A Regularized Quadratic Programming Approach to Real-Time Scheduling of Autonomous Mobile Robots in a Prioritized Task Space

The use of Autonomous Mobile Robots (AMRs) for fast and efficient manufacturing has attracted the interest of academia and industry in recent times, especially due to significant improvements in computational efficiency. However, one of the biggest challenges in terms of controls is the optimal task assignment and scheduling of AMRs in order to finish the assigned tasks as quickly as possible, taking into account the priority of the tasks. This paper focuses on the single-AMR scheduling problem, i.e., once each AMR is assigned a set of tasks, the objective is to order these tasks efficiently while considering task priorities. The need for real-time algorithms to solve this problem renders exhaustive search algorithms inappropriate, since their focus is on the accuracy of the solution without considering time constraints. This paper proposes a gradient-based real-time approach for the scheduling problem based on a mathematical formulation in the structure of a regularized quadratic program. This scheduling algorithm is shown to perform better than a simulated annealing based pairwise exchange technique, which is a commonly used heuristic method, in terms of a defined cost metric. Therefore, the proposed algorithm allows for the generation of efficient realtime solutions to the scheduling problem for a single AMR.