Stochastic Collection and Replenishment (SCAR) Optimisation for Persistent Autonomy
This work addresses the challenge of maintaining robot operations in the field by optimizing replenishment schedules, but it is incremental as it builds on existing combinatorial optimization methods.
The paper tackled the problem of optimizing a single replenishment agent's schedule to refuel or recharge robots for persistent autonomy, showing that a ratio objective function outperforms total weighted tardiness, especially with shorter schedules, and highlighting the importance of incorporating uncertainty.
Robots have a finite supply of resources such as fuel, battery charge, and storage space. The aim of the Stochastic Collection and Replenishment (SCAR) scenario is to use dedicated agents to refuel, recharge, or otherwise replenish robots in the field to facilitate persistent autonomy. This paper explores the optimisation of the SCAR scenario with a single replenishment agent, using several different objective functions. The problem is framed as a combinatorial optimisation problem, and A* is used to find the optimal schedule. Through a computational study, a ratio objective function is shown to have superior performance compared with a total weighted tardiness objective function, with a greater performance advantage present when using shorter schedule lengths. The importance of incorporating uncertainty in the objective function used in the optimisation process is also highlighted, in particular for scenarios in which the replenishment agent is under- or fully-utilised.