Socially-Aware Navigation Using Non-Linear Multi-Objective Optimization

Abstract

For socially assistive robots (SAR)to be accepted into complex and stochastic human environments, it is important to account for subtle social norms. In this paper, we propose a novel approach to socially-aware navigation (SAN)which garnered an immense interest in the Human-Robot Interaction (HRI)community. We use a multi-objective optimization tool called the Pareto Concavity Elimination Transformation (PaC-cET)to capture the non-linear human navigation behavior, a novel contribution to the community. A candidate point on a trajectory is scored (1)for its progress towards the goal, and (2)based on autonomously-sensed distance-based features that capture the social norms and associated social costs. Rather than use a finely-tuned linear combination of these costs, we use PaCcET to select an optimized future trajectory point, associated with a non-linear combination of the costs. Existing research in this domain concentrates on geometric reasoning, model-based, and learning approaches, which have their own pros and cons. This approach is distinct from prior work in this area. We showed in a simulation that the PaCcET-based trajectory planner not only is able to avoid collisions and reach the intended destination in static and dynamic environments but also considers a human's personal space i.e. rules of proxemics in the trajectory selection process.