Joseph A. Starek

Joseph A. Starek


Joseph A. Starek received the Bachelor’s of Science in Engineering (BSE) and the Master’s of Science in Engineering (MSE) degrees in Aerospace Engineering from the University of Michigan, Ann Arbor, in 2009 and 2010, respectively. He earned a Ph.D. degree in Aeronautics and Astronautics from Stanford University, Stanford in June 2016.

In 2009 and 2010, he worked as a Research Assistant at NASA Ames Research Center in rotorcraft aeromechanics testing and small spacecraft propulsion system design, later returning to NASA Ames to join the small spacecraft Mission Design Center for nine months as a Systems Engineer before entering Stanford in the fall of 2011. In the Stanford Autonomous Systems Laboratory (ASL) he conducted research focused on spacecraft dynamics and control, safety-constrained trajectory optimization, and spacecraft motion planning algorithms. He also devotes much of his time to engineering education; he served as an assistant teacher five times over a four-year career at Stanford.

Dr. Starek was a recipient of the Stanford Centennial Teaching Assistant award in 2014. He also received the Distinguished Achievement Award in Aerospace Engineering and the James B. Angell Scholar Award from the University of Michigan in 2010.

Awards:

  • Stanford Centennial Teaching Assistant award, 2014

Currently at Nuro (via Space Systems Loral)

ASL Publications

  1. J. A. Starek, E. Schmerling, G. D. Maher, B. W. Barbee, and M. Pavone, “Fast, Safe, Propellant-Efficient Spacecraft Motion Planning Under Clohessy-Wiltshire-Hill Dynamics,” AIAA Journal of Guidance, Control, and Dynamics, vol. 40, no. 2, pp. 418–438, 2017.

    Abstract: This paper presents a sampling-based motion planning algorithm for real-time and propellant-optimized autonomous spacecraft trajectory generation in near-circular orbits. Specifically, this paper leverages recent algorithmic advances in the field of robot motion planning to the problem of impulsively-actuated, propellant-optimized rendezvous and proximity operations under the Clohessy-Wiltshire-Hill (CWH) dynamics model. The approach calls upon a modified version of the Fast Marching Tree (FMT*) algorithm to grow a set of feasible trajectories over a deterministic, low-dispersion set of sample points covering the free state space. To enforce safety, the tree is only grown over the subset of actively-safe samples, from which there exists a feasible one-burn collision avoidance maneuver that can safely circularize the spacecraft orbit along its coasting arc under a given set of potential thruster failures. Key features of the proposed algorithm include: (i) theoretical guarantees in terms of trajectory safety and performance, (ii) amenability to real-time implementation, and (iii) generality, in the sense that a large class of constraints can be handled directly. As a result, the proposed algorithm offers the potential for widespread application, ranging from on-orbit satellite servicing to orbital debris removal and autonomous inspection missions.

    @article{StarekSchmerlingEtAl2016,
      author = {Starek, J. A. and Schmerling, E. and Maher, G. D. and Barbee, B. W. and Pavone, M.},
      title = {Fast, Safe, Propellant-Efficient Spacecraft Motion Planning Under {Clohessy}-{Wiltshire}-{Hill} Dynamics},
      journal = {{AIAA Journal of Guidance, Control, and Dynamics}},
      volume = {40},
      number = {2},
      pages = {418--438},
      year = {2017},
      doi = {10.2514/1.g001913},
      url = {/wp-content/papercite-data/pdf/Starek.Schmerling.ea.JGCD16.pdf},
      owner = {bylard},
      timestamp = {2017-01-28}
    }
    
  2. J. A. Starek, B. Acikmese, I. A. D. Nesnas, and M. Pavone, “Spacecraft Autonomy Challenges for Next Generation Space Missions,” in Advances in Control System Technology for Aerospace Applications, Springer, 2016.

    Abstract: In early 2011, NASA’s Office of the Chief Technologist released a set of technology roadmaps with the aim of fostering the development of concepts and cross-cutting technologies addressing NASA’s needs for the 2011-2021 decade and beyond. NASA reached out to the National Research Council (NRC) to review the program objectives and prioritize the list of technologies. In January 2012, the NRC released its report entitled “Restoring NASA’s Technological Edge and Paving the Way for a New Era in Space." While the NRC report provides a systematic and thorough ranking of the future technology needs for NASA, it does not discuss in detail the technical aspects of its prioritized technologies (which lie beyond its scope). This chapter, building upon this framework, aims at providing such technical details for a selected number of high-priority technologies in the autonomous systems area. Specifically, this chapter focuses on technology area TA04 “Robotics, Tele-Robotics, and Autonomous Systems" and discusses in some detail the technical aspects and challenges associated with three high-priority TA04 technologies: “Relative Guidance Algorithms," “Extreme Terrain Mobility," and “Small Body/Microgravity Mobility." Each of these technologies is discussed along four main dimensions: scope, need, state-of-the-art, and challenges/future directions. The result is a unified presentation of key autonomy challenges for next-generation space missions.

    @incollection{StarekAcikmeseEtAl2016,
      author = {Starek, J. A. and Acikmese, B. and Nesnas, I. A. D. and Pavone, M.},
      title = {Spacecraft Autonomy Challenges for Next Generation Space Missions},
      booktitle = {Advances in Control System Technology for Aerospace Applications},
      publisher = {{Springer}},
      year = {2016},
      chapter = {1},
      doi = {10.1007/978-3-662-47694-9_1},
      month = sep,
      owner = {bylard},
      timestamp = {2017-01-28},
      url = {/wp-content/papercite-data/pdf/Starek.Acikmese.ea.ACSTAA16.pdf}
    }
    
  3. J. A. Starek, “Sampling-Based Motion Planning for Safe and Efficient Spacecraft Proximity Operations,” PhD thesis, Stanford University, Dept. of Aeronautics and Astronautics, Stanford, California, 2016.

    Abstract: Autonomy has demonstrated success in many vehicle control problems, but has yet to show significant breakthroughs for spacecraft guidance during proximity operations. In part due to a costly verification and validation process as well as from limited access to formally-safe guidance algorithms, mission planners have instead had to rely on maneuver plans with straightforward, easily-verified trajectories and extensive human oversight. Unfortunately, this strategy often introduces propellant inefficiencies, adds significant labor overhead, and limits missions to Earth proximity where two-way communication times are short. This dissertation seeks to remedy these issues by developing a provably-safe and propellant-efficient sampling-based motion planning framework for fully-autonomous spacecraft proximity operations. The framework is designed for a wide range of hazardous guidance scenarios, including autonomous orbital rendezvous and inspection, pinpoint small-body descent, and on-orbit satellite servicing. Due to the dangers associated with operating near other objects, special care is taken to enable real-time guidance as well as ensure the availability of safe abort trajectories so that spacecraft can respond quickly and safely to control failures and sudden environmental changes. Through its generality, efficiency, and speed, the proposed approach offers the potential to enable entirely new capabilities for next-generation space missions, while also increasing the frequency, flexibility, and reliability of present-day operations in space.

    @phdthesis{Starek2016,
      author = {Starek, J. A.},
      title = {Sampling-Based Motion Planning for Safe and Efficient Spacecraft Proximity Operations},
      school = {{Stanford University, Dept. of Aeronautics and Astronautics}},
      year = {2016},
      address = {Stanford, California},
      month = jun,
      url = {/wp-content/papercite-data/pdf/Starek.PhD16.pdf},
      owner = {bylard},
      timestamp = {2017-03-07}
    }
    
  4. J. A. Starek, E. Schmerling, G. D. Maher, B. W. Barbee, and M. Pavone, “Real-Time, Propellant-Optimized Spacecraft Motion Planning under Clohessy-Wiltshire-Hill Dynamics,” in IEEE Aerospace Conference, Big Sky, Montana, 2016.

    Abstract: This paper presents a sampling-based motion planning algorithm for real-time, propellant-optimized autonomous spacecraft trajectory generation in near-circular orbits. Specifically, this paper leverages recent algorithmic advances in the field of robot motion planning to the problem of impulsively-actuated, propellant-optimized rendezvous and proximity operations under the Clohessy-Wiltshire-Hill (CWH) dynamics model. The approach calls upon a modified version of the Fast Marching Tree (FMT*) algorithm to grow a set of feasible and actively-safe trajectories over a deterministic, low-dispersion set of sample points covering the free state space. Key features of the proposed algorithm include: (i) theoretical guarantees of trajectory safety and performance, (ii) real-time implementability, and (iii) generality, in the sense that a large class of constraints can be handled directly. As a result, the proposed algorithm offers the potential for widespread application, ranging from on-orbit satellite servicing to orbital debris removal and autonomous inspection missions.

    @inproceedings{StarekSchmerlingEtAl2016b,
      author = {Starek, J. A. and Schmerling, E. and Maher, G. D. and Barbee, B. W. and Pavone, M.},
      title = {Real-Time, Propellant-Optimized Spacecraft Motion Planning under {Clohessy-Wiltshire-Hill} Dynamics},
      booktitle = {{IEEE Aerospace Conference}},
      year = {2016},
      address = {Big Sky, Montana},
      doi = {10.1109/aero.2016.7500704},
      month = mar,
      owner = {bylard},
      timestamp = {2017-01-28},
      url = {/wp-content/papercite-data/pdf/Starek.Schmerling.ea.AeroConf16.pdf}
    }
    
  5. J. A. Starek, J. V. Gomez, E. Schmerling, L. Janson, L. Moreno, and M. Pavone, “An Asymptotically-Optimal Sampling-Based Algorithm for Bi-directional Motion Planning,” in IEEE/RSJ Int. Conf. on Intelligent Robots & Systems, Hamburg, Germany, 2015.

    Abstract: Bi-directional search is a widely used strategy to increase the success and convergence rates of sampling-based motion planning algorithms. Yet, few results are available that merge both bi-directional search and asymptotic optimality into existing optimal planners, such as PRM*, RRT*, and FMT*. The objective of this paper is to fill this gap. Specifically, this paper presents a bi-directional, sampling-based, asymptotically-optimal algorithm named Bi-directional FMT* (BFMT*) that extends the Fast Marching Tree (FMT*) algorithm to bidirectional search while preserving its key properties, chiefly lazy search and asymptotic optimality through convergence in probability. BFMT* performs a two-source, lazy dynamic programming recursion over a set of randomly-drawn samples, correspondingly generating two search trees: one in cost-to-come space from the initial configuration and another in cost-to-go space from the goal configuration. Numerical experiments illustrate the advantages of BFMT* over its unidirectional counterpart, as well as a number of other state-of-the-art planners.

    @inproceedings{StarekGomezEtAl2015,
      author = {Starek, J. A. and Gomez, J. V. and Schmerling, E. and Janson, L. and Moreno, L. and Pavone, M.},
      title = {An Asymptotically-Optimal Sampling-Based Algorithm for Bi-directional Motion Planning},
      booktitle = {{IEEE/RSJ Int. Conf. on Intelligent Robots \& Systems}},
      year = {2015},
      address = {Hamburg, Germany},
      doi = {10.1109/IROS.2015.7353652},
      month = sep,
      url = {/wp-content/papercite-data/pdf/Starek.Gomez.ea.IROS15.pdf},
      owner = {bylard},
      timestamp = {2017-03-07}
    }
    
  6. J. A. Starek, B. W. Barbee, and M. Pavone, “A Sampling-Based Approach to Spacecraft Autonomous Maneuvering with Safety Specifications,” in AAS GN&C Conference, Breckenridge, Colorado, 2015.

    Abstract: This paper presents a method for safe spacecraft autonomous maneuvering that leverages robotic motion planning techniques to spacecraft control. Specifically, the scenario we consider is an in-plane rendezvous of a chaser spacecraft in proximity to a target spacecraft at the origin of the Clohessy-Wiltshire-Hill frame. The trajectory for the chaser spacecraft is generated in a receding-horizon fashion by executing a sampling-based robotic motion planning algorithm named Fast Marching Trees (FMT*), which efficiently grows a tree of trajectories over a set of probabilistically-drawn samples in the state space. To enforce safety, the tree is only grown over actively safe samples, from which there exists a one-burn collision avoidance maneuver that circularizes the spacecraft orbit along a collision-free coasting arc and that can be executed under potential thruster failures. The overall approach establishes a provably-correct framework for the systematic encoding of safety specifications into the spacecraft trajectory generation process and appears promising for real-time implementation on orbit. Simulation results are presented for a two-fault tolerant spacecraft during autonomous approach to a single client in Low Earth Orbit.

    @inproceedings{StarekBarbeeEtAl2015,
      author = {Starek, J. A. and Barbee, B. W. and Pavone, M.},
      title = {A Sampling-Based Approach to Spacecraft Autonomous Maneuvering with Safety Specifications},
      booktitle = {{AAS GN\&C Conference}},
      year = {2015},
      address = {Breckenridge, Colorado},
      month = feb,
      owner = {bylard},
      timestamp = {2017-01-28},
      url = {/wp-content/papercite-data/pdf/Starek.Barbee.ea.AASGNC15.pdf}
    }
    
  7. R. Allen, A. Clark, J. A. Starek, and M. Pavone, “A Machine Learning Approach for Real-time Computation of Dynamical System Reachability Sets,” in IEEE/RSJ Int. Conf. on Intelligent Robots & Systems, Chicago, Illinois, 2014.

    Abstract: Assessing reachability for a dynamical system, that is deciding whether a certain state is reachable from a given initial state within a given cost threshold, is a central concept in controls, robotics, and optimization. Direct approaches to assess reachability involve the solution to a two-point boundary value problem (2PBVP) between a pair of states. Alternative, indirect approaches involve the characterization of reachable sets as level sets of the value function of an appropriate optimal control problem. Both methods solve the problem accurately, but are computationally intensive and do no appear amenable to real-time implementation for all but the simplest cases. In this work, we leverage machine learning techniques to devise query-based algorithms for the approximate, yet real-time solution of the reachability problem. Specifically, we show that with a training set of pre-solved 2PBVP problems, one can accurately classify the cost-reachable sets of a differentially-constrained system using either (1) locally-weighted linear regression or (2) support vector machines. This novel, query-based approach is demonstrated on two systems: the Dubins car and a deep-space spacecraft. Classification errors on the order of 10% (and often significantly less) are achieved with average execution times on the order of milliseconds, representing 4 orders-of-magnitude improvement over exact methods. The proposed algorithms could find application in a variety of time-critical robotic applications, where the driving factor is computation time rather than optimality.

    @inproceedings{AllenClarkEtAl2014,
      author = {Allen, R. and Clark, A. and Starek, J. A. and Pavone, M.},
      title = {A Machine Learning Approach for Real-time Computation of Dynamical System Reachability Sets},
      booktitle = {{IEEE/RSJ Int. Conf. on Intelligent Robots \& Systems}},
      year = {2014},
      address = {Chicago, Illinois},
      doi = {10.1109/IROS.2014.6942859},
      month = sep,
      owner = {bylard},
      timestamp = {2017-01-28},
      url = {/wp-content/papercite-data/pdf/Allen.Clark.Starek.ea.IROS2014.pdf}
    }