Kong et al., 2023 - Google Patents
Flocking with obstacle avoidance for fixed-wing unmanned aerial vehicles via nonlinear model predictive controlKong et al., 2023
View PDF- Document ID
- 11646812676826294206
- Author
- Kong F
- Chen H
- Li H
- Yan J
- Wang X
- Fang J
- Publication year
- Publication venue
- 2023 42nd Chinese control conference (CCC)
External Links
Snippet
Inspired by biological swarms, collective motions such as flocking of multiple agents can be generated by local interactions. Nevertheless, most of the current swarm models cannot ensure safe flight for fixed-wing unmanned aerial vehicles (UAVs) in obstacle-dense …
Classifications
-
- 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
- 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
- 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
- G05D1/104—Simultaneous control of position or course in three dimensions specially adapted for aircraft involving a plurality of aircrafts, e.g. formation flying
-
- 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
- G05D1/0268—Control of position or course in two dimensions specially adapted to land vehicles using internal positioning means
-
- 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
- G05D1/0027—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot associated with a remote control arrangement involving a plurality of vehicles, e.g. fleet or convoy travelling
-
- 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
- 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
- G05D1/0231—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/0265—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
- G05B13/027—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion using neural networks only
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Wu et al. | Formation obstacle avoidance: A fluid-based solution | |
| Qi et al. | Formation tracking and obstacle avoidance for multiple quadrotors with static and dynamic obstacles | |
| Li et al. | A survey on formation control algorithms for multi-AUV system | |
| CN115509251A (en) | Multi-UAV multi-target cooperative tracking control method based on MAPPO algorithm | |
| CN113759935B (en) | Intelligent group formation mobile control method based on fuzzy logic | |
| Tran et al. | Switching formation strategy with the directed dynamic topology for collision avoidance of a multi‐robot system in uncertain environments | |
| Fan et al. | Formation control of multiple unmanned surface vehicles using the adaptive null-space-based behavioral method | |
| CN114661066B (en) | An intelligent obstacle avoidance method for drone swarm based on reinforcement learning | |
| Kumar et al. | Emergent formations of a Lagrangian swarm of unmanned ground vehicles | |
| Chen et al. | Extrinsic-and-intrinsic reward-based multi-agent reinforcement learning for multi-UAV cooperative target encirclement | |
| Varma et al. | Robotic vision based obstacle avoidance for navigation of unmanned aerial vehicle using fuzzy rule based optimal deep learning model | |
| CN116300905A (en) | A Constrained Multi-robot Reinforcement Learning Safe Formation Method Based on 2D Laser Observation | |
| Tang et al. | A Distributed Autonomous System for Multi-UAVs With Limited Visualization: Employing Dual-Horizon NMPC Controller | |
| Gudeta et al. | Consensus-based distributed collective motion of swarm of quadcopters | |
| Kong et al. | Flocking with obstacle avoidance for fixed-wing unmanned aerial vehicles via nonlinear model predictive control | |
| Zhang et al. | Cooperative obstacle avoidance of unmanned system swarm via reinforcement learning under unknown environments | |
| Khan et al. | Swarmpath: Drone swarm navigation through cluttered environments leveraging artificial potential field and impedance control | |
| Selje et al. | GPS-denied three dimensional leader-follower formation control using deep reinforcement learning | |
| Regier et al. | Improving navigation with the social force model by learning a neural network controller in pedestrian crowds | |
| Deng et al. | Multi-robot dynamic formation path planning with improved polyclonal artificial immune algorithm | |
| Essghaier et al. | Co-leaders and a flexible virtual structure based formation motion control | |
| Zhang et al. | Research on multiple quadrotor UAV formation obstacle avoidance based on finite-time consensus | |
| Fu et al. | Multi-UAV formation control method based on modified artificial physics | |
| Brandstätter et al. | Multi-agent spatial predictive control with application to drone flocking (extended version) | |
| do Nascimento | Coordinated multi-robot formation control |