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Title: Socially-Aware Navigation Using Non-Linear Multi-Objective Optimization
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 (PaCcET) to capture the non-linear human navigation behavior, a novel contribution to the community. We use autonomously sensed distance-based features that captures the social norms and associated social costs for a given trajectory point towards the goal. 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 in the trajectory selection process.  more » « less
Award ID(s):
1719027
NSF-PAR ID:
10072573
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
Proceedings of the International Conference on Intelligent Robots and Systems
ISSN:
2153-0866
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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