Offline Time-Independent Multi-Agent Path Planning

Abstract

A large number of emergency humanitarian rescue demands in conflict areas around the world are accompanied by intentional, persistent and unpredictable attacks on rescuers and supplies. Unfortunately, existing work on humanitarian relief planning mostly ignores this challenge in reality resulting a parlous and short-sighted relief distribution plan to a large extent. To address this, we first propose an offline multi-stage optimization problem of emergency relief planning under intentional attacks, in which all parameters in the game between the rescuer and attacker are supposed to be known or predictable. Then, an online version of this problem is introduced to meet the need of online and irrevocable decision making when those parameters are revealed in an online fashion. To achieve a far-sighted emergency relief planning under attacks, we design an online learning approach which is proven to obtain a near-optimal solution of the offline problem when those online reveled parameters are i.i.d. sampled from an unknown distribution. Finally, extensive experiments on a real anti-Ebola relief planning case based on the data of Ebola outbreak and armed attacks in DRC Congo show the scalability and effectiveness of our approach.

A Comprehensive Review on Leveraging Machine Learning for Multi-Agent Path Finding

“…These advancements have not only improved planning capabilities but also expanded the research scope of MAPF in related technological fields. This includes going beyond the simplified assumption of uniform travel time between vertices [30]- [32], extending MAPF to various robotic systems like manipulators [33]- [35], moving beyond the typical one-shot planning problems in MAPF [2], [36], [37], joint planning with target assignments [38]- [41], and executing MAPF solutions with actual robots under timing uncertainties [42]- [45], among others. This evolution broadens the horizons of MAPF technologies, making them increasingly realistic and practical for implementing efficient multi-agent navigation.…”

“…Okumura et al [34] takes a holistic view of solving multiagent navigation, which transfers to diverse environments and agent kinematics, such as ground robots of different sizes and robotic arms in 3D space. In essence, the work proposed an algorithm combining the gradual development of roadmaps and motion planning for multiple agents, aiming to balance the representation issue in Figure 2.…”

“…All these studies [34], [96], [99] in MAPF techniques and inspire the advancement of more sophisticated methods, detailed in the following section.…”

Offline Time-Independent Multiagent Path Planning

“…OTIMAPP was originally presented in our preliminary paper [20]. In this article, in addition to improving the description and presentation of OTIMAPP, the following major differences are discussed.…”

Intractability of Optimal Multi-Agent Pathfinding on Directed Graphs

“…In a multi-agent pathfinding (MAPF) problem, multiple autonomous agents (e.g., robots in a warehouse) move simultaneously, searching and planning for paths to their respective destinations without colliding with each other [19,16]. The problem is receiving increasing attention in both research and application in recent years [13,11]. Relevance of MAPF can be found in several important problems/applications in the areas of robotics (e.g., in the coordination of autonomous drones or mobile robots in a factory), Operations Research (e.g., for optimizing transportation of goods and personnel in logistics and supply chain management), and AI planning [12,2,18,15,6,14].…”