Intent Prediction-Driven Model Predictive Control for UAV Planning and Navigation in Dynamic Environments
<|reference_start|>Intent Prediction-Driven Model Predictive Control for UAV Planning and Navigation in Dynamic Environments: The emergence of indoor aerial robots holds significant potential for enhancing construction site workers' productivity by autonomously performing inspection and mapping tasks. The key challenge to this application is ensuring navigation safety with human workers. While navigation in static environments has been extensively studied, navigating dynamic environments remains open due to challenges in perception and planning. Payload limitations of unmanned aerial vehicles limit them to using cameras with limited fields of view, resulting in unreliable perception and tracking during collision avoidance. Moreover, the unpredictable nature of the dynamic environments can quickly make the generated optimal trajectory outdated. To address these challenges, this paper presents a comprehensive navigation framework that incorporates both perception and planning, introducing the concept of dynamic obstacle intent prediction. Our perception module detects and tracks dynamic obstacles efficiently and handles tracking loss and occlusion during collision avoidance. The proposed intent prediction module employs a Markov Decision Process (MDP) to forecast potential actions of dynamic obstacles with the possible future trajectories. Finally, a novel intent-based planning algorithm, leveraging model predictive control (MPC), is applied to generate safe navigation trajectories. Simulation and physical experiments demonstrate that our method enables safe navigation in dynamic environments and achieves the fewest collisions compared to benchmarks.<|reference_end|>
@article{xu2024intent,
    title={Intent Prediction-Driven Model Predictive Control for UAV Planning and
  Navigation in Dynamic Environments},
    author={Zhefan Xu, Hanyu Jin, Xinming Han, Haoyu Shen, Kenji Shimada},
    journal={arXiv preprint arXiv:2409.15633},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.15633},
    primaryClass={cs.RO}
}