Energy-efficient animal tracking with multi-unmanned aerial vehicle path planning using reinforcement learning and wireless sensor networks.
Senol ErgunsahVedat TümenSelahattin KosunalpKubilay DemirPublished in: Concurr. Comput. Pract. Exp. (2023)
Keyphrases
- unmanned aerial vehicles
- energy efficient
- path planning
- wireless sensor networks
- reinforcement learning
- multi core architecture
- mobile robot
- energy consumption
- path planning algorithm
- sensor networks
- collision avoidance
- data aggregation
- obstacle avoidance
- aerial vehicles
- autonomous systems
- base station
- motion planning
- sensor nodes
- dynamic environments
- multi robot
- data dissemination
- data gathering
- multi hop
- energy efficiency
- data transmission
- routing algorithm
- optimal path
- multiple robots
- multi agent
- control system
- autonomous vehicles
- robot control
- degrees of freedom
- routing protocol
- cluster head
- state space
- visual tracking
- power consumption
- particle filter
- real time