Applying Model Predictive Control Architecture for Efficient Autonomous Data Collection and Operations on Planetary Missions

Transcription

1 Applying Model Predictive Control Architecture for Efficient Autonomous Data Collection and Operations on Planetary Missions M. Lieber, E. Schindhelm, R. Rohrschneider, C. Weimer, S. Roark Cahill Center for Astronomy and Astrophysics August 2017

2 Overview Model Predictive Control(MPC) - Optimized, autonomous data collection for planetary missions What is MPC and why is it relevant to planetary missions Ball relevant technology development Multi-beam adaptive lidar application developed Predictive control architecture Target application for Mars mission Challenges to implementation Page_2

3 Model Predictive Control(MPC) - Optimized, Autonomous Data Collection for Planetary Missions LCPM stands to benefit from recent developments in miniaturization of satellite technology (e.g. CubeSats) and an increase in autonomous operations. MPC is an ideal framework for autonomous, real-time control of complex systems, in part, because it re-optimizes actions with respect to multiple goals and constraints at every time step. Technology Readiness Level (TRL) = 4, illustrated for adaptive, multibeam imaging lidar with Predictive Control Architecture for Earth Science (PCAES) funded by NASA. We have developed an initial prototype MPC architecture which can optimize data collection with adaptive lidar systems using a data-driven or scene-based approach. Also NASA and Ball Internal funding has developed a 3D imaging lidar with real-time adaptive multi-beam lidar hardware for the Asteroid Redirect Mission. This HW configuration was tested on the STOORM mission in 2011 and part of the RAVEN technology demonstration on the International Space station. Vision Navigation System (VNS) Page_3

4 MPC and it s History of Applications MPC was invented in the process industry. Large, complex, multivariable systems with constraints (flow rates, volumes, temperatures, etc), multi-level or hierarchical but with very slow time constants. It s an architecture not a set controller. Although there has not been problems with systems going unstable, the first stability proofs were not developed until the mid-late 90 s. Two things have been a deterrent to adoption computational burden and technology migration. Technology still evolving many forms. Proposed Robotic vision systems Autonomous vehicles & rovers Process industries Economics modeling Wastewater treatment Power grids Automotive systems Formation flying Adaptive lidar and real-time decisions SMD/ Planetary Science applications Computational capability Biological systems Rendezvous & docking 1970, 1980 mainframe computers Space-qualified, FPGA and heterogeneous processing Page_4

5 Fundamental Problem Being Solved Using Adaptive and autonomous Sensor Systems A common theme that emerges is that adaptive sensing should be formulated as the process of evaluating different sensing actions that the robot (..instrument) can take and choosing the action that maximizes the information acquired by the robot (..instrument). The challenge in implementing this concept in practice is to develop a methodology for quantifying the expected information for different sensing actions and evaluate them in a computationally feasible manner given limited a priori information. ** [Feder1999] Feder, J.S.H., Leonard, J. J., Smith, C. M., Adaptive Mobile Robot Navigation and Mapping, IJRR, 18(7), 1999, ** As noted, we borrow heavily from rapid developments in autonomous controlled & complex ground-based systems (cars, mobile robots, etc.) Page_5

6 than the 4.7 seconds shown, the evolution of the 10x10 power maps would be more apparent. This simplistic case was done without any control (except human) the MPC optimizer would be evolving a predicted map from the camera and adjusting the power map according to con- Technology Development Ball Internal R&D and NASA Funding straints and weights. The actual power map would have a threshold applied and so that many of Ball development of adaptive lidar with independent, multiple beamlet control. Electronically Steerable Flash Lidar (ESFL) Predictive Control Architecture Developed initially under Ball internal R&D funding. Funded for further development under the ESTO AIST program as Model Predictive Control Architecture for Optimizing Earth Science Data Collection (PCAES) Application directed to autonomous and optimal control of multi-beam lidar. Data driven modeling approach (MPC comes in many different forms) the power maps pixels shown in Figure 4 would in fact have a zero power allocation. The important point is that the fast layer of the MPC controller needs to operate on the space-based platform to keep up with the rapidly changing scene dynamics. In Section 1.3 we compare MPC to other technologies but here we describe in more detail it s features and challenges. Model predictive control possesses some attractive characteristics as a candidate ESFL control framework. Firstly, because model predictive control re-plans optimal beamlet directions and power allocation at each sampling instant, it is conceivable that information extraction can be maximized in the presence of uncertainty in comparison to higher-level A Ball Aerospace & Technologies Corp. Proprietary Information 6 Use or disclosure of the information contained in this proposal is subject to the restrictions on the title page of this document. Page_6

7 MPC for Optimizing Science Data Collection Real-time scene classification/ understanding for limited set of features. Reduces dimensionality for known objects data driven approach while retaining predictive dynamics feature. Scene classification algorithms can be tailored Deep Learning synergy Single adaptive instrument with hierarchical control Distributed adaptive instrument with hierarchical control Page_7

8 Directed Application - Multi-Beam Adaptive Lidar We apply MPC architecture to an electronically steerable flash lidar (ESFL) AOM controls amplitude and angle (frequency) of each beamlet independently. Spatial light modulator also demonstrated for 2D multi-beamlet control Laser pulse frequency and total power levels are constants. 2D multi-beam steering Constraints: Total power available SNR Steering angle Page_8

9 Challenges to Implementation a Predictive Control Architecture for LCPM MPC is a computationally intensive control architecture. Verification/ validation requires different testing processes than systems that do not possess adaptive or autonomous capability. Each planetary body has it s own unique science opportunities and phenomenological characteristics requiring different mission approaches. Orbiting, flyby, roving, multiple interacting sensor platforms, transient. Field Programmable Gate Array (FPGAs) and heterogeneous computing platforms SpaceCube and SpaceCubeX. Support >30 Hz updates for scene classification and prediction modeling. Mission analysis very unique Page_9

10 Example Earth Remote Sensing application : Adaptive, multi-beam lidar lab demonstration of MPC software Page_10

11 SW Graphical User Interface (GUI) & RT Control Interface Nominal ground track nadir pointing lidar Output to projection system Instantaneous FOV of multibeam lidar Landsat Vis/ IR bands (darkened bands are scene classification & cloud ID Scene classes available Test/ SW validation patterns for calibration User selects scene from folder Enables RT control of SLM Optimized data collection metric (see next chart) Page_11

12 PCAES Lab Interface Software RT Control of Multi-beam Laser using Scene ID from Multiple Spectral Bands Double click image to the right to start short video demonstrating the user interface to the lab hardware. Selections by user of data file and settings are shown also. Page_12

13 Utilizing MPC with Adaptive LIDAR at Mars Scene classifier to use multispectral (VIS/IR) or spatial information from changing terrain as metric to guide laser pointing, therefore optimizing efficiency 2D adaptive LIDAR could find range, adjust gates for higher precision measurements without requiring onboard DEMs or advanced planning Swiss cheese terrain, Christensen 2006 Lobes of ice-rich material from THEMIS, Christensen 2006 HiRISE CO 2 /H 2 O band depth ratios, Pommerol et al Page_13

14 Other Applications of a Predictive Control Architecture for Planetary Missions Onboard science operations Looking for features with imaging instruments, honing in with higher spatial/spectral resolution instruments. Record, prioritize, compress and send back data autonomously. Controlling swarm of smallsats/cubesats Radio occultation for atmospheric temperature, density In-situ instruments to sample multiple regions of upper atmosphere at once Bistatic LIDAR from multiple scattering angles increases measurement accuracy Landing/Flyby optimization Adaptive LIDAR for autonomous ranging, hazard avoidance Scout satellite for asteroid flybys provides cross-track information so mothership can autonomously plan/optimize operations/observations Page_14