Projects

The goal of the Autonomous Systems Laboratory (ASL) is the development of methodologies for the analysis, design, and control of autonomous systems, with a particular emphasis on self-driving cars, autonomous aerospace vehicles, and future mobility systems. The lab combines expertise from control theory, robotics, optimization, and machine learning to develop the theoretical foundations for networked autonomous systems operating in uncertain, rapidly-changing, and potentially adversarial environments. Theoretical insights are then used to devise practical, computationally-efficient, and provably-correct algorithms for field deployment.

Specifically, the ASL’s current research is along the following main dimensions.


Control of Infinite Dimensional Systems
Real-world autonomous systems often have dynamics that are best described by infinite dimensional systems. For example, PDE-constrained systems that require aerodynamic modeling (UAV control) or structural deformation modeling (soft robotics). Current frameworks for design and simulation of infinite dimensional systems, such as computational fluid dynamics (CFD) and finite element methods (FEM), are well established. However, they often fall short in the context of control applications due to their heavy computational complexity. This project investigates the use of reduced order models to address this shortcoming, especially for use within the framework of model predictive control.

Keywords: Model Predictive Control, Dimensionality Reduction, Reduced Order Modeling

Future Mobility Systems
Investigation of large-scale coordination algorithms for the optimization of future mobility systems, with an emphasis on autonomous mobility on demand (AMoD) – a transformative and rapidly developing mode of transportation wherein fleets of self-driving vehicles transport passengers on demand within a city. Emphasis is placed on accounting for the couplings with other modes of transportation (in the context of an intermodal transportation system) and with other infrastructure (e.g., the power network). This line of research involves collaborations with a number of industry partners, from conceptual studies all the way to field deployments.

Keywords: Autonomous Mobility on Demand, Optimization, Routing

Related Work

Journal Articles

  1. F. Rossi, R. Zhang, Y. Hindy, and M. Pavone, “Routing Autonomous Vehicles in Congested Transportation Networks: Structural Properties and Coordination Algorithms,” Autonomous Robots, vol. 42, no. 7, pp. 1427–1442, 2018.

    Abstract: This paper considers the problem of routing and rebalancing a shared fleet of autonomous (i.e., self-driving) vehicles providing on-demand mobility within a capacitated transportation network, where congestion might disrupt throughput. We model the problem within a network flow framework and show that under relatively mild assumptions the rebalancing vehicles, if properly coordinated, do not lead to an increase in congestion (in stark contrast to common belief). From an algorithmic standpoint, such theoretical insight suggests that the problem of routing customers and rebalancing vehicles can be decoupled, which leads to a computationally-efficient routing and rebalancing algorithm for the autonomous vehicles. Numerical experiments and case studies corroborate our theoretical insights and show that the proposed algorithm outperforms state-of-the-art point-to-point methods by avoiding excess congestion on the road. Collectively, this paper provides a rigorous approach to the problem of congestion-aware, system-wide coordination of autonomously driving vehicles, and to the characterization of the sustainability of such robotic systems.

    @article{RossiZhangEtAl2017,
      author = {Rossi, F. and Zhang, R. and Hindy, Y. and Pavone, M.},
      title = {Routing Autonomous Vehicles in Congested Transportation Networks: Structural Properties and Coordination Algorithms},
      journal = {{Autonomous Robots}},
      volume = {42},
      number = {7},
      pages = {1427--1442},
      year = {2018},
      doi = {10.1007/s10514-018-9750-5},
      url = {../wp-content/papercite-data/pdf/Rossi.Zhang.Hindy.Pavone.AURO17.pdf},
      owner = {frossi2},
      timestamp = {2018-08-07}
    }
    

Conference Articles

  1. M. Tsao, D. Milojevic, C. Ruch, M. Salazar, E. Frazzoli, and M. Pavone, “Model Predictive Control of Ride-sharing Autonomous Mobility on Demand Systems,” in Proc. IEEE Conf. on Robotics and Automation, Montreal, Canada, 2019. (Submitted)

    Abstract: This paper presents a model predictive control (MPC) approach to optimize routes for Ride-sharing Autonomous Mobility-on-Demand (RAMoD) systems, whereby self-driving vehicles provide coordinated on-demand mobility, possibly allowing multiple customers to share a ride. Specifically, we first devise a time-expanded network flow model for RAMoD. Second, leveraging this model, we design a real-time MPC algorithm to optimize the routes of both empty and customer-carrying vehicles, with the goal of optimizing social welfare, namely, a weighted combination of customers’ travel time and vehicles’ mileage. Finally, we present a real-world case study for the city of San Francisco, CA, by using the microscopic traffic simulator MATSim. The simulation results show that a RAMoD system can significantly improve social welfare with respect to a single-occupancy AMoD system, and that the predictive structure of the proposed MPC controller allows it to outperform existing reactive ride-sharing coordination algorithms for RAMoD.

    @inproceedings{TsaoMilojevicEtAl2019,
      author = {Tsao, M. and Milojevic, D. and Ruch, C. and Salazar, M. and Frazzoli, E. and Pavone, M.},
      title = {Model Predictive Control of Ride-sharing Autonomous Mobility on Demand Systems},
      booktitle = {{Proc. IEEE Conf. on Robotics and Automation}},
      year = {2019},
      note = {Submitted},
      address = {Montreal, Canada},
      month = may,
      url = {../wp-content/papercite-data/pdf/Tsao.ea.ICRA19.pdf},
      keywords = {sub},
      owner = {samauro},
      timestamp = {2018-09-16}
    }
    

Robust Trajectory Optimization
Motion planning algorithms for agile robotic systems operating in uncertain environments, with application to self-driving cars, drones, and autonomous spacecraft. Emphasis is placed on real-time implementability (e.g., via massive parallelization on GPUs), on robustness (via techniques from robust model predictive control, convex optimization, and contraction theory), and on formal performance guarantees (via advanced mathematical and statistical tools).

Keywords: Motion Planning, Robust Control, Optimization

Related Work

Conference Articles

  1. S. Singh, V. Sindhwani, J.-J. E. Slotine, and M. Pavone, “Learning Stabilizable Dynamical Systems via Control Contraction Metrics,” in Workshop on Algorithmic Foundations of Robotics, 2018. (In Press)

    Abstract: We propose a novel framework for learning stabilizable nonlinear dynamical systems for continuous control tasks in robotics. The key idea is to develop a new control-theoretic regularizer for dynamics fitting rooted in the notion of stabilizability, which guarantees that the learnt system can be accompanied by a robust controller capable of stabilizing any trajectory that the system can generate. By leveraging tools from contraction theory, statistical learning, and convex optimization, we provide a general and tractable algorithm to learn stabilizable dynamics, which can be applied to complex underactuated systems. We validate the proposed algorithm on a simulated planar quadrotor system and observe that the control-theoretic regularized dynamics model is able to consistently generate and accurately track reference trajectories whereas the model learnt using standard regression techniques, e.g., ridge-regression (RR) does extremely poorly on both tasks. Furthermore, in aggressive flight regimes with high velocity and bank angle, the tracking controller fails to stabilize the trajectory generated by the ridge-regularized model whereas no instabilities were observed using the control-theoretic learned model, even with a small number of demonstration examples. The results presented illustrate the need to infuse standard model-based reinforcement learning algorithms with concepts drawn from nonlinear control theory for improved reliability.

    @inproceedings{SinghSindhwaniEtAl2018,
      author = {Singh, S. and Sindhwani, V. and Slotine, J.-J. E. and Pavone, M.},
      title = {Learning Stabilizable Dynamical Systems via Control Contraction Metrics},
      booktitle = {{Workshop on Algorithmic Foundations of Robotics}},
      year = {2018},
      note = {In Press},
      month = oct,
      url = {https://arxiv.org/abs/1808.00113},
      keywords = {press},
      owner = {ssingh19},
      timestamp = {2018-09-21}
    }
    
  2. S. Singh, M. Chen, S. L. Herbert, C. J. Tomlin, and M. Pavone, “Robust Tracking with Model Mismatch for Fast and Safe Planning: an SOS Optimization Approach,” in Workshop on Algorithmic Foundations of Robotics, 2018. (In Press)

    Abstract: In the pursuit of real-time motion planning, a commonly adopted practice is to compute a trajectory by running a planning algorithm on a simplified, low-dimensional dynamical model, and then employ a feedback tracking controller that tracks such a trajectory by accounting for the full, high-dimensional system dynamics. While this strategy of planning with model mismatch generally yields fast computation times, there are no guarantees of dynamic feasibility, which hampers application to safety-critical systems. Building upon recent work that addressed this problem through the lens of Hamilton-Jacobi (HJ) reachability, we devise an algorithmic framework whereby one computes, offline, for a pair of "planner" (i.e., low-dimensional) and "tracking" (i.e., high-dimensional) models, a feedback tracking controller and associated tracking bound. This bound is then used as a safety margin when generating motion plans via the low-dimensional model. Specifically, we harness the computational tool of sum-of-squares (SOS) programming to design a bilinear optimization algorithm for the computation of the feedback tracking controller and associated tracking bound. The algorithm is demonstrated via numerical experiments, with an emphasis on investigating the trade-off between the increased computational scalability afforded by SOS and its intrinsic conservativeness. Collectively, our results enable scaling the appealing strategy of planning with model mismatch to systems that are beyond the reach of HJ analysis, while maintaining safety guarantees.

    @inproceedings{SinghChenEtAl2018,
      author = {Singh, S. and Chen, M. and Herbert, S. L. and Tomlin, C. J. and Pavone, M.},
      title = {Robust Tracking with Model Mismatch for Fast and Safe Planning: an {SOS} Optimization Approach},
      booktitle = {{Workshop on Algorithmic Foundations of Robotics}},
      year = {2018},
      note = {In Press},
      month = oct,
      url = {https://arxiv.org/abs/1808.00649},
      keywords = {press},
      owner = {ssingh19},
      timestamp = {2018-09-21}
    }
    

Safe and Uncertainty-Aware Learning
Theoretical foundations of risk-sensitive decision-making and learning. Deployment of safety-critical systems in uncertain environments requires predicting and reacting to rare but potentially disastrous event. Our group focuses on devising risk-sensitive algorithms for various types of real world scenarios. This includes projects to devise algorithms for risk-sensitive planning, for inferring the profile of a risk-sensitive expert (e.g., inverse reinforcement learning, imitation learning), for interactive decision making for self-driving cars (e.g., for traffic weaving scenarios), for safe transfer of control policies from simulation environments to the real world (e.g., autonomous driving in varying weather conditions), and new techniques to merge formal methods with stochastic optimal control and deep learning for high-confidence implementation on safety-critical systems.

Keywords: Reinforcement Learning, Risk-Sensitive Learning, Deep Learning

Related Work

Journal Articles

  1. S. Singh, J. Lacotte, A. Majumdar, and M. Pavone, “Risk-sensitive Inverse Reinforcement Learning via Semi- and Non-Parametric Methods,” Int. Journal of Robotics Research, 2018. (In Press)

    Abstract: The literature on Inverse Reinforcement Learning (IRL) typically assumes that humans take actions in order to minimize the expected value of a cost function, i.e., that humans are risk neutral. Yet, in practice, humans are often far from being risk neutral. To fill this gap, the objective of this paper is to devise a framework for risk-sensitive IRL in order to explicitly account for a human’s risk sensitivity. To this end, we propose a flexible class of models based on coherent risk measures, which allow us to capture an entire spectrum of risk preferences from risk-neutral to worst-case. We propose efficient non-parametric algorithms based on linear programming and semi-parametric algorithms based on maximum likelihood for inferring a human’s underlying risk measure and cost function for a rich class of static and dynamic decision-making settings. The resulting approach is demonstrated on a simulated driving game with ten human participants. Our method is able to infer and mimic a wide range of qualitatively different driving styles from highly risk-averse to risk-neutral in a data-efficient manner. Moreover, comparisons of the Risk-Sensitive (RS) IRL approach with a risk-neutral model show that the RS-IRL framework more accurately captures observed participant behavior both qualitatively and quantitatively, especially in scenarios where catastrophic outcomes such as collisions can occur.

    @article{SinghLacotteEtAl2018,
      author = {Singh, S. and Lacotte, J. and Majumdar, A. and Pavone, M.},
      title = {Risk-sensitive Inverse Reinforcement Learning via Semi- and Non-Parametric Methods},
      journal = {{Int. Journal of Robotics Research}},
      year = {2018},
      note = {In Press},
      url = {https://arxiv.org/pdf/1711.10055.pdf},
      keywords = {press},
      owner = {ssingh19},
      timestamp = {2018-03-30}
    }
    

Conference Articles

  1. J. Harrison, A. Sharma, and M. Pavone, “Meta-Learning Priors for Efficient Online Bayesian Regression,” in Workshop on Algorithmic Foundations of Robotics, Merida, Mexico, 2018. (In Press)

    Abstract: Gaussian Process (GP) regression has seen widespread use in robotics due to its generality, simplicity of use, and the utility of Bayesian predictions. In particular, the predominant implementation of GP regression is kernel-based, as it enables fitting of arbitrary nonlinear functions by leveraging kernel functions as infinite-dimensional features. While incorporating prior information has the potential to drastically improve data efficiency of kernel-based GP regression, expressing complex priors through the choice of kernel function and associated hyperparameters is often challenging and unintuitive. Furthermore, the computational complexity of kernel-based GP regression scales poorly with the number of samples, limiting its application in regimes where a large amount of data is available. In this work, we propose ALPaCA, an algorithm for efficient Bayesian regression which addresses these issues. ALPaCA uses a dataset of sample functions to learn a domain-specific, finite-dimensional feature encoding, as well as a prior over the associated weights, such that Bayesian linear regression in this feature space yields accurate online predictions of the posterior density. These features are neural networks, which are trained via a meta-learning approach. ALPaCA extracts all prior information from the dataset, rather than relying on the choice of arbitrary, restrictive kernel hyperparameters. Furthermore, it substantially reduces sample complexity, and allows scaling to large systems. We investigate the performance of ALPaCA on two simple regression problems, two simulated robotic systems, and on a lane-change driving task performed by humans. We find our approach outperforms kernel-based GP regression, as well as state of the art meta-learning approaches, thereby providing a promising plug-in tool for many regression tasks in robotics where scalability and data-efficiency are important.

    @inproceedings{HarrisonSharmaEtAl2018,
      author = {Harrison, J. and Sharma, A. and Pavone, M.},
      title = {Meta-Learning Priors for Efficient Online Bayesian Regression},
      booktitle = {{Workshop on Algorithmic Foundations of Robotics}},
      year = {2018},
      note = {In press},
      address = {Merida, Mexico},
      month = oct,
      url = {https://arxiv.org/pdf/1807.08912.pdf},
      keywords = {press},
      owner = {apoorva},
      timestamp = {2018-10-07}
    }
    

Spacecraft Motion Planning
The objective of this research is to devise real-time, efficient and dependable algorithms for spacecraft autonomous maneuvering, with a focus on dynamic and cluttered environments. Specifically, we aim to devise technologies for the online planning of trajectories in proximity operations, which together with reliable environmental sensing and autonomous high-level decision-making is a key enabler for autonomous spacecraft navigation. To this end, we develop robot motion planning and trajectory optimization techniques, tailor them to aerospace mission constraints (e.g., unique dynamics and environments, often limited computation, etc.), and apply them to aerospace hardware platforms, both on ground test beds and in space.

Keywords: Space Systems, Motion Planning, Control Systems

Related Work

Conference Articles

  1. 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. (Submitted)

    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},
      note = {Submitted},
      address = {Montreal, Canada},
      month = may,
      url = {../wp-content/papercite-data/pdf/Bonalli.Cauligi.Bylard.Pavone.ICRA19.pdf},
      keywords = {sub},
      owner = {bylard},
      timestamp = {2018-10-04}
    }
    
  2. 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}
    }
    

Trustworthy Interaction-Aware Decision Making and Planning
For autonomous systems, having the ability to predict the evolution of their surroundings is essential for safe, reliable, and efficient operation. Prediction is especially important when the autonomous system must interact and “negotiate” with humans, whether it be in settings that are cooperative, adversarial, or anywhere in between. This line of research involves quantifying the relative likelihoods of multiple, possibly highly distinct futures for interactive scenarios, planning strategies such that the autonomous agent is cognizant of how the human may respond, developing models that are offer transparency into the autonomous agent’s decision making process, and designing safe human-in-the-loop testing methodologies to validate our models and planning algorithms.

Keywords: Human-Robot Interaction, Deep Learning, Motion Planning

Related Work

Conference Articles

  1. K. Leung, E. Schmerling, M. Chen, J. Talbot, J. C. Gerdes, and M. Pavone, “On Infusing Reachability-Based Safety Assurance within Probabilistic Planning Frameworks for Human-Robot Vehicle Interactions,” in Int. Symp. on Experimental Robotics, 2018.

    Abstract:

    @inproceedings{LeungSchmerlingEtAl2018,
      author = {Leung, K. and Schmerling, E. and Chen, M. and Talbot, J. and Gerdes, J. C. and Pavone, M.},
      title = {On Infusing Reachability-Based Safety Assurance within Probabilistic Planning Frameworks for Human-Robot Vehicle Interactions},
      booktitle = {{Int. Symp. on Experimental Robotics}},
      year = {2018},
      owner = {mochen72},
      timestamp = {2018-10-13}
    }
    
  2. B. Ivanovic, E. Schmerling, K. Leung, and M. Pavone, “Generative Modeling of Multimodal Multi-Human Behavior,” in IEEE/RSJ Int. Conf. on Intelligent Robots & Systems, Madrid, Spain, 2018.

    Abstract: This work presents a methodology for modeling and predicting human behavior in settings with N humans interacting in highly multimodal scenarios (i.e. where there are many possible highly-distinct futures). A motivating example includes robots interacting with humans in crowded environments, such as self-driving cars operating alongside human-driven vehicles or human-robot collaborative bin packing in a warehouse. Our approach to model human behavior in such uncertain environments is to model humans in the scene as nodes in a graphical model, with edges encoding relationships between them. For each human, we learn a multimodal probability distribution over future actions from a dataset of multi-human interactions. Learning such distributions is made possible by recent advances in the theory of conditional variational autoencoders and deep learning approximations of probabilistic graphical models. Specifically, we learn action distributions conditioned on interaction history, neighboring human behavior, and candidate future agent behavior in order to take into account response dynamics. We demonstrate the performance of such a modeling approach in modeling basketball player trajectories, a highly multimodal, multi-human scenario which serves as a proxy for many robotic applications.

    @inproceedings{IvanovicSchmerlingEtAl2018,
      author = {Ivanovic, B. and Schmerling, E. and Leung, K. and Pavone, M.},
      title = {Generative Modeling of Multimodal Multi-Human Behavior},
      booktitle = {{IEEE/RSJ Int. Conf. on Intelligent Robots \& Systems}},
      year = {2018},
      address = {Madrid, Spain},
      month = oct,
      url = {https://arxiv.org/pdf/1803.02015.pdf},
      owner = {borisi},
      timestamp = {2018-10-14}
    }
    

Unconventional Space Robotics
Space as a frontier is bursting with unique challenges and opportunities, for mankind and especially for roboticists. Novel technologies are required to engage the harsh realities of space and further space science, exploration, and development. Among other projects, the lab focuses on: (1) small assistive free-flying robots, such as the Astrobee robots soon to be operational on the International Space Station, (2) space robot manipulator systems, for on-orbit tasks such as satellite servicing and debris removal, (3) hopping rovers (e.g. Hedgehog) for efficient mobility on small Solar System bodies with extremely low gravity, such as asteroids and comets, (4) gecko-inspired adhesive grippers, a novel space-qualified technology for grasping surfaces, enabling robust capture and manipulation even of large, tumbling objects, (5) multi-agent, modular robots for collaborative, multi-modal mobility (e.g., flying, swimming, rolling) on bodies such as Titan having an atmosphere.

Keywords: Space Systems, Motion Planning, Control Systems

Related Work

Journal Articles

  1. B. Hockman, A. Frick, I. A. D. Nesnas, and M. Pavone, “Design, Control, and Experimentation of Internally-Actuated Rovers for the Exploration of Low-Gravity Planetary Bodies,” Journal of Field Robotics, vol. 34, no. 1, pp. 5–24, 2016.

    Abstract: In this paper we discuss the design, control, and experimentation of internally-actuated rovers for the exploration of low-gravity (micro-g to milli-g) planetary bodies, such as asteroids, comets, or small moons. The actuation of the rover relies on spinning three internal flywheels, which allows all subsystems to be packaged in one sealed enclosure and enables the platform to be minimalistic, thereby reducing its cost. By controlling flywheels’ spin rate, the rover is capable of achieving large surface coverage by attitude-controlled hops, fine mobility by tumbling, and coarse instrument pointing by changing orientation relative to the ground. We discuss the dynamics of such rovers, their control, and key design features (e.g., flywheel design and orientation, geometry of external spikes, and system engineering aspects). We then discuss the design and control of a first-of-a-kind test bed, which allows the accurate emulation of a microgravity environment for mobility experiments and consists of a 3 DoF gimbal attached to an actively controlled gantry crane. Finally, we present experimental results on the test bed that provide key insights for control and validate the theoretical analysis.

    @article{HockmanFrickEtAl2016,
      author = {Hockman, B. and Frick, A. and Nesnas, I. A. D. and Pavone, M.},
      title = {Design, Control, and Experimentation of Internally-Actuated Rovers for the Exploration of Low-Gravity Planetary Bodies},
      journal = {{Journal of Field Robotics}},
      volume = {34},
      number = {1},
      pages = {5--24},
      year = {2016},
      doi = {10.1002/rob.21656},
      url = {../wp-content/papercite-data/pdf/Hockman.Pavone.ea.JFR15.pdf},
      owner = {bylard},
      timestamp = {2017-08-11}
    }
    

Conference Articles

  1. 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}
    }
    

The lab currently comprises about 15 researchers, plus a number of graduate and undergraduate students with temporary appointments. The work of the lab has been recognized with several awards, including a Presidential Early Career Award for Scientists and Engineers (the highest honor bestowed by the United States Government on science and engineering professionals in the early stages of their independent research careers).