SC-M: A Multi-Agent Path Planning Algorithm with Soft-Collision Constraint on Allocation of Common Resources

• Published in: Applied sciences 2019-10, Vol.9 (19), p.4037
• Main Authors: Shi, Rongye, Steenkiste, Peter, Veloso, Manuela
• Format: Article
• Language: English
• Subjects: Algorithms; Automation; Autonomous vehicles; collision-free constraint; Constraint modelling; m algorithm; multi-agent systems; optimality and completeness; Path planning; Planning; Public transportation; Resource allocation; shortest path finding; Solution space; Traffic control; Traffic engineering; Traffic planning; Traffic surveillance
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app9194037

Multi-agent path planning (MAPP) is increasingly being used to address resource allocation problems in highly dynamic, distributed environments that involve autonomous agents. Example domains include surveillance automation, traffic control and others. Most MAPP approaches assume hard collisions, e.g., agents cannot share resources, or co-exist at the same node or edge. This assumption unnecessarily restricts the solution space and does not apply to many real-world scenarios. To mitigate this limitation, this paper introduces a more general class of MAPP problems—MAPP in a soft-collision context. In soft-collision MAPP problems, agents can share resources or co-exist in the same location at the expense of reducing the quality of the solution. Hard constraints can still be modeled by imposing a very high cost for sharing. This paper motivates and defines the soft-collision MAPP problem, and generalizes the widely-used M* MAPP algorithm to support the concept of soft-collisions. Soft-collision M* (SC-M*) extends M* by changing the definition of a collision, so paths with collisions that have a quality penalty below a given threshold are acceptable. For each candidate path, SC-M* keeps track of the reduction in satisfaction level of each agent using a collision score, and it places agents whose collision scores exceed its threshold into a soft-collision set for reducing the score. Our evaluation shows that SC-M* is more flexible and more scalable than M*. It can also handle complex environments that include agents requesting different types of resources. Furthermore, we show the benefits of SC-M* compared with several baseline algorithms in terms of path cost, success rate and run time.