Abstract:
Motion planning is crucial for helping autonomous robots navigate complex
environments efficiently. Recently, Augmented Reality (AR) has been
introduced to improve Human-Robot Interaction (HRI) in mobile robot
motion planning. However, AR gap-based reactive control systems often
suffer from issues like sensor noise and inaccuracies, leading to higher levels
of jerk and stress. On the other hand, Simultaneous Localization and Mapping
(SLAM) provides a global understanding of the environment, ensuring robust
navigation even in dynamic or unfamiliar areas. In this paper, we propose an
AR-based Hector Simultaneous Localization and Mapping (SLAM) method
for intuitive indoor mobile robot navigation that reduces jerk and stress. Our
approach uses AR to set navigation goals and provide visual markers for the
user, while SLAM ensures accurate real-time mapping for precise navigation
and obstacle avoidance. For path planning, the robot uses Dijkstra's algorithm
for global planning and Trajectory Rollout for local planning. We tested the
effectiveness of our AR-based Hector SLAM in three different scenarios and
compared the results with an admissible gap-based navigation algorithm.
Experimental results showed that our method improved jerk and stress by
11.63% and 11.39% respectively, leading to smoother and safer trajectories.
