Beyond Shortsighted Navigation: Merging Best View Trajectory Planning with Robot Navigation
<|reference_start|>Beyond Shortsighted Navigation: Merging Best View Trajectory Planning with Robot Navigation: Gathering visual information effectively to monitor known environments is a key challenge in robotics. To be as efficient as human surveyors, robotic systems must continuously collect observational data required to complete their survey task. Inspection personnel instinctively know to look at relevant equipment that happens to be ``along the way.'' In this paper, we introduce a novel framework for continuous long-horizon viewpoint planning, for ground robots, applied to tasks involving patrolling, monitoring or visual data gathering in known environments. Our approach to Long Horizon Viewpoint Planning (LHVP), enables the robot to autonomously navigate and collect environmental data optimizing for coverage over the horizon of the patrol. Leveraging a quadruped's mobility and sensory capabilities, our LHVP framework plans patrol paths that account for coupling the viewpoint planner for the arm camera with the mobile base's navigation planner. The viewpath optimization algorithm seeks a balance between comprehensive environmental coverage and dynamically feasible movements, thus ensuring prolonged and effective operation in scenarios including monitoring, security surveillance, and disaster response. We validate our approach through simulations and in the real world and show that our LHVP significantly outperforms naive patrolling methods in terms of area coverage generating information-gathering trajectories for the robot arm. Our results indicate a promising direction for the deployment of mobile robots in long-term, autonomous surveying, and environmental data collection tasks, highlighting the potential of intelligent robotic systems in challenging real-world applications.<|reference_end|>
@article{tankasala2024beyond,
    title={Beyond Shortsighted Navigation: Merging Best View Trajectory Planning
  with Robot Navigation},
    author={Srinath Tankasala, Roberto Mart'in-Mart'in, Mitch Pryor},
    journal={arXiv preprint arXiv:2408.12513},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.12513},
    primaryClass={cs.RO}
}