1. AFOSR MURI. Salem, MA. June 4, 2002. 1/10 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 ONR YIA

2. AFOSR MURI. Salem, MA. June 4, 2002. 2/10 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? Overview We view spatial distribution of the UAVs as a key factor and present original results concerning the UAV separations and the UAV placements. Coordinated Path Planning Surveillance Number of UAVs Efficiency = ??

3. AFOSR MURI. Salem, MA. June 4, 2002. 3/10 Coordinated Path Planning (CPP) 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! 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.

4. AFOSR MURI. Salem, MA. June 4, 2002. 4/10 Related Past Works We present new results in a coordinated target acquisition setting using DP. 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]. known region new region agent unknown region

5. AFOSR MURI. Salem, MA. June 4, 2002. 5/10 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.

6. AFOSR MURI. Salem, MA. June 4, 2002. 6/10 The CPP Separation Results G 7, infinite horizon, discount factor  = 0.8 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. 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 Communication power, hierarchy tier sizes

7. AFOSR MURI. Salem, MA. June 4, 2002. 7/10 Surveillance as CPP Surveillance Problem Setting Terrain as regions with dynamic, uncertain payoffs. UAVs face dynamic, uncertain threats. 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! efficiency SNR

8. AFOSR MURI. Salem, MA. June 4, 2002. 8/10 Related Efficiency Results capacity … Gupta-Kumar [2000] capacity … Grossglauser-Tse [2000] Dumb Antennas … Viswanath et al [2002] Space-Time Codes … Tarokh et al [2000] Commonalities with Cellular Network Concepts i.i.d. uniformly distributed payoffs path loss decrease in efficiency How many Network Capacity Cellular network understanding has promise in the UAV setting. Techniques to exploit the UAV mobility

9. AFOSR MURI. Salem, MA. June 4, 2002. 9/10 Future Directions probability link cost 1 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 efficiency per UAV number of UAVs ??

10. AFOSR MURI. Salem, MA. June 4, 2002. 10/10 Questions ?? 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. Thank You !