Online Multi-Agent Pathfinding

Švancara, Jiří; Vlk, Marek; Stern, Roni; Atzmon, Dor; Barták, Roman

doi:10.1609/aaai.v33i01.33017732

Cited by 48 publications

(34 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…MAPF for mobile robotics is both a well-studied and dynamic developing topic. The usage scenarios of MAPF and their corresponding algorithm are diverse, such as warehouse [9], computer games [42], and autonomous driving in intersection [43]. Until the end of 2020, although the concept of reinforcement learning is attracting more and more attention, current influential research about the shortest path and MAPF is mainly based on graph theory.…”

Section: Mapf For Mobile Roboticsmentioning

confidence: 99%

An Extension of BIM Using AI: A Multi Working-Machines Pathfinding Solution

Xiang¹,

Liu

Su³

et al. 2021

IEEE Access

View full text Add to dashboard Cite

Multi working-machines pathfinding solution enables more mobile machines simultaneously to work inside of a working site so that the productivity can be expected to increase evolutionary. To date, the potential cooperation conflicts among construction machinery limit the amount of construction machinery investment in a concrete working site. To solve the cooperation problem, civil engineers optimize the working site from a logistic perspective while computer scientists improve pathfinding algorithms' performance on the given benchmark maps. In the practical implementation of a construction site, it is sensible to solve the problem with a hybrid solution; therefore, in our study, we proposed an algorithm based on a cutting-edge multi-pathfinding algorithm to enable the massive number of machines cooperation and offer the advice to modify the unreasonable part of the working site in the meantime. Using the logistic information from BIM, such as unloading and loading point, we added a pathfinding solution for multi machines to improve the whole construction fleet's productivity. In the previous study, the experiments were limited to no more than ten participants, and the computational time to gather the solution was not given; thus, we publish our pseudo-code, our tested map, and benchmark our results. Our algorithm's most extensive feature is that it can quickly replan the path to overcome the emergency on a construction site.

show abstract

Section: Mapf For Mobile Roboticsmentioning

confidence: 99%

An Extension of BIM Using AI: A Multi Working-Machines Pathfinding Solution

Xiang¹,

Liu

Su³

et al. 2021

IEEE Access

View full text Add to dashboard Cite

show abstract

“…These agents plan to reach their goals from their starting positions while avoiding conflicts among themselves and with other agents that exist in environment [1]. In crowded environments, the MAPF problems are a challenging problem and have attracted the attention of many research groups in recent years [2,3].…”

Section: Introductionmentioning

confidence: 99%

AB-Mapper: Attention and BicNet Based Multi-agent Path Finding for Dynamic Crowded Environment

Guan,

Gao,

Zhao

et al. 2021

Preprint

View full text Add to dashboard Cite

Multi-agent path finding in dynamic crowded environments is of great academic and practical value for multirobot systems in the real world. To improve the effectiveness and efficiency of communication and learning process during path planning in dynamic crowded environments, we introduce an algorithm called Attention and BicNet based Multiagent path planning with effective reinforcement (AB-Mapper) under the actor-critic reinforcement learning framework. In this framework, on the one hand, we utilize the BicNet with communication function in the actor-network to achieve intra team coordination. On the other hand, we propose a centralized critic network that can selectively allocate attention weights to surrounding agents. This attention mechanism allows an individual agent to automatically learn a better evaluation of actions by also considering the behaviours of its surrounding agents. Compared with the state-of-the-art method Mapper, our AB-Mapper is more effective (85.86% vs. 81.56% in terms of success rate) in solving the general path finding problems with dynamic obstacles. In addition, in crowded scenarios, our method outperforms the Mapper method by a large margin, reaching a stunning gap of more than 40% for each experiment.

show abstract

“…Since the agents do not disappear once they reach their locations, R-MAPF may not be always solvable even when the corresponding D-MAPF is solvable [31]. For that reason, we introduce an algorithm for D-MAPF, based on our solution to R-MAPF using ASP.…”

Section: Solving D-mapf Using Aspmentioning

confidence: 99%

“…Note that our experiments involve relatively large instances (in terms of grid size, number of agents, and obstacle occupancy) comparable with the ones reported in the related work. For instance, the evaluation of online MAPF [31] is performed over grids of sizes 3x5 (with at most 30% obstacle occupancy) and 3x10 (with at most 40% obstacle occupancy) with 10-25 new agents, and a grid of size 16x16 (with 39 % obstacle occupancy) with 60-70 new agents. Table 2: Experimental results on a 20x20 grid without obstacles, with block obstacles as in Figure 7(a), and with a cross obstacle as in Figure 7(b).…”

Section: Experimental Evaluationsmentioning

confidence: 99%

“…Online MAPF. Online MAPF [31] considers the addition of new agents to the team while a plan is being executed, but no other changes in the team or environment (e.g., no existing agent leaves, no new obstacle appears). Moreover, it is assumed that agents disappear when they reach their goal and that new agents may wait before entering their initial location in the environment.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A Declarative Method for Dynamic Multi-Agent Path Finding

Boğatarkan¹,

Patoğlu²,

Erdem³

EPiC Series in Computing

View full text Add to dashboard Cite

The multi-agent path finding (MAPF) problem is a combinatorial search problem that aims at finding paths for multiple agents such that no two agents collide with each other. We study a dynamic variant of MAPF, called D-MAPF, which allows changes in the environment (e.g., some existing obstacles may be removed from the environment or moved to some other location, or new obstacles may be included in the environment), and/or changes in the team (e.g., some existing agents may leave and some new agents may join the team) at different times. We introduce a new method to solve D-MAPF, using answer set programming.

show abstract

Online Multi-Agent Pathfinding

Cited by 48 publications

References 10 publications

An Extension of BIM Using AI: A Multi Working-Machines Pathfinding Solution

An Extension of BIM Using AI: A Multi Working-Machines Pathfinding Solution

AB-Mapper: Attention and BicNet Based Multi-agent Path Finding for Dynamic Crowded Environment

A Declarative Method for Dynamic Multi-Agent Path Finding

Contact Info

Product

Resources

About