arXiv:2606.03441v1 Announce Type: cross Abstract: Autonomous vision-based perching of quadrotors on moving inclined platforms is critical for air-ground collaboration but remains challenging due to the limited field of view (FOV). In this paper, we propose PerchRL, a reinforcement learning (RL) framework for vision-based agile perching on inclined platforms under rapid and irregular motion. Specifically, we employ a two-stage learning strategy consisting of state-based pre-training followed by vision-based fine-tuning. To improve generalization across diverse platform motions, we employ random
Advances in reinforcement learning and computer vision are enabling more sophisticated autonomous robotic capabilities, particularly for dynamic environments.
This development in autonomous perching for quadrotors addresses a critical challenge for air-ground collaboration, enhancing operational versatility and endurance for unmanned systems.
Quadrotors will gain significantly improved capabilities for extended missions, covert operations, and deployment in unstable or unstructured environments.
- · Defence contractors
- · Logistics companies
- · Search and rescue organizations
- · Robotics research institutions
- · Manual drone operators in complex environments
Enhanced autonomous drone capabilities, particularly for long-duration missions and difficult terrains.
Increased adoption of drone fleets for surveillance, inspection, and delivery in challenging operational contexts.
Integration of advanced perching and AI into a new generation of multi-modal robotic systems for complex tasks.
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