MURI Telecon, Update 7/26/2012
This presentation is the property of its rightful owner.
Sponsored Links
1 / 10

MURI Telecon, Update 7/26/2012 PowerPoint PPT Presentation


  • 48 Views
  • Uploaded on
  • Presentation posted in: General

MURI Telecon, Update 7/26/2012. Summary, Part I:

Download Presentation

MURI Telecon, Update 7/26/2012

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript


Muri telecon update 7 26 2012

MURI Telecon, Update 7/26/2012

  • Summary, Part I:

    • Completed: proving and validating numerically optimality conditions for Distributed Optimal Control (DOC) problem; conservation law analysis; direct method of solution for DOC problems; computational complexity analysis; application to multi-agent path planning.

    • Submitted paper on developments above to Automatica.

    • Completed: modeling of maneuvering targets by Markov motion models; derivation of (corresponding) multi-sensor performance function representing the probability of detection of multiple distributed sensors; application to multi-sensor placement.

    • Submitted paper on developments above to IEEE TC.

    • In progress: application of methods above to multi-sensor trajectory optimization for tracking and detecting Markov targets based on feedback from a Kalman-Particle filter.

    • Submitted paper on developments above to MSIT 2012; another journal paper on developments above in preparation.


Muri telecon update 7 26 2012

MURI Telecon, Update 7/26/2012

  • Summary, Part II:

    • Completed: comparison of information theoretic functions for multi-sensor systems performing target classification.

    • Published paper on above developments in SMCB –Part B, Vol. 42, No. 1, Feb 2012.

    • In progress: comparison of information theoretic functions for multi-sensor systems performing (Markov) target tracking and detection.

    • Submitted paper on above developments to SSP 2012; another journal paper on developments above in preparation.

    • Completed: derived new approximate dynamic relations for hybrid systems.

    • Submitted paper on above developments to JDSM.

    • In progress: integrating DOC for multiple tasks and distributions with consensus based bundle algorithm (CBBA); apply DOC to non-parametric Bayesian models of sensors/targets.

    • In progress: develop DOC reachability proofs in the presence of communication constraints, for decentralized DOC.


Muri telecon update 7 26 2012

DOC Background

  • Distributed Systems: A system of multiple autonomous dynamic systems that communicate and interact with each other to achieve a common goal.

    • Swarms: Hundreds to thousands of systems; homogeneous; minimal communication and sensing capabilities. Decentralized control laws: stable; non-optimal; and, do not meet common goal.

    • Multi-agent systems: few to hundreds of systems; heterogeneous; advanced sensing and, possibly, communication capabilities. Centralized vs. decentralized control laws: path planning; obstacle avoidance; must meet one or more common goals, subject to agent constraints and dynamics.

  • Classical Optimal Control: Determines the optimal control law and trajectory for a single agent or dynamical system.

    • Characterized by well-known optimality conditions and numerical algorithms

    • Applied to a single agent for trajectory optimization, pursuit-evasion, feedback control (auto-pilots) ..

    • Does not scale to systems of hundreds of agents

3


Muri telecon update 7 26 2012

Benchmark Problem:

Multi-agent Path Planning

The agent microscopic dynamics are given by the unicycle model with constant velocity, which amounts to the following system of ODEs,

Agent:

Where:

The number of components (m) in the Gaussian mixture is chosen by the used based on the complexity of the initial and goal PDFs.

4


Muri telecon update 7 26 2012

Example with m = 4

Goal PDF, h(xi, tf)

Initial PDF, p(xi, t0)

Pr(xi)

: Fixed obstacle

5


Muri telecon update 7 26 2012

Results: Optimal PDF (m = 4)

Pr(xi): Optimal PDF

: Fixed obstacle

6


Muri telecon update 7 26 2012

Agents’ Optimal Trajectories

Feedback control of agents via DOC.

Pr(xi): Optimal PDF

Agent’s control input (Sample)

: Individual agent (unicycle)

: Fixed obstacle

7


Muri telecon update 7 26 2012

Example with m = 6

Goal PDF, h(xi, tf)

Initial PDF, p(xi, t0)

Pr(xi)

: Fixed obstacle

8


Muri telecon update 7 26 2012

Results: Optimal PDF (m = 6)

Pr(xi): Optimal PDF

: Fixed obstacle

9


Muri telecon update 7 26 2012

Agents’ Optimal Trajectories

Feedback control of N = 200 agents via DOC.

Pr(xi): Optimal PDF

Agent’s control input (Sample)

: Individual agent (unicycle)

: Fixed obstacle

10


  • Login