[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Jiang et al., 2019 - Google Patents

A probabilistic decision engine for navigation of autonomous vehicles under uncertainty

Jiang et al., 2019

Document ID
10444629763801958315
Author
Jiang Z
Raziei S
Publication year
Publication venue
2019 IEEE National Aerospace and Electronics Conference (NAECON)

External Links

Snippet

Navigation or route planning under uncertainty is a very challenging but important task for autonomous vehicles such as self-driving cars, drones, unmanned aerial systems (UAS), etc. Probabilistic methods to the modeling and optimization of these systems are attractive in …
Continue reading at ieeexplore.ieee.org (other versions)

Classifications

    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/0265Adaptive 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
    • G05B13/027Adaptive 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
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04Adaptive 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
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass
    • G06N99/005Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05DSYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
    • G05D1/02Control of position or course in two dimensions
    • G05D1/021Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0287Control of position or course in two dimensions specially adapted to land vehicles involving a plurality of land vehicles, e.g. fleet or convoy travelling
    • G05D1/0291Fleet control
    • G05D1/0295Fleet control by at least one leading vehicle of the fleet
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00Computer systems utilising knowledge based models
    • G06N5/04Inference methods or devices
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05DSYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
    • G05D1/10Simultaneous control of position or course in three dimensions
    • G05D1/101Simultaneous control of position or course in three dimensions specially adapted for aircraft
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00Computer systems based on specific mathematical models
    • G06N7/005Probabilistic networks
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05DSYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
    • G05D1/0011Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot associated with a remote control arrangement
    • G05D1/0044Control 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
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00Program-control systems
    • G05B2219/30Nc systems

Similar Documents

Publication Publication Date Title
Yijing et al. Q learning algorithm based UAV path learning and obstacle avoidence approach
Choi et al. Unmanned aerial vehicles using machine learning for autonomous flight; state-of-the-art
Kim et al. Probabilistic vehicle trajectory prediction over occupancy grid map via recurrent neural network
Murphey et al. Cooperative control and optimization
Al-Hasan et al. Intelligent route planning for fast autonomous vehicles operating in a large natural terrain
Dunlap et al. Motion planning for mobile robots via sampling-based model predictive optimization
Yang et al. Fault tolerant control using Gaussian processes and model predictive control
Bai et al. Information-driven path planning
Yi et al. MORRF*: Sampling-based multi-objective motion planning
de Castro et al. Dynamic path planning based on neural networks for aerial inspection
Jiang et al. A probabilistic decision engine for navigation of autonomous vehicles under uncertainty
Chowdhury et al. Optimal path planning of autonomous marine vehicles in stochastic dynamic ocean flows using a GPU-accelerated algorithm
Hahn et al. Predictive collision management for time and risk dependent path planning
Khaleghi et al. Agent-based hardware-in-the-loop simulation for UAV/UGV surveillance and crowd control system
Ghambari et al. UAV path planning in the presence of static and dynamic obstacles
US11680807B2 (en) Probabilistic decision engine
Perez-Leon et al. Integration of a 4D-trajectory follower to improve multi-UAV conflict management within the U-space context
Jarray et al. Path planning strategy for unmanned aerial vehicles based on a grey wolf optimiser
Al-Hussaini et al. A simulation-based framework for generating alerts for human-supervised multi-robot teams in challenging environments
De Carolis et al. Runtime energy estimation and route optimization for autonomous underwater vehicles
Radmanesh et al. Analytical pde based trajectory planning for unmanned air vehicles in dynamic hostile environments
Jacinto et al. Navigation of autonomous vehicles using reinforcement learning with generalized advantage estimation
Yang et al. Learning graph-enhanced commander-executor for multi-agent navigation
Mukherjee et al. Multi-Agent Systems (MAS) related data analytics in the Hybrid Aerial Underwater Robotic System (HAUCS)
Hongfei et al. Research on situation awareness based on ontology for UUV