An Integrated Autonomous Rover-Drone System for Intelligent Exploration and Environmental Monitoring - Shashwat Pandey (International Journal of Mechanical Dynamics and Systems Analysis, 2025)
This paper presents a hybrid autonomous exploration platform integrating a ground rover and aerial drone, enhanced by swarm intelligence and a custom-trained YOLO V8 object detection model. The rover is equipped with GPS, IMU, and environmental sensors (DHT11, MQ135, BMP180), while the drone performs real-time aerial mapping and obstacle prediction. A YOLO V8 model, trained on 500 annotated terrain images (six classes: rocks, pits, trees, water, animals, vegetation), achieves a mean average precision (mAP@0.5) of 72.3% with a 42 ms/frame inference speed. The system employs Ant Colony Optimization and Kalman filtering to enable decentralized path planning and trajectory correction. Swarm coordination across rover-drone units achieved 95.8% sync accuracy, reducing exploration time by 18% compared to static waypoints. Aerial-ground fusion improved GPS localization accuracy by 14%. This paper details the system architecture, experimental validations, and proposes future enhancements including solar-assisted energy, edge-based planning, and 5G telemetry integration for long-range deployments.
Keywords: GPS-IMU fusion; YOLO V8 object detection; autonomous exploration; bio-inspired path planning; decentralized navigation; environmental sensing; hybrid UAV-UGV system; multi-agent systems; real-time terrain mapping; swarm robotics