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Coordinated UAV Operations: Perspectives and New Results

AFOSR MURI. ONR YIA. Coordinated UAV Operations: Perspectives and New Results. Vishwesh Kulkarni Joint Work with Jan De Mot, Sommer Gentry, Tom Schouwenaars, Vladislav Gavrilets, and Prof. Eric Feron at the Laboratory for Information and Decision Systems, MIT. Efficiency =. ??.

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Coordinated UAV Operations: Perspectives and New Results

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  1. AFOSR MURI ONR YIA Coordinated UAV Operations:Perspectives and New Results Vishwesh Kulkarni Joint Work with Jan De Mot, Sommer Gentry, Tom Schouwenaars, Vladislav Gavrilets, and Prof. Eric Feron at the Laboratory for Information and Decision Systems, MIT. AFOSR MURI. Salem, MA. June 4, 2002.

  2. Efficiency = ?? Number of UAVs • Coordinated Path Planning • Surveillance We view spatial distribution of the UAVs as a key factor and present original results concerning the UAV separations and the UAV placements. Overview • Efficient multi-agent operations require robust, optimal coordination policies. • UAV specifications constrain deployable coordination policies. • How may we improve our understanding of these constraints? • How may we use it to synthesize more efficient coordination policies? AFOSR MURI. Salem, MA. June 4, 2002.

  3. Coordinated Path Planning (CPP) • CPP Problem Setting • UAVs need to go from a point s to a point t. • Environment is dynamic and uncertain. • UAVs cooperate by sharing the acquired local information. • UAVs have limited resources. GOAL: Optimize the traversal efficiency. • Questions • What is the spatial distribution under an optimal policy? • We have characterized the separation bounds. • How many UAVs are needed? • We do not know the full answer yet! AFOSR MURI. Salem, MA. June 4, 2002.

  4. Multi-Agent Exploration of Unknown Environments • Probabilistic map building of Burgard et al [2002] uses deterministic value iteration to determine the next optimal observation point. • The market architecture of Zlot et al [2002] auctions off the next optimal observation points obtained by solving a TSP. • The end goal is spanning rather than CPP. • CPP as Multi-Agent MDPs • Boutilier et al [2000]. We consider partially observable MDPs. • Greedy policy pursuit-evasion games of Hespanha et al [2002]. Related Past Works agent known region unknown region new region We present new results in a coordinated target acquisition setting using DP. AFOSR MURI. Salem, MA. June 4, 2002.

  5. Our CPP Problem Terrain is mapped into regions having payoffs. Terrain traversal becomes graph traversal. • UAVs share local information. • Partially known, uncertain environment • On-board sensors reduce uncertainty in a • direction dependent manner. • Lookahead link costs are deterministic, others i.i.d. Goal: Find a path for each agent that minimizes the expected aggregate cost. AFOSR MURI. Salem, MA. June 4, 2002.

  6. Cluster Separation Lemma Using optimal paths for two agents in , configurations , , and do not evolve into configurations with l > 2. The UAV separation is bounded in Conjecture 1: The UAV separation is bounded in Extra nodes should not affect the separation adversely. Conjecture 2: The UAV separation is bounded in in a pair-wise sense. Conjecture 1 should hold pair-wise in the n-agent setting. The CPP Separation Results G7, infinite horizon, discount factor a = 0.8 • Communication power, hierarchy tier sizes AFOSR MURI. Salem, MA. June 4, 2002.

  7. efficiency SNR Surveillance as CPP • Surveillance Problem Setting • Terrain as regions with dynamic, uncertain payoffs. • UAVs face dynamic, uncertainthreats. • Limited communication capacity and efficiency. • Efficiency decreases with distance. • UAVs cooperate by repositioning and handoffs. • Goal: Maximize the net minimal spare UAV capacity. • Questions • What is the spatial distribution under an optimal policy? • Characterized by the separation results. • How many UAVs are needed? • We do not know the full answer yet! AFOSR MURI. Salem, MA. June 4, 2002.

  8. Commonalities with Cellular Network Concepts • i.i.d. uniformly distributed payoffs • path loss decrease in efficiency • How many Network Capacity • capacity … Gupta-Kumar [2000] • capacity … Grossglauser-Tse [2000] • Dumb Antennas … Viswanath et al [2002] • Space-Time Codes … Tarokh et al [2000] Related Efficiency Results • Techniques to exploit the UAV mobility Cellular network understanding has promise in the UAV setting. AFOSR MURI. Salem, MA. June 4, 2002.

  9. probability 1 link cost efficiency per UAV ?? number of UAVs Future Directions • Extensions for larger and heterogeneous clusters • Dynamic program modifications • More incremental on-board information gathering • Gradual link cost change from i.i.d. to deterministic • Sets of possible link cost distributions • Separation and efficiency properties for large scale systems • Curse of dimensionality • Neuro-Dynamic programming for approximate solutions • To add or not to add (a UAV) … • Brute force iterative DP-based solution • Binary search for an optimum number AFOSR MURI. Salem, MA. June 4, 2002.

  10. Questions ?? Thank You ! http://www.mit.edu/people/vishwesh/ vishwesh@mit.edu Joint work being done at MIT with Prof. Eric Feron’s group, supported by his AFOSR MURI and ONR Young Investigator Award grants. AFOSR MURI. Salem, MA. June 4, 2002.

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