[go: up one dir, main page]

HomChaudhuri et al., 2017 - Google Patents

Computation of forward stochastic reach sets: Application to stochastic, dynamic obstacle avoidance

HomChaudhuri et al., 2017

View PDF
Document ID
18406379812887299429
Author
HomChaudhuri B
Vinod A
Oishi M
Publication year
Publication venue
2017 American Control Conference (ACC)

External Links

Snippet

We propose a method to efficiently compute the forward stochastic reach (FSR) set and its probability measure. We consider nonlinear systems with an affine disturbance input, that is stochastic and bounded. This model includes uncontrolled systems and systems with an a …
Continue reading at arxiv.org (PDF) (other versions)

Classifications

    • 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
    • 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
    • G05D1/0011—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
    • 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
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/30—Nc systems
    • 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
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00—Computer systems based on specific mathematical models
    • G06N7/005—Probabilistic networks

Similar Documents

Publication Publication Date Title
Lew et al. Safe active dynamics learning and control: A sequential exploration–exploitation framework
Lindemann et al. Safe planning in dynamic environments using conformal prediction
HomChaudhuri et al. Computation of forward stochastic reach sets: Application to stochastic, dynamic obstacle avoidance
Dhiman et al. Control barriers in bayesian learning of system dynamics
Nakka et al. Chance-constrained trajectory optimization for safe exploration and learning of nonlinear systems
Kousik et al. Ellipsotopes: Uniting ellipsoids and zonotopes for reachability analysis and fault detection
Liu et al. Communication-aware motion planning for multi-agent systems from signal temporal logic specifications
Zhou et al. A real-time and fully distributed approach to motion planning for multirobot systems
US20210309264A1 (en) Human-robot collaboration
Hung et al. Hierarchical distributed control for global network integrity preservation in multirobot systems
Park et al. A distributed ADMM approach to non-myopic path planning for multi-target tracking
Biyik et al. Efficient and safe exploration in deterministic markov decision processes with unknown transition models
Vinod et al. Stochastic motion planning using successive convexification and probabilistic occupancy functions
Wu et al. Safe path planning for unmanned aerial vehicle under location uncertainty
Rafieisakhaei et al. T-lqg: Closed-loop belief space planning via trajectory-optimized lqg
Bhattacharyya et al. Automated vehicle highway merging: Motion planning via adaptive interactive mixed-integer mpc
Rafieisakhaei et al. Feedback motion planning under non-gaussian uncertainty and non-convex state constraints
Lee et al. Signal temporal logic synthesis as probabilistic inference
Hibbard et al. Safely: safe stochastic motion planning under constrained sensing via Duality
Lefkopoulos et al. Using uncertainty data in chance-constrained trajectory planning
Asarkaya et al. Temporal-logic-constrained hybrid reinforcement learning to perform optimal aerial monitoring with delivery drones
Vinod et al. Decentralized, safe, multiagent motion planning for drones under uncertainty via filtered reinforcement learning
Kleff et al. Robust motion planning in dynamic environments based on sampled-data hamilton–jacobi reachability
Xu et al. Decentralised coordination of mobile robots for target tracking with learnt utility models
Xu et al. Online and robust intermittent motion planning in dynamic and changing environments