Abstract
The article proposes a new path planning method for a multi-robot system for transportation with various loading conditions. For a given system, one needs to distribute given pickup and delivery jobs to the robots and find a path for each robot while minimizing the sum of travel costs. The system has multiple robots with different payloads. Each job has a different required minimum payload, and as a result, job distribution in this situation must take into account the difference in payload capacities of robots. By reflecting job handling restrictions and job accomplishment costs in travel costs, the problem is formulated as a multiple heterogeneous asymmetric Hamiltonian path problem and a primal-dual based heuristic is developed to solve the problem. The heuristic produces a feasible solution in relatively short amount of time and verified by the implementation results.
Highlights
For automation of transportation, the choice of the right automated material handling system is critical for its efficiency
Since the modern production design departed from conveyor systems to avoid its limitation in mobility, mobile robots, including automated guided vehicles, are one of the popular material delivery systems that have been utilized by the factories
One needs to consider: (1) dispatching, which assigns the jobs to robots and find an optimal sequence; (2) routing, which finds an optimal path for each robot with a given sequence; and (3) scheduling, which determines arrival and departure time of robots at each node in the workspace
Summary
The choice of the right automated material handling system is critical for its efficiency. The problem addressed in this article is a generalization of traveling salesman problem (TSP) and NP-Hard.[2] Because we are dealing with a system with multiple robots having different payloads that originate from distinctive depots, the problem can be considered as a multiple depot heterogeneous asymmetric HPP. There is lack of literature that addresses multiple HPP that considers functional heterogeneity of vehicles (which is different payloads in this article) and asymmetric travel costs at the same time. We propose a heuristic for dispatching based on the primal-dual technique and combined with a heuristic for routing This allows us to focus on efficient job assignment, which decides how many and which robots should be used in what sequence to minimize the sum of travel costs. The computational results are discussed in the section on implementation and we conclude in the last section
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