Abstract:
This project proposes a reinforcement learning approach to collision avoidance and optimal trajectory planning for unmanned aerial vehicle (UAV) communication networks. Each UAV delivers objects forward and collects data from heterogeneous ground IoT devices backward.
Reinforcement learning helps UAVs avoid collisions without knowing other UAV trajectories. For each UAV, we use optimization theory to find the shortest backward path that collects data from all IoT devices.
We solve a no-return traveling salesman problem to find the best IoT device visiting order. We solve convex optimization problems to find line segments of an optimal backward path for heterogeneous ground IoT devices given a visiting order. Analytical and simulation results support our approach. The proposed approach outperforms several alternatives in simulations.
Note: Please discuss with our team before submitting this abstract to the college. This Abstract or Synopsis varies based on student project requirements.
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