Andrew Bylard

Andrew Bylard


Andrew Bylard is a Ph.D. candidate in Aeronautics and Astronautics. He received his B.Eng. with a dual concentration of Mechanical Engineering and Electrical Engineering from Walla Walla University in 2014, and a M.Sc. in Aeronautics and Astronautics from Stanford University in 2016.

Andrew’s research interests include geometric methods for reactive control and structured robot task planning, real-time spacecraft motion-planning, and use of gecko-inspired controllable dry adhesives for space grasping and manipulation. He has been a lead developer at the Stanford Space Robotics Facility, which houses test beds used to perform spacecraft contact dynamics experiments and demonstrate autonomous spacecraft robotics and proximity operations under simulated frictionless, microgravity conditions.

Outside work, Andrew enjoys photography, piano, drums, philosophy, history, and learning languages.

Awards:

  • NASA Space Technology Research Fellowship, 2015

ASL Publications

  1. M. Mote, M. Egerstedt, E. Feron, A. Bylard, and M. Pavone, “Collision-Inclusive Trajectory Optimization for Free-Flying Spacecraft,” AIAA Journal of Guidance, Control, and Dynamics, 2020. (In Press)

    Abstract: In systems where collisions can be tolerated, permitting and optimizing collisions in vehicle trajectories can enable a richer set of possible behaviors, allowing both better performance and determination of safest courses of action in scenarios where collision is inevitable. This paper develops an approach for optimal trajectory planning on a three degree-of-freedom free-flying spacecraft having tolerance to collisions. First, we use experimental data to formulate a physically realistic collision model for the spacecraft. We show that this model is linear over the expected operational range, enabling a piecewise affine representation of the full hybrid-vehicle dynamics. Next, we incorporate this dynamics model along with vehicle constraints into a mixed integer program. Experimental comparisons of trajectories with and without collision-avoidance requirements demonstrate the capability of the collision-tolerant strategy to achieve significant performance improvements in realistic scenarios. A simulated case study illustrates the potential for this approach to find damage-mitigating paths in online implementations.

    @article{MoteEgerstedtEtAl2020,
      author = {Mote, M. and Egerstedt, M. and Feron, E. and Bylard, A. and Pavone, M.},
      title = {Collision-Inclusive Trajectory Optimization for Free-Flying Spacecraft},
      journal = {{AIAA Journal of Guidance, Control, and Dynamics}},
      year = {2020},
      note = {In press},
      url = {/wp-content/papercite-data/pdf/Mote.ea.JGCD.2020.preprint.pdf},
      keywords = {press},
      owner = {bylard},
      timestamp = {2020-02-27}
    }
    
  2. R. Bonalli, A. Bylard, A. Cauligi, T. Lew, and M. Pavone, “Trajectory Optimization on Manifolds: A Theoretically-Guaranteed Embedded Sequential Convex Programming Approach,” in Robotics: Science and Systems, Freiburg im Breisgau, Germany, 2019.

    Abstract: Sequential Convex Programming (SCP) has recently gain popularity as a tool for trajectory optimization, due to its sound theoretical properties and practical performance. Yet, most SCP-based methods for trajectory optimization are restricted to Euclidean settings, which precludes their application to problem instances where one needs to reason about manifold-type constraints (that is, constraints, such as loop closure, which restrict the motion of a system to a subset of the ambient space). The aim of this paper is to fill this gap by extending SCP-based trajectory optimization methods to a manifold setting. The key insight is to leverage geometric embeddings to lift a manifold-constrained trajectory optimization problem into an equivalent problem defined over a space enjoying Euclidean structure. This insight allows one to extend existing SCP methods to a manifold setting in a fairly natural way. In particular, we present an SCP algorithm for manifold problems with theoretical guarantees that resemble those derived for the Euclidean setting, and demonstrate its practical performance via numerical experiments.

    @inproceedings{BonalliBylardEtAl2019,
      author = {Bonalli, R. and Bylard, A. and Cauligi, A. and Lew, T. and Pavone, M.},
      title = {Trajectory Optimization on Manifolds: {A} Theoretically-Guaranteed Embedded Sequential Convex Programming Approach},
      booktitle = {{Robotics: Science and Systems}},
      year = {2019},
      address = {Freiburg im Breisgau, Germany},
      month = jun,
      url = {https://arxiv.org/pdf/1905.07654.pdf},
      owner = {bylard},
      timestamp = {2019-05-01}
    }
    
  3. R. Bonalli, A. Cauligi, A. Bylard, and M. Pavone, “GuSTO: Guaranteed Sequential Trajectory Optimization via Sequential Convex Programming,” in Proc. IEEE Conf. on Robotics and Automation, Montreal, Canada, 2019.

    Abstract: Sequential Convex Programming (SCP) has recently seen a surge of interest as a tool for trajectory optimization. Yet, most available methods lack rigorous performance guarantees and are often tailored to specific optimal control setups. In this paper, we present GuSTO (Guaranteed Sequential Trajectory Optimization), an algorithmic framework to solve trajectory optimization problems for control-affine systems with drift. GuSTO generalizes earlier SCP-based methods for trajectory optimization (by addressing, for example, goal region constraints and problems with either fixed or free final time), and enjoys theoretical convergence guarantees in terms of convergence to, at least, a stationary point. The theoretical analysis is further leveraged to devise an accelerated implementation of GuSTO, which originally infuses ideas from indirect optimal control into an SCP context. Numerical experiments on a variety of trajectory optimization setups show that GuSTO generally outperforms current state-of-the-art approaches in terms of success rates, solution quality, and computation times.

    @inproceedings{BonalliCauligiEtAl2019,
      author = {Bonalli, R. and Cauligi, A. and Bylard, A. and Pavone, M.},
      title = {{GuSTO:} Guaranteed Sequential Trajectory Optimization via Sequential Convex Programming},
      booktitle = {{Proc. IEEE Conf. on Robotics and Automation}},
      year = {2019},
      address = {Montreal, Canada},
      month = may,
      url = {https://arxiv.org/pdf/1903.00155.pdf},
      owner = {bylard},
      timestamp = {2018-10-04}
    }
    
  4. T. Zahroof, A. Bylard, H. Shageer, and M. Pavone, “Perception-Constrained Robot Manipulator Planning for Satellite Servicing,” in IEEE Aerospace Conference, Big Sky, Montana, 2019.

    Abstract: Satellite servicing is a rapidly developing industry which requires a number advances in semi- and fully-automated space robotics to unlock many key servicing capabilities. One upcoming mission example is the NASA Restore-L Robotic Servicing spacecraft, which is equipped with two 7-joint robotic manipulators used to capture a satellite and perform a complex series of refueling tasks, including swapping between various end-effector tools stored on board. In this scenario, planning of the manipulator motions must account for a number of constraints, such as collision avoidance and the potential need for uninterrupted visual tracking of objects or of the end-effector. Such complex constraints in a cluttered environment, such as the interface between two spacecraft, are time-consuming to incorporate into hand-designed trajectories. Thus, in this work we present a software tool which uses robot motion planning and path refinement algorithms for automated, real-time computation of near-optimal, collision-free trajectories which satisfy the aforementioned perception constraints. The tool is built on the ROS MoveIt! framework, which can simulate and visualize trajectories as well as seamlessly switch between motion planning and refinement algorithms depending on task requirements. Additionally, we performed experimental campaigns to benchmark a number of available algorithms for performance in handling such perception constraints. Although the framework is applied to a mock-up of Restore-L satellite servicer in this paper, the tool can be applied to any fixed-base manipulator planning scenario with a similar class of constraints.

    @inproceedings{ZahroofBylardEtAl2019,
      author = {Zahroof, T. and Bylard, A. and Shageer, H. and Pavone, M.},
      title = {Perception-Constrained Robot Manipulator Planning for Satellite Servicing},
      booktitle = {{IEEE Aerospace Conference}},
      year = {2019},
      address = {Big Sky, Montana},
      month = mar,
      url = {/wp-content/papercite-data/pdf/Zahroof.Bylard.Shageer.Pavone.AeroConf19.pdf},
      owner = {bylard},
      timestamp = {2019-01-14}
    }
    
  5. R. MacPherson, B. Hockman, A. Bylard, M. A. Estrada, M. R. Cutkosky, and M. Pavone, “Trajectory Optimization for Dynamic Grasping in Space using Adhesive Grippers,” in Field and Service Robotics, Zurich, Switzerland, 2017.

    Abstract: Spacecraft equipped with gecko-inspired dry adhesive grippers can dynamically grasp objects having a wide variety of featureless surfaces. In this paper we propose an optimization-based control strategy to exploit the dynamic robustness of such grippers for the task of grasping a free-floating, spinning object. First, we extend previous work characterizing the dynamic grasping capabilities of these grippers to the case where both object and spacecraft are free-floating and comparably sized. We then formulate the acquisition problem as a two-phase optimal control problem, which is amenable to real time implementation and can handle constraints on velocity, control, as well as integer timing constraints for grasping a specific target location on the surface of a spinning object. Conservative analytical bounds on the set of initial states that guarantee persistent feasibility are derived.

    @inproceedings{MacPhersonHockmanEtAl2017,
      author = {MacPherson, R. and Hockman, B. and Bylard, A. and Estrada, M. A. and Cutkosky, M. R. and Pavone, M.},
      title = {Trajectory Optimization for Dynamic Grasping in Space using Adhesive Grippers},
      booktitle = {{Field and Service Robotics}},
      year = {2017},
      address = {Zurich, Switzerland},
      month = sep,
      url = {/wp-content/papercite-data/pdf/MacPherson.Hockman.Bylard.ea.FSR17.pdf},
      owner = {bylard},
      timestamp = {2018-01-16}
    }
    
  6. A. Bylard, R. MacPherson, B. Hockman, M. R. Cutkosky, and M. Pavone, “Robust Capture and Deorbit of Rocket Body Debris Using Controllable Dry Adhesion,” in IEEE Aerospace Conference, Big Sky, Montana, 2017.

    Abstract: Removing large orbital debris in a safe, robust, and cost-effective manner is a long-standing challenge, having serious implications for LEO satellite safety and access to space. Many studies have focused on the deorbit of spent rocket bodies (R/Bs) as an achievable and high-priority first step. However, major difficulties arise from the R/Bs’ residual tumble and lack of traditional docking/grasping fixtures. Previously investigated docking strategies often require complex and risky approach maneuvers or have a high chance of producing additional debris. To address this challenge, this paper investigates the use of controllable dry adhesives (CDAs), also known as gecko-inspired adhesives, as an alternative approach to R/B docking and deorbiting. CDAs are gathering interest for in-space grasping and manipulation due to their ability to controllably attach to and detach from any smooth, clean surface, including flat and curved surfaces. Such capability significantly expands the number and types of potential docking locations on a target. CDAs are also inexpensive, are space-qualified (performing well in a vacuum, in extreme temperatures, and under radiation), and can attach and detach while applying minimal force to a target surface, all important considerations for space deployment. In this paper, we investigate a notional strategy for initial capture and stabilization of a R/B having multi-axis tumble, exploiting the unique properties of CDA grippers to reduce maneuver complexity, and we propose alternatives for rigidly attaching deorbiting kits to a R/B. Simulations based on experimentally verified models of CDA grippers show that these approaches show promise as robust alternatives to previously explored methods.

    @inproceedings{BylardMacPhersonEtAl2017,
      author = {Bylard, A. and MacPherson, R. and Hockman, B. and Cutkosky, M. R. and Pavone, M.},
      title = {Robust Capture and Deorbit of Rocket Body Debris Using Controllable Dry Adhesion},
      booktitle = {{IEEE Aerospace Conference}},
      year = {2017},
      address = {Big Sky, Montana},
      month = mar,
      url = {/wp-content/papercite-data/pdf/Bylard.MacPherson.Hockman.ea.AeroConf17.pdf},
      owner = {bylard},
      timestamp = {2017-03-07}
    }
    
  7. M. A. Estrada, B. Hockman, A. Bylard, E. W. Hawkes, M. R. Cutkosky, and M. Pavone, “Free-Flyer Acquisition of Spinning Objects with Gecko-Inspired Adhesives,” in Proc. IEEE Conf. on Robotics and Automation, Stockholm, Sweden, 2016.

    Abstract: We explore the use of grippers with gecko-inspired adhesives for spacecraft docking and acquisition of tumbling objects in microgravity. Towards the goal of autonomous object manipulation in space, adhesive grippers mounted on planar free-floating platforms are shown to be tolerant of a range of incoming linear and angular velocities. Through modeling, simulations, and experiments, we characterize the dynamic “grasping envelope” for successful acquisition and derive insights to inform future gripper designs and grasping strategies for motion planning.

    @inproceedings{EstradaHockmanEtAl2016,
      author = {Estrada, M. A. and Hockman, B. and Bylard, A. and Hawkes, E. W. and Cutkosky, M. R. and Pavone, M.},
      title = {Free-Flyer Acquisition of Spinning Objects with Gecko-Inspired Adhesives},
      booktitle = {{Proc. IEEE Conf. on Robotics and Automation}},
      year = {2016},
      address = {Stockholm, Sweden},
      doi = {10.1109/ICRA.2016.7487696},
      month = may,
      url = {/wp-content/papercite-data/pdf/Estrada.Hockman.Bylard.ea.ICRA16.pdf},
      owner = {bylard},
      timestamp = {2017-01-28}
    }