Sensor-based Motion Planning for MCM Teams. by Sean Kragelund Center for Autonomous Vehicle Research (CAVR)
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1 Sensor-based Motion Planning for MCM Teams by Sean Kragelund Center for Autonomous Vehicle Research (CAVR) October 5, 2015
2 Sensor-based Planning GOAL: optimize some mission objective Max. information gain (map coverage) Min. probability of not detecting mines, etc. Explicitly consider: Vehicle dynamics/constraints Sensor characteristics CHALLENGE: Strike a balance between Exploiting prior knowledge (previously mapped areas) Exploring unknown regions of op area 2
3 Heterogeneous Autonomous Vehicle Teams Layered System of Systems Put vehicle sensors & capabilities where needed Use multiple, possibly expendable, UxVs vs. costly all-purpose platforms Motivation Marsupial robots Host UxVs transport, deploy, and interact with payload UxVs Attractive for distributed, wide-area operations Environmental sensing & monitoring Undersea Warfare (USW) Mine countermeasures (MCM) 3
4 MCM Operations Typically conduct three sequential phases by dedicated sensor platforms 1. Wide-area survey to detect and localize mine-like objects Long range, wide swath sonar for high area search rates 2. Reacquire & Identify: classify prior survey contacts as mine or non-mine High resolution sonar and/or optical sensors for classification Multiple aspects often required positive identification 3. Neutralize ONR FNC: Single Sortie Detect to Engage (SSDTE) Autonomous USV to launch, sustain, recover, coordinate multiple MCM AUVs NSWC-PC (Matt Bays) Multiple Aspect Coverage (MAC) Problem for efficient R&I Path Planning Optimized Waterspace Management and MCM Scheduling 4
5 Optimal Control Framework General Form for Parameter Uncertainty: Given φ:ω R, determine the control u:[0,t] U R n u that minimizes the cost functional: J= Ω [F(x(T,ω),ω) +G( 0 T r(x(t,ω),u(t),t,ω)dt )]φ(ω)dω subject to: x (t,ω)=f(x(t,ω),u(t),ω) x(0,ω)= x 0 (ω) g(u(t)) 0 Motion Planning Applications Autonomous navigation in unknown riverine environments Sensor-based planning for collaborative MCM by USV/AUV team 5
6 ATLAS Autonomous Topographic Large Area Sonar Developed by ARL:UT (Austin) Transitioning into fleet systems Mk 18 Mod 2 Kingfish (REMUS 600) Large Displacement UUV SEAL Delivery Vehicle OAS Dr. Horner (CAVR) won ONR DURIP award for two systems ATLAS Capabilities Long Range (> 800 m) Wide Swath (200 FOV) Bathymetric Mapping Automatic Target Detection 6
7 SeaFox/ATLAS Integration NPS SeaFox Mk II USV 5.1-meter RHIB 200 HP Mercury Sport Jet (30 knots top speed) Navy SeaCAN Architecture (NAWCWD Pt. Mugu) Sensors EO/IR Camera Turrets ATLAS FLS Broadband Radar (FY15) Custom Sonar Mount Remotely actuated Retracts for trailer L&R Extends sonar to depth 7
8 ATLAS Imagery Shale Bed Features MISO Location Bottom Imagery near NPS MISO (off Del Monte Beach) 8
9 CAVR Research USV/AUV Teaming for Mine Countermeasures USV has forward-looking sonar and acomms Proposed Experiment: SeaFox USV with ATLAS sonar for wide area survey (Detect/Localize) REMUS 100 AUV with sidescan sonar for Require and Identify (RI) SeaFox commands REMUS to investigate ATLAS targets with RI search patterns USV is enabling technology for system of low-cost (Neutralizer) AUVs Mobile communications gateway External navigation aid Maintains AUV navigational accuracy System can respond to new information 9
10 Sonar Detection Model Acoustic Signal Excess [db] Sensor-based Planning SE(t)=FOM PL(x(t),ω) FOM: Figure of Merit (Sonar Design Parameters) Source Level, Receiver Noise, Detection Threshold PL: Propagation Losses (Range/Environment Dependent) Acoustic Spreading, Absorption Poisson Scan Model: Independent Detections occur at Poisson rate λ Instantaneous Detection Rate: γ(t)=λφ ( SE(t)/σ ) 10
11 ATLAS Geometry ~10 Receive Array 200 FOV > 400 m (not to scale) Acoustic Projector Vertically Steerable Beam Angle Software Selectable Beam Width 11
12 Sidescan Geometry 900 khz Sidescan Sonar Array ~40 m Max. Range <1 Horizontal Beam Width ~40 Vertical Beam Width 12
13 Sonar Geometry Effects Optimization objective function: J= Ω e 0 T r(x(τ),τ,ω) dτ φ(ω)dω where r(x(t),t,ω)= F α F ε γ(t) modifies instantaneous detection rate by the azimuth (horizontal) and elevation (vertical) angles in sonar FOV F α = 1/1+ e ( α ω α L ) + 1/1+ e ( α ω α U ) 1 F ε = 1/1+ e ( ε ω ε L ) + 1/1+ e ( ε ω ε U ) 1 13
14 Simulated MCM Survey 14
15
16 Future Work Formulate motion planning problems for heterogeneous UxV teams in optimal control framework (parameter uncertainty) Determine optimal solutions as baselines for analysis Analyze solutions for insight into cost function & tradeoffs between vehicle dynamics and sensor characteristics Evaluate techniques for generating near-optimal, approximate trajectories for real-time implementation 16
17 Questions? 17
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