Wang et al., 2021 - Google Patents
Fast path planning for unmanned aerial vehicles by self-correction based on q-learningWang et al., 2021
- Document ID
- 12066791907453900081
- Author
- Wang Z
- Yang H
- Wu Q
- Zheng J
- Publication year
- Publication venue
- Journal of Aerospace Information Systems
External Links
Snippet
This paper addresses a path planning problem for unmanned aerial vehicles with correcting position errors through correction-point navigation, which requires a rapid response when determining the flight path. A two-layer nested iterative hybrid algorithm based on Q learning …
- 238000005457 optimization 0 abstract description 19
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0287—Control of position or course in two dimensions specially adapted to land vehicles involving a plurality of land vehicles, e.g. fleet or convoy travelling
- G05D1/0291—Fleet control
- G05D1/0295—Fleet control by at least one leading vehicle of the fleet
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/0011—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot associated with a remote control arrangement
- G05D1/0044—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot associated with a remote control arrangement by providing the operator with a computer generated representation of the environment of the vehicle, e.g. virtual reality, maps
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/04—Inference methods or devices
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/02—Knowledge representation
- G06N5/022—Knowledge engineering, knowledge acquisition
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/10—Simultaneous control of position or course in three dimensions
- G05D1/101—Simultaneous control of position or course in three dimensions specially adapted for aircraft
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0255—Control of position or course in two dimensions specially adapted to land vehicles using acoustic signals, e.g. ultra-sonic singals
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/0094—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot involving pointing a payload, e.g. camera, weapon, sensor, towards a fixed or moving target
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Duan et al. | Dynamic discrete pigeon-inspired optimization for multi-UAV cooperative search-attack mission planning | |
Yijing et al. | Q learning algorithm based UAV path learning and obstacle avoidence approach | |
Sharma et al. | Path planning for multiple targets interception by the swarm of UAVs based on swarm intelligence algorithms: A review | |
Capitan et al. | Decentralized multi-robot cooperation with auctioned POMDPs | |
Mahmoud Zadeh et al. | A novel versatile architecture for autonomous underwater vehicle’s motion planning and task assignment | |
Khalil et al. | FED-UP: Federated deep reinforcement learning-based UAV path planning against hostile defense system | |
Huang | A Novel Three‐Dimensional Path Planning Method for Fixed‐Wing UAV Using Improved Particle Swarm Optimization Algorithm | |
Levine et al. | Information-theoretic motion planning for constrained sensor networks | |
Liang et al. | Multi-UAV autonomous collision avoidance based on PPO-GIC algorithm with CNN–LSTM fusion network | |
Ramasamy et al. | A heuristic learning algorithm for preferential area surveillance by unmanned aerial vehicles | |
Xiang et al. | An effective memetic algorithm for UAV routing and orientation under uncertain navigation environments | |
Wang et al. | Fast path planning for unmanned aerial vehicles by self-correction based on q-learning | |
Cao et al. | 3D trajectory planning based on the Rapidly-exploring Random Tree–Connect and artificial potential fields method for unmanned aerial vehicles | |
Zu et al. | Research on UAV path planning method based on improved HPO algorithm in multi-task environment | |
Xie et al. | Mathematical problems in engineering improved CNP‐Method‐Based local real‐time cooperative task allocation of heterogeneous multi‐UAV in communication‐constrained environment | |
Yang et al. | Learning graph-enhanced commander-executor for multi-agent navigation | |
Khalil et al. | A hybrid modified ABC-PSO algorithm for optimal robotic path planner | |
Luan et al. | Path planning of unmanned surface vehicle based on artificial potential field approach considering virtual target points | |
Zhai et al. | Real‐Time Task Allocation of Heterogeneous Unmanned Aerial Vehicles for Search and Prosecute Mission | |
Wei et al. | UCAV formation online collaborative trajectory planning using hp adaptive pseudospectral method | |
Ma et al. | Receding horizon control with extended solution for UAV path planning | |
Zhou et al. | Multi‐UAVs Formation Autonomous Control Method Based on RQPSO‐FSM‐DMPC | |
Jotrao et al. | Time-Constrained UAV Path Planning in 3D Network for Maximum Information Gain | |
Thoma et al. | Prioritising paths: An improved cost function for local path planning for UAV in medical applications | |
Zhu et al. | Research on AGV path tracking method based on global vision and reinforcement learning |