Activity Planning for Assistive Robots Using Chance-Constrained Stochastic Programming

In this article, we present a framework for planning an activity to be executed with the support of a robotic navigation assistant. The two main components are the activity and the motion planner. The activity planner composes a sequence of abstract activities, chosen from a given set, to synthesize a plan. Each activity is associated with a point of interest in the environment and with probabilistic parameters that depend on the plan, which are characterized by simulations in realistic scenarios. The low-level action to pass from an activity to the next is handled by the motion planner, which secures the physical feasibility of the chosen actions and their compatibility with the constraints posed by the user and the environment. Indeed, the final plan must respect the user constraints and optimise his/her satisfaction from the activity. We show a possible model for the problem as a chance constrained optimization along with an efficient technique to find high-quality solutions.

Automated summary

This article presents a framework for planning activities with a robotic navigation assistant, focusing on activity and motion planners. The activity planner composes abstract activities and probabilistic parameters to synthesize a plan, while the motion planner ensures physical feasibility and compatibility with user and environment constraints. The final plan aims to respect user constraints and optimize satisfaction.