Planning under non-rational perception of uncertain spatial costs

Abstract

This work investigates the design of motionplanning strategies that can incorporate non-rational perception of risks associated with uncertain spatial costs. Our proposed method employs the Cumulative Prospect Theory (CPT) model to generate a perceived risk function across a given environment, which is scalable to high dimensional space. Using this, CPT-like perceived risks and path-length metrics are combined to define a cost function that is compliant with the requirements of asymptotic optimality of sampling-based motion planners (RRT*). The modeling capabilities of CPT are demonstrated in simulation by producing a rich set of meaningful paths, capturing a range of different risk perceptions in a custom environment. Furthermore, using a simultaneous perturbation stochastic approximation (SPSA) method, we investigate the capacity of these CPT-based risk-perception planners to approximate arbitrary paths drawn in the environment. We compare this adaptability with Conditional Value at Risk (CVaR), another popular risk perception model. Our simulations show that CPT is richer and able to capture a larger class of paths as compared to CVaR and expected risk in our setting.

Risk-Aware Motion Planning in Partially Known Environments

“…In our work we employ risk metrics, such as CVaR [4], in order to take into consideration less likely -but probably dangerous -tail events of the distribution returned by GP regression, and use that to construct a risk-aware cost function. [5], [6] provide approaches to motion planning on complex cost spaces, and [7] to uncertain cost spaces. Neither of them, however, deal with a dynamic cost function.…”