Shashwat Pandey,
- Student, Department of Computer Science Engineering, Babu Banarasi Das University, Uttar Pradesh, India
Abstract
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 ([email protected]) 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: autonomous exploration, YOLO V8 object detection, swarm robotics, environmental sensing, hybrid UAV-UGV system, bio-inspired path planning, real-time terrain mapping, GPS-IMU fusion, decentralized navigation, multi-agent systems
[This article belongs to International Journal of Mechanical Dynamics and Systems Analysis ]
Shashwat Pandey. An Integrated Autonomous Rover-Drone System for Intelligent Exploration and Environmental Monitoring. International Journal of Mechanical Dynamics and Systems Analysis. 2025; 03(02):42-59.
Shashwat Pandey. An Integrated Autonomous Rover-Drone System for Intelligent Exploration and Environmental Monitoring. International Journal of Mechanical Dynamics and Systems Analysis. 2025; 03(02):42-59. Available from: https://journals.stmjournals.com/ijmdsa/article=2025/view=230891
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| Volume | 03 |
| Issue | 02 |
| Received | 23/09/2025 |
| Accepted | 31/10/2025 |
| Published | 10/11/2025 |
| Publication Time | 48 Days |
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