Stochastic Collection and Replenishment (SCAR): Objective Functions
This work addresses the problem of enabling persistent autonomy for user agents in resource-limited scenarios, though it appears incremental as it focuses on improving computational efficiency for an existing framework.
The paper tackles the problem of computing expected cost in the Stochastic Collection and Replenishment (SCAR) scenario by introducing two objective functions: a Monte Carlo method and a faster analytical method using Gaussian approximations. The analytical method achieves over 99% accuracy compared to the Monte Carlo method with speed gains of several orders of magnitude.
This paper introduces two objective functions for computing the expected cost in the Stochastic Collection and Replenishment (SCAR) scenario. In the SCAR scenario, multiple user agents have a limited supply of a resource that they either use or collect, depending on the scenario. To enable persistent autonomy, dedicated replenishment agents travel to the user agents and replenish or collect their supply of the resource, thus allowing them to operate indefinitely in the field. Of the two objective functions, one uses a Monte Carlo method, while the other uses a significantly faster analytical method. Approximations to multiplication, division and inversion of Gaussian distributed variables are used to facilitate propagation of probability distributions in the analytical method when Gaussian distributed parameters are used. The analytical objective function is shown to have greater than 99% comparison accuracy when compared with the Monte Carlo objective function while achieving speed gains of several orders of magnitude.