SPHERES. Maturation of Autonomous Rendezvous & Docking Algorithms with SPHERES aboard the ISS. Alvar Saenz-Otero MIT SPHERES Lead Scientist

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1 Maturation of Autonomous Rendezvous & Docking Algorithms with aboard the ISS Alvar Saenz-Otero MIT Lead Scientist ICRA 2012 Satellite Servicing Workshop 2012/04/13

2 What is? A Facility of the ISS National Laboratory with three nano-satellites designed by MIT to research estimation, control, and autonomy algorithms By working aboard ISS under crew supervision, it provides a risk-tolerant environment The satellites can be reused Replenishable consumables Multiple test sessions assigned per year If anything goes wrong, reset and try again! If you can t bring the space environment to the laboratory, take the laboratory to space! 2

Science Development Process 1c: Upload MSFC ISS

Tests SSC w/lptx 1b: OCR JSC Audio & Video to

uploaded ~1week early 2) Run Test Session Live

for all parties: MIT, JSC, and MSFC START 1a:

Data END Responses up to ISS must go from MIT

LAN 3) Data download Data is downloaded 24-48

3 Science Development Process 1c: Upload MSFC ISS 2d: Paycom / POD 3a: Download Ops LAN 2a: Run Tests SSC w/lptx 1b: OCR JSC Audio & Video to all 2c: Relay to Loop 3b: Collect 1) Algorithms uploaded ~1week early 2) Run Test Session Live audio & video from ISS to Ground (during AOS) for all parties: MIT, JSC, and MSFC START 1a: Create Algorithms 2b: Response MIT 3c: Analyze Data END Responses up to ISS must go from MIT to JSC to MSFC Paycomm/POD Data stored in Ops LAN 3) Data download Data is downloaded hours after the session Uplink to ISS Downlink from ISS 3

4 Satellite Overview Interfaces: Control Panel Buttons Power Switch with Power LED Master reset button Enable button Circuit Breaker (pop-up) Indicators Power LED (on switch) Low battery LED Thrusters enabled Regulator adjust knob and pressure gauge 1 CO2 tank per satellite Threaded tank screws into satellite 2 Battery packs per satellite Magnetic closing door Velcro guard strap holds packs in Control panel Thrusters Pressure gauge Tank (bottom) Regulator adjust knob Ultrasound sensors Battery door 4

5 Satellite Servicing Algorithms w/ Ultimately: docking to an uncooperative, unknown, spinning target Target Characteristics: Uncooperative: No information is transmitted by the target Unknown: No a priori information regarding structure, appearance or mass/inertia properties Spinning: The target is spinning with a significant angular velocity. Note distinction between spinning and tumbling The target may be nutating No External Forces/Torques (possible future add-on) Approach: incremental technology maturation Docking Algorithms Reconfiguration Algorithms Inspection Algorithms Example 1: Spacecraft HS 376 Spin Stabilized to 50RPM Example 2: Defense Support Program (DSP) 23 Spin Stabilized at 6 RPM Geostationary Orbit Failure in 2008 (1 year after launch) Inspected by DARPA MiTex Program in 2008 (Wikipedia) 5

6 ISS Docking Algorithms Algorithm Description Dock to Beacon Dock to ISS Frame Dock to Cooperative Dock 2 actuated Target Dock to Drifting Target Study plume impingement Dock to Cooperative Improved estimation Target 2 algorithm Safe Docking Follow a path that improves safety Dock to Tumbling Simulate future servicing Target ops Dock with Online Demonstrate autonomous Path-Planning path planning 6

7 ISS Docking Algorithms - Movies (1) Safe Docking 7

8 ISS Docking Algorithms - Movies (2) Docking to a Tumbling Target 8

9 ISS Docking Algorithms - Movies (3) On-line Path Planning 9

alignment Replicates metrology for direct relative measurements 3-board design allows 5DOF relative

10 Universal Docking Port Provides docking and relative estimation capability Mechanical rigid attachment using cams Universal Identical copies of the port can dock to each other Requires relative attitude alignment Replicates metrology for direct relative measurements 3-board design allows 5DOF relative estimation US transmitter US receiver IR transmitter IR receiver Universal Docking Port (UDP) Front Face UDP CAD Model 10

11 Reconfiguration Element configuration: Mass, # UDPs, location of UDP, actuation specs, type of Air carriage Assembly configuration: How elements are connected Ex. SPHERE +X face docked to Payload #1 X face Mass properties: Mass, CM, Inertia Thruster locations with respect to CM Database: Mass properties of elements and possible assemblies Off-line On-Line SPHERE Payload #1 Assembly Config Database Assembly update Code Element Mass Properties Assembly Mass Properties Change to Assembly Config Control/ Test Execution Process Flow of Reconfiguration 11

12 SWARM Self-assembling, Wireless, Autonomous, Reconfigurable Modules UDP SPHERE satellite Node Payload #1: Sub-aperture Air carriage 12

SWARM Docking Results Total Tests 33 Reached Berthing 13 Reached Capture 7 Success matrix for docking tests Autonomous docking successfully demonstrated Non-capture of the floor perturbations by the

13 SWARM Docking Results Total Tests 33 Reached Berthing 13 Reached Capture 7 Success matrix for docking tests Autonomous docking successfully demonstrated Non-capture of the floor perturbations by the controllers led to low repeatability of the experiments Very close-proximity maneuvers proved to the most difficult ones because of jumps in state estimates Legend: * Initialization * Approach * Capture 13

underactuated control Research Ideas for adaptive control Initial hardware testing 2D flat floor demonstration Hardware: on

14 Goals: maneuvering and docking with flexible structures Requirements for assembly Follow trajectories for positioning and docking Minimize vibrational disturbances Desired Handle parameter uncertainty for unknown payloads Fewer actuators than degrees of freedom: underactuated control Research Ideas for adaptive control Initial hardware testing 2D flat floor demonstration Hardware: on propulsion module Flexible segmented beam Docking ports SWARM - Flexible Beam docking port satellite flexible beam element propulsion module 14

space X (m) Y (m) (rad) -0.2-0.4-0.6 0 20 40 60 80 100 120 140 160 180 200 Alignment Approach Docking 0.2 0-0.

15 SWARM - Flexible Beam Control Combines non-linear & underactuated adaptive control Mostly based on tip deflection Perform control on a lower dimensional task space Can not guarantee convergence / limited to output space X (m) Y (m) (rad) Alignment Approach Docking Target State Time (s) 15

16 SWARM - ISS Reconfiguration State-space implementation of a configuration mass CM inertia (about the CM) actuator locations (w.r.t. CM) sensor locations (w.r.t. CM) 1 or 2 satellites control 16

Satellite Inspection: Vision Based VERTIGO upgrade for Perform Computer Vision Based Navigation and Inspection of an Unknown, Uncooperative Tumbling Object ISS Microgravity environment provides

17 Satellite Inspection: Vision Based VERTIGO upgrade for Perform Computer Vision Based Navigation and Inspection of an Unknown, Uncooperative Tumbling Object ISS Microgravity environment provides excellent cost-effective 6DOF microgravity testbed for navigation and mapping algorithms Goggles Assembly is an upgrade that includes cameras, increased computing power and communications system Science Algorithms VERTIGO GSP API and Supporting Libraries Flight GUI Video Streaming IP Comm & Ethernet Goggles Hardware Batt, panel Camera and LED Lights Exp Port Interface Ubuntu Distribution and Linux Kernel 17

Relative Inspection Three phase approach to algorithm development 1) Initial inspection of an from a safe distance with an expectation of near global coverage of the target object 2) Pause

perform a closer inspection Phase 1 Initial ISS Tests Circumnavigation while performing data collection Simple estimation and control algorithms Sensor Inputs Vision Relative [X,Y,Z]

18 Relative Inspection Three phase approach to algorithm development 1) Initial inspection of an from a safe distance with an expectation of near global coverage of the target object 2) Pause to process the inspection data & build a 3D map of the target using techniques such as bundle adjustment or simultaneous localization and mapping 3) Relative navigation using the 3D map to perform a closer inspection Phase 1 Initial ISS Tests Circumnavigation while performing data collection Simple estimation and control algorithms Sensor Inputs Vision Relative [X,Y,Z] position geometric center of object Inertial 3 axis gyroscope measurements Control Maintain relative distance Maintain object in view Move around object to collect data from different viewpoints 18

19 ISS Relative Motion Inspection Maneuvers Moving target Desired (top) and actual (bottom) path INSP in blue, TGT in red Vision inspection algorithm: Meets navigation needs for mapping mission Tool is independent of object rotation/tumbling Gyroscope noise is not a significant factor on the satellites 19

VBN Development (1) Spinning Target Estimation Estimate linear and angular velocities as well as Center of Mass and Inertia Matrix up to a scale factor (mass) with modified isam Generic Dynamic Model

20 VBN Development (1) Spinning Target Estimation Estimate linear and angular velocities as well as Center of Mass and Inertia Matrix up to a scale factor (mass) with modified isam Generic Dynamic Model for isam and Linear/Angular Velocity Estimation Likely requires inertial measurements such as accelerometer or gyroscope Working on validation using Lie-Derivative based Rank Test for Local Weak Observability Non-Textured and Specular Object Image Processing Point features do not work on un-textured objects or objects with specular reflections Working on methods that utilize dense (SAD) map with global localization methods in Spherical Harmonic space 20

21 VBN Development (2) 21

funding MOSR Mars-Orbital Sample Return Demonstration of capture of orbital sample using only vision Currently in 2D

22 Future Work VERTIGO Launch to ISS October 2012 Operations Winter 12 / Spring 13 Long-term use aboard ISS ARMADAS ISS follow-on to SWARM Launch of UDP s to ISS Assembly of complex structure Expected in 2013 or 2014 Approved, pending funding MOSR Mars-Orbital Sample Return Demonstration of capture of orbital sample using only vision Currently in 2D ground ops May be ISS project ~ 2014 Fluid Transfer Initial research, jointly with ERAU, to demonstrate fluid transfer aboard ISS 22

23 How to participate on ISS is an official facility of the ISS National Laboratory Office on ISS is managed by NASA Ames Research Center For projects with existing funding: Opportunity For The Use Of The International Space Station By Domestic Entities Other Than U.S. Federal Government Agencies No funds exchanged, but creates a vehicle (Space Act Agreement or similar) by which NASA ARC supports the tasks for the investigator Open until 2014 For projects without existing funding: Expected SBIR and STTR calls for grants to conduct science aboard the ISS Stay in touch with NASA ARC team! "Martinez, Andres (ARC-RE)" <andres.martinez@nasa.gov> 23

Team Information NASA ARC Bruce Yost, Program Manager Andres Martinez, Project Manager Steve Ormsby, Operations Lead Mark Micire, Engineering Lead MIT Investigators Prof. David W.

24 Team Information NASA ARC Bruce Yost, Program Manager Andres Martinez, Project Manager Steve Ormsby, Operations Lead Mark Micire, Engineering Lead MIT Investigators Prof. David W. Miller, PI Alvar Saenz Otero, Lead Scientist MIT Science Team Jacob Katz (PhD) (SWARM, Zero Robotics) Brent Tweddle (PhD) (VERTIGO) Michael O Conner (MS) (VERTIGO) Alexander Buck (MS) (RINGS) Gregory Eslinger (MS) (RINGS) Vishnu Jyothindran (MS) (MOSR) Alumns showcased in presentation: Simon Nolet (Docking) Swati Mohan (SWARM) Amer Fejzic (Docking) Lenon Rodgers (UDP) AFS Hardware Integration & Program Management John Merk spheres@mit.edu 24

Development of Formation Flight and Docking Algorithms Using the SPHERES Testbed

Development of Formation Flight and Docking Algorithms Using the Testbed Prof. David W. Miller MIT Allen Chen, Alvar Saenz-Otero, Mark Hilstad, David W. Miller Introduction : Synchronized Position Hold