K. KRISHNA, D. CASBEER, P. CHANDLER & M. PACHTER AFRL CONTROL SCIENCE CENTER OF EXCELLENCE MACCCS REVIEW APRIL 19, 2012 UAV Search & Capture of a Moving.

Slides:



Advertisements
Similar presentations
TARGET DETECTION AND TRACKING IN A WIRELESS SENSOR NETWORK Clement Kam, William Hodgkiss, Dept. of Electrical and Computer Engineering, University of California,
Advertisements

AI Pathfinding Representing the Search Space
Mobile and Wireless Computing Institute for Computer Science, University of Freiburg Western Australian Interactive Virtual Environments Centre (IVEC)
3/3 Factoid for the day: “Most people have more than the average number of feet” & eyes & ears & noses.
Markov Decision Process
Partially Observable Markov Decision Process (POMDP)
Totally Unimodular Matrices
Study Group Randomized Algorithms 21 st June 03. Topics Covered Game Tree Evaluation –its expected run time is better than the worst- case complexity.
Presentation by: Drew Wichmann Paper by: Samer Hanoun and Saeid Nahavandi 1.
CWI PNA2, Reading Seminar, Presented by Yoni Nazarathy EURANDOM and the Dept. of Mechanical Engineering, TU/e Eindhoven September 17, 2009 An Assortment.
G. Alonso, D. Kossmann Systems Group
Decision Theoretic Planning
Game Playing Games require different search procedures. Basically they are based on generate and test philosophy. At one end, generator generates entire.
Graphs Graphs are the most general data structures we will study in this course. A graph is a more general version of connected nodes than the tree. Both.
Part 3: The Minimax Theorem
Information Theoretical Security and Secure Network Coding NCIS11 Ning Cai May 14, 2011 Xidian University.
Hierarchical Decompositions for Congestion Minimization in Networks Harald Räcke 1.
1 Sensor Relocation in Mobile Sensor Networks Guiling Wang, Guohong Cao, Tom La Porta, and Wensheng Zhang Department of Computer Science & Engineering.
Planning under Uncertainty
1 Stochastic Event Capture Using Mobile Sensors Subject to a Quality Metric Nabhendra Bisnik, Alhussein A. Abouzeid, and Volkan Isler Rensselaer Polytechnic.
Decision Analysis. Decision Analysis provides a framework and methodology for rational decision making when the outcomes are uncertain.
CPSC 322, Lecture 9Slide 1 Search: Advanced Topics Computer Science cpsc322, Lecture 9 (Textbook Chpt 3.6) January, 23, 2009.
Lehrstuhl für Informatik 2 Gabriella Kókai: Maschine Learning Reinforcement Learning.
The Cache Location Problem IEEE/ACM Transactions on Networking, Vol. 8, No. 5, October 2000 P. Krishnan, Danny Raz, Member, IEEE, and Yuval Shavitt, Member,
Tirgul 10 Rehearsal about Universal Hashing Solving two problems from theoretical exercises: –T2 q. 1 –T3 q. 2.
Localized Techniques for Power Minimization and Information Gathering in Sensor Networks EE249 Final Presentation David Tong Nguyen Abhijit Davare Mentor:
Beyond selfish routing: Network Formation Games. Network Formation Games NFGs model the various ways in which selfish agents might create/use networks.
1 Motion Planning Algorithms : BUG-family. 2 To plan a path  find a continuous trajectory leading from initial position of the automaton (a mobile robot)
1 Worst and Best-Case Coverage in Sensor Networks Seapahn Meguerdichian, Farinaz Koushanfar, Miodrag Potkonjak, Mani Srivastava IEEE TRANSACTIONS ON MOBILE.
Optimal Tuning of Continual Online Exploration in Reinforcement Learning Youssef Achbany, Francois Fouss, Luh Yen, Alain Pirotte & Marco Saerens Information.
SMART: A Scan-based Movement- Assisted Sensor Deployment Method in Wireless Sensor Networks Jie Wu and Shuhui Yang Department of Computer Science and Engineering.
13. The Weak Law and the Strong Law of Large Numbers
Chess Review May 11, 2005 Berkeley, CA Closing the loop around Sensor Networks Bruno Sinopoli Shankar Sastry Dept of Electrical Engineering, UC Berkeley.
Establishing Pairwise Keys in Distributed Sensor Networks Donggang Liu, Peng Ning Jason Buckingham CSCI 7143: Secure Sensor Networks October 12, 2004.
9/23. Announcements Homework 1 returned today (Avg 27.8; highest 37) –Homework 2 due Thursday Homework 3 socket to open today Project 1 due Tuesday –A.
Distributed process management: Distributed deadlock
A Study of Computational and Human Strategies in Revelation Games 1 Noam Peled, 2 Kobi Gal, 1 Sarit Kraus 1 Bar-Ilan university, Israel. 2 Ben-Gurion university,
CS Reinforcement Learning1 Reinforcement Learning Variation on Supervised Learning Exact target outputs are not given Some variation of reward is.
A Randomized Approach to Robot Path Planning Based on Lazy Evaluation Robert Bohlin, Lydia E. Kavraki (2001) Presented by: Robbie Paolini.
Energy Saving In Sensor Network Using Specialized Nodes Shahab Salehi EE 695.
Decentralised Coordination of Mobile Sensors School of Electronics and Computer Science University of Southampton Ruben Stranders,
Computational Stochastic Optimization: Bridging communities October 25, 2012 Warren Powell CASTLE Laboratory Princeton University
Energy-Aware Scheduling with Quality of Surveillance Guarantee in Wireless Sensor Networks Jaehoon Jeong, Sarah Sharafkandi and David H.C. Du Dept. of.
CCAN: Cache-based CAN Using the Small World Model Shanghai Jiaotong University Internet Computing R&D Center.
1 ECE-517 Reinforcement Learning in Artificial Intelligence Lecture 7: Finite Horizon MDPs, Dynamic Programming Dr. Itamar Arel College of Engineering.
KRISHNA KALYANAM (INFOSCITEX CORP.) IN COLLABORATION WITH S. DARBHA (TAMU) P. P. KHARGONEKAR (UF, E-ARPA) M. PACHTER (AFIT/ENG) P. CHANDLER AND D. CASBEER.
Disclosure risk when responding to queries with deterministic guarantees Krish Muralidhar University of Kentucky Rathindra Sarathy Oklahoma State University.
Online Algorithms By: Sean Keith. An online algorithm is an algorithm that receives its input over time, where knowledge of the entire input is not available.
Games. Adversaries Consider the process of reasoning when an adversary is trying to defeat our efforts In game playing situations one searches down the.
1 Distributed and Optimal Motion Planning for Multiple Mobile Robots Yi Guo and Lynne Parker Center for Engineering Science Advanced Research Computer.
CHAPTER 4 RECURSION. BASICALLY, A FUNCTION IS RECURSIVE IF IT INCLUDES A CALL TO ITSELF.
Quickest Detection of a Change Process Across a Sensor Array Vasanthan Raghavan and Venugopal V. Veeravalli Presented by: Kuntal Ray.
Graph Colouring L09: Oct 10. This Lecture Graph coloring is another important problem in graph theory. It also has many applications, including the famous.
1 - CS7701 – Fall 2004 Review of: Detecting Network Intrusions via Sampling: A Game Theoretic Approach Paper by: – Murali Kodialam (Bell Labs) – T.V. Lakshman.
1 Utilizing Shared Vehicle Trajectories for Data Forwarding in Vehicular Networks IEEE INFOCOM MINI-CONFERENCE Fulong Xu, Shuo Gu, Jaehoon Jeong, Yu Gu,
Efficient Computing k-Coverage Paths in Multihop Wireless Sensor Networks XuFei Mao, ShaoJie Tang, and Xiang-Yang Li Dept. of Computer Science, Illinois.
CS483/683 Multi-Agent Systems Lecture 2: Distributed variants of 2 important AI problems: Search and Constraint Satisfaction 21 January 2010 Instructor:
COSC 3101NJ. Elder Announcements Midterm Exam: Fri Feb 27 CSE C –Two Blocks: 16:00-17:30 17:30-19:00 –The exam will be 1.5 hours in length. –You can attend.
Fault tolerance and related issues in distributed computing Shmuel Zaks GSSI - Feb
Smart Sleeping Policies for Wireless Sensor Networks Venu Veeravalli ECE Department & Coordinated Science Lab University of Illinois at Urbana-Champaign.
1 (Chapter 3 of) Planning and Control in Stochastic Domains with Imperfect Information by Milos Hauskrecht CS594 Automated Decision Making Course Presentation.
Coverage Problems in Wireless Ad-hoc Sensor Networks Seapahn Meguerdichian 1 Farinaz Koushanfar 2 Miodrag Potkonjak 1 Mani Srivastava 2 University of California,
Network Management Lecture 13. MACHINE LEARNING TECHNIQUES 2 Dr. Atiq Ahmed Université de Balouchistan.
Maryam Pourebadi Kent State University April 2016.
Mean Field Methods for Computer and Communication Systems Jean-Yves Le Boudec EPFL Network Science Workshop Hong Kong July
Towards Optimal Sleep Scheduling in Sensor Networks for Rare-Event Detection Qing Cao, Tarek Abdelzaher, Tian He, John Stankovic Department of Computer.
NAME THAT ALGORITHM #2 HERE ARE SOME PROBLEMS. SOLVE THEM. GL HF.
Coverage and Connectivity in Sensor Networks
Discrete Math 2 Shortest Path Using Matrix
Illustrative Example p p Lookup Table for Digits of h g f e ) ( d c b
Presentation transcript:

K. KRISHNA, D. CASBEER, P. CHANDLER & M. PACHTER AFRL CONTROL SCIENCE CENTER OF EXCELLENCE MACCCS REVIEW APRIL 19, 2012 UAV Search & Capture of a Moving Ground Target Approved for public release; distribution unlimited; case number: 88ABW

UGS Sensor Range UGS Communication Range Valid Intruder Path Scenario UAV Communication Range BASE

UAV/UGS Framework UAV engaged in search and capture of intruder on a road network Intersections in road instrumented with Unattended Ground Sensors (UGSs) Intruder is a goal oriented random walker UAV has a speed advantage over the intruder Passing intruder triggers UGS and the event is time- stamped and stored in the UGS UAV has no sensing capability Capture occurs when UAV and intruder are at an UGS location at the same time

Manhattan Grid All edges of the grid are of same length Intruder starts at node S and proceeds towards goal nodes marked G UAV also starts at node S and observes red UGS with delay d Intruder dynamics - randomly move North, East or South but cannot retrace path UAV actions - move North, East or South or Loiter at current locationGG G S

System Dynamics

Solution Method Pose the problem as a POMDP  unconventional POMDP since observations give delayed intruder location information with random time delays!  Use observations to compute the current intruder position probabilities Dual control problem  choose UAV action that minimizes the uncertainty associated with intruder location (localization) or choose action that gets the UAV closest to the most probable intruder location (“end game”/ capture)

A few notes Assumptions:  The intruder finishes in finite time and  can not retrace his steps (no cycles). Guaranteed intruder capture is hard because  Incomplete, out-of-sequence, and delayed information (Unconventional POMDP formulation)

Worst Case Guaranteed Capture (Perfect Information) Related to control with uncertainty modeled as bounded sets [Witsenhausen 68, Bertsekas and Rhodes 73] Guaranteed capture is related to reachable sets [Bertsekas and Rhodes 71]

Full Information Scenario From end of finite horizon  Find set of UAV “safe’’ locations that guarantee capture  Proceed backwards in time looking for “safe locations” Under full information, we have a necessary and sufficient condition for guaranteed capture [Bertsekas and Rhodes 71]

Full Information strategy Case 1: Intruder is at a goal  The UAV must be at the same goal location Case 2: Intruder is not at the goal  Safe locations: 1. Capture now OR 2. Ensure that UAV is in a position to capture the intruder in the future regardless of intruder’s moves.

Proceed in a fashion similar to the full information case except using (estimate) set of intruder locations instead of the true (unknown) intruder location. Partial and Delayed observation

Out of sequence Estimation Process

Set of safe UAV locations

Backward (DP) recursion Either the UAV is at the current intruder location leading to immediate capture or the UAV can take action such that it goes to a favorable location in the next time step; leading to eventual capture.

Sufficient condition The condition is however not necessary for guaranteed capture due to the dual control aspect of the problem.

2x2 Grid Example Problem UAV starting location 1 satisfies sufficient condition (see entry 0 in table)

b1b1b1b1 b2b2b2b2 Min-Max Optimal Paths For bottom row delays 1 and 2. Intruder path is orange and corresponding optimal UAV path is in green (dotted circle represents loiter).

c1c1c1c1 b1b1b1b1 Min-Max Optimal (End Game) For middle row delay 1. Intruder path is orange and corresponding optimal UAV path is in green (dotted circle represents loiter).

c1c1c1c1 b1b1b1b1 b3b3b3b3 Min-Max Optimal Paths For bottom row delay 3. Intruder path is orange and corresponding optimal UAV path is in green (dotted circle represents loiter).

c1c1c1c1 b1b1b1b1 c2c2c2c2 For middle row delay 2. Intruder path is orange and corresponding optimal UAV path is in green (dotted circle represents loiter). Min-Max Optimal Paths

c1c1c1c1 b1b1b1b1 cDcDcDcD c D-1 (D-2) steps For middle row delay D>0 - Induction argument (to prove optimality) leading to the “End Game” Min-Max Optimal Paths

c1c1c1c1 b1b1b1b1 bDbDbDbD c D-2 (D-3) steps For bottom row delay D>2 - Induction argument (to prove optimality) leading to the “End Game” Min-Max Optimal Paths

Min-Max optimal Steps to capture CaseNumber stepsNumber columns needed bottom row - delay 1 (b 1 ) 51 middle row - delay 1 (c 1 ) 113 bottom row - delay 2 (b 2 ) 62 middle row - delay 2 (c 2 ) 124 bottom row - delay 3 (b 3 ) 134 middle row - delay 3 (c 3 ) 135

Conclusions Sufficient condition for guaranteed capture in partial information case Currently trying to tighten the gap between necessity and sufficiency  Include possible future observations to reduce the predicted future uncertainty set An induction argument for guaranteed capture on longer horizons (exploit structure in the graph)

Search & Capture Video