[go: up one dir, main page]

Agha-Mohammadi et al., 2011 - Google Patents

FIRM: Feedback controller-based Information-state RoadMap-a framework for motion planning under uncertainty

Agha-Mohammadi et al., 2011

View PDF
Document ID
12751935907513346955
Author
Agha-Mohammadi A
Chakravorty S
Amato N
Publication year
Publication venue
2011 IEEE/RSJ International Conference on Intelligent Robots and Systems

External Links

Snippet

Direct transformation of sampling-based motion planning methods to the Information-state (belief) space is a challenge. The main bottleneck for roadmap-based techniques in belief space is that the incurred costs on different edges of the graph are not independent of each …
Continue reading at www.researchgate.net (PDF) (other versions)

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
    • G05D1/0276—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle
    • G05D1/0278—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle using satellite positioning signals, e.g. GPS
    • 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
    • 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
    • G05D1/02—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
    • 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
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D2201/00—Application
    • G05D2201/02—Control of position of land vehicles

Similar Documents

Publication Publication Date Title
Agha-Mohammadi et al. FIRM: Feedback controller-based Information-state RoadMap-a framework for motion planning under uncertainty
Zuo et al. A hierarchical path planning approach based on A⁎ and least-squares policy iteration for mobile robots
Rosolia et al. Unified multirate control: From low-level actuation to high-level planning
Van Den Berg et al. Motion planning under uncertainty using iterative local optimization in belief space
McKinnon et al. Learn fast, forget slow: Safe predictive learning control for systems with unknown and changing dynamics performing repetitive tasks
Agha-mohammadi et al. SLAP: Simultaneous localization and planning under uncertainty via dynamic replanning in belief space
Nilsson et al. Toward Specification-Guided Active Mars Exploration for Cooperative Robot Teams.
Agha-Mohammadi et al. Robust online belief space planning in changing environments: Application to physical mobile robots
Wang et al. Autonomous exploration with expectation-maximization
Rafieisakhaei et al. T-lqg: Closed-loop belief space planning via trajectory-optimized lqg
Chen et al. Efficient active SLAM based on submap joining, graph topology and convex optimization
Rafieisakhaei et al. Feedback motion planning under non-gaussian uncertainty and non-convex state constraints
Jacinto et al. Navigation of autonomous vehicles using reinforcement learning with generalized advantage estimation
Alam et al. Minimalist robot navigation and coverage using a dynamical system approach
Agha-mohammadi et al. SLAP: Simultaneous localization and planning under uncertainty for physical mobile robots via dynamic replanning in belief space: Extended version
Zhang et al. Efficient and near-optimal global path planning for AGVs: A DNN-based double closed-loop approach with guarantee mechanism
Juston et al. Robust Error State Sage-Husa Adaptive Kalman Filter for UWB localization
Nardi et al. User preferred behaviors for robot navigation exploiting previous experiences
Cuevas et al. Path planning under risk and uncertainty of the environment
Rafieisakhaei et al. Belief space planning simplified: Trajectory-optimized lqg (t-lqg)
Dadvar et al. Joint communication and motion planning for cobots
Rosolia et al. Model predictive control in partially observable multi-modal discrete environments
Yu et al. Stochastic feedback control of systems with unknown nonlinear dynamics
Ivanov et al. An efficient robotic exploration planner with probabilistic guarantees
Agha-mohammadi et al. Sampling-based nonholonomic motion planning in belief space via dynamic feedback linearization-based FIRM