Abstract
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 problemsâMAPP 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.
Highlights
Multi-agent path planning (MAPP) involves finding the set of least-cost paths for a set of agents co-existing in a given graph such that each of the agents is free from collision, where a collision is defined as at least two agents moving to the same location at the same time
We develop a generalized version of the M* algorithm, called soft-collision M* (SC-M*), for solving the MAPP problem in the soft-collision context
Given the fact that we show that SC-M* is superior to other alternative SC-based MAPP solvers (e.g., SC-A* and SC-Conflict-Based Search (CBS)) in terms of scalability, run time, and path cost, we demonstrate that the proposed method, which is adjusted to MAPP in the soft-collision context, is a powerful tool in practice
Summary
Multi-agent path planning (MAPP) involves finding the set of least-cost paths for a set of agents co-existing in a given graph such that each of the agents is free from collision, where a collision is defined as at least two agents moving to the same location at the same time. The M* algorithm is a state-of-the-art coupled approach It starts with decoupled planning and applies a strategy called sub-dimensional expansion to dynamically increase the dimensionality of the search space in regions in which agent collisions occur. This paper relaxes the hard collisions constraint by allowing some sharing of resources, including space and various services on edges/nodes, by agents Such sharing reduces the quality of the path, i.e., the satisfaction level of the agent using it, but as long as the quality reduction for each path is below a settable threshold, the solution is acceptable. SC-M* tracks the collision score of each agent and places agents whose collision scores exceed certain thresholds into a soft-collision set for sub-dimensional expansion, a technique that limits the search space while maintaining the optimality of the algorithm with respect to the objective
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