Human-Aware Navigation Planner for Diverse Human-Robot Contexts
This work addresses the challenge of human-robot interaction in navigation for robots deployed in indoor and outdoor settings, representing an incremental improvement in adapting to various contexts.
The paper tackles the problem of enabling robots to navigate safely and effectively in diverse human environments by proposing a tunable human-aware navigation planner, and demonstrates its performance through simulations and real-world deployment on a robot.
As more robots are being deployed into human environments, a human-aware navigation planner needs to handle multiple contexts that occur in indoor and outdoor environments. In this paper, we propose a tunable human-aware robot navigation planner that can handle a variety of humanrobot contexts. We present the architecture of the planner and discuss the features and some implementation details. Then we present a detailed analysis of various simulated humanrobot contexts using the proposed planner along with some quantitative results. Finally, we show the results in a real-world scenario after deploying our system on a real robot.