US 12,494,137 B1
Energy-efficient path planning system and method for internet of drones using reinforcement learning
Gamil Abdullah Mohsen Ahmed, Dhahran (SA); Zainab Saleh Mohammed Almania, Dammam (SA); Tarek Rahil Omar Sheltami, Dhahran (SA); Ashraf Sharif Hasan Mahmoud, Dhahran (SA); and Abdulaziz Yagoub Mahmoud Barnawi, Dhahran (SA)
Assigned to KING FAHD UNIVERSITY OF PETROLEUM AND MINERALS, Dhahran (SA)
Filed by KING FAHD UNIVERSITY OF PETROLEUM AND MINERALS, Dhahran (SA)
Filed on Apr. 11, 2025, as Appl. No. 19/177,320.
Int. Cl. G08G 5/57 (2025.01); G06N 3/006 (2023.01); G06N 3/092 (2023.01)
CPC G08G 5/57 (2025.01) [G06N 3/006 (2013.01); G06N 3/092 (2023.01)] 16 Claims
OG exemplary drawing
 
1. A path planning system for an unmanned aerial vehicle in a network of unmanned aerial vehicles, comprising:
a plurality of the unmanned aerial vehicles (UAVs);
a first processing circuitry configured with
a particle swarm optimization component configured to offline generate paths for each of the UAVs by particle swarm optimization (PSO) to minimize path length and avoid static obstacles; and
a second processing circuitry configured with, for each UAV of the plurality of UAVs,
a deep reinforcement learning (RL)-based planner component configured to perform real-time path planning to navigate the UAV through dynamic environmental conditions using a particular path generated by the PSO for the UAV as a consistent reference for the UAV, and
a reward component to calculate a reward as part of the path planning by the deep RL-based planner component to determine potential paths and converging to an optimal path for the UAV,
wherein the PSO is configured with an initialization stage in which chaos-based particles utilize a logistic map to obtain an initialization formation, and
wherein the PSO is configured to replace inactive particles with fresh particles, such that the RL-based planner converges towards a global optimum rather than getting stuck in a local optimum.