[go: up one dir, main page]

Dudarenko et al., 2019 - Google Patents

Robot navigation system in stochastic environment based on reinforcement learning on lidar data

Dudarenko et al., 2019

Document ID
13662747147733987257
Author
Dudarenko D
Kovalev A
Tolstoy I
Vatamaniuk I
Publication year
Publication venue
Proceedings of 14th International Conference on Electromechanics and Robotics “Zavalishin's Readings” ER (ZR) 2019, Kursk, Russia, 17-20 April 2019

External Links

Snippet

In this paper, we present an approach ensuring efficient pathfinding by a robotic platform among static and dynamic objects in a stochastic environment. The approach utilizes data from two-dimensional laser scanner (lidar) that are fed to the neural network for …
Continue reading at link.springer.com (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00624Recognising scenes, i.e. recognition of a whole field of perception; recognising scene-specific objects
    • 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
    • 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
    • G05D1/0268Control of position or course in two dimensions specially adapted to land vehicles using internal positioning means
    • 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
    • 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
    • G05D1/0255Control of position or course in two dimensions specially adapted to land vehicles using acoustic signals, e.g. ultra-sonic singals
    • 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
    • G05D1/0231Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/36Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/46Extraction of features or characteristics of the image
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00Computer systems utilising knowledge based models
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01SRADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/66Radar-tracking systems; Analogous systems where the wavelength or the kind of wave is irrelevant
    • G01S13/72Radar-tracking systems; Analogous systems where the wavelength or the kind of wave is irrelevant for two-dimensional tracking, e.g. combination of angle and range tracking, track-while-scan radar
    • G01S13/723Radar-tracking systems; Analogous systems where the wavelength or the kind of wave is irrelevant for two-dimensional tracking, e.g. combination of angle and range tracking, track-while-scan radar by using numerical data
    • G01S13/726Multiple target tracking

Similar Documents

Publication Publication Date Title
Ridel et al. Scene compliant trajectory forecast with agent-centric spatio-temporal grids
Choi et al. Unmanned aerial vehicles using machine learning for autonomous flight; state-of-the-art
Desaraju et al. Vision-based landing site evaluation and informed optimal trajectory generation toward autonomous rooftop landing
Burgard et al. World modeling
Tao et al. SEER: Safe efficient exploration for aerial robots using learning to predict information gain
Coscia et al. Long-term path prediction in urban scenarios using circular distributions
Naveed et al. Deep introspective SLAM: Deep reinforcement learning based approach to avoid tracking failure in visual SLAM
Harik et al. Fuzzy logic controller for predictive vision-based target tracking with an unmanned aerial vehicle
Eiffert et al. Resource and response aware path planning for long-term autonomy of ground robots in agriculture
Xiao et al. Vision-based learning for drones: A survey
Nguyen et al. Uncertainty-aware visually-attentive navigation using deep neural networks
Dudarenko et al. Robot navigation system in stochastic environment based on reinforcement learning on lidar data
Wulfmeier et al. Incorporating human domain knowledge into large scale cost function learning
Agishev et al. Trajectory optimization using learned robot-terrain interaction model in exploration of large subterranean environments
Wang et al. Towards better generalization in quadrotor landing using deep reinforcement learning
Shu et al. Overview of Terrain Traversability Evaluation for Autonomous Robots
Kalidas et al. Deep Reinforcement Learning for Vision-Based Navigation of UAVs in Avoiding Stationary and Mobile Obstacles. Drones 2023, 7, 245
Zaier et al. Vision-based UAV tracking using deep reinforcement learning with simulated data
Soteriou et al. Closing the sim-to-real gap: Enhancing autonomous precision landing of uavs with detection-informed deep reinforcement learning
Hatao et al. Construction of semantic maps for personal mobility robots in dynamic outdoor environments
Sviatov et al. Detection of Scenes Features for Path Following on a Local Map of a Mobile Robot Using Neural Networks
Wigness et al. Reducing adaptation latency for multi-concept visual perception in outdoor environments
Zhang et al. A Vision-Based Method for UAV Autonomous Landing Area Detection
Lu et al. YOPOv2-Tracker: An End-to-End Agile Tracking and Navigation Framework from Perception to Action
Fahnestock et al. Far-Field Image-Based Traversability Mapping for A Priori Unknown Natural Environments