contextual bandit -- pickup and delivery problem -- conflict resolution -- path planning; Factory management -- Periodicals; 658.57
• DOI: 10.1080/00207543.2022.2063085
• Languages: English
• ISSNs: 0020-7543

Abstract

Abstract : As autonomous robot and sensor technologies have advanced, utilisation of autonomous mobile robots (AMRs) in material handling has grown quickly, owing especially to their scalability and versatility compared with automated guided vehicles (AGVs). In order to take full advantage of AMRs, in this paper, we address an AMR scheduling and routing problem by dividing the entire problem into three sub-problems: path finding, vehicle routing, and conflict resolution. We first discuss the previous literature on characteristics of each sub-problem. We then present a comprehensive framework for minimising total tardiness of transportation requests with consideration of conflicts between routes. First, the shortest paths between all locations are calculated with A*. Based on the shortest paths, for vehicle routing, we propose a new local search algorithm called COntextual-Bandit-based Adaptive Local search with Tree-based regression (COBALT), which utilises the contextual bandit to select the best operator in consideration of contexts. After routing of AMRs, an agent-based model with states and protocols resolves collisions and deadlocks in a decentralised way. The results indicate that the proposed framework can improve the performance of AMR scheduling for conflict-free routes and that, especially for vehicle routing, COBALT outperforms the other algorithms in terms of average total tardiness.