Ross Allen

Ross Allen


Ross Allen earned his Ph.D. in Aeronautics and Astronautics from Stanford University in June 2016. At Stanford, he was a UTRC Fellow in Aerospace Systems. He has also worked as a roboticist at the NASA Jet Propulsion Laboratory and one of the first employees at Lily Robotics developing an autonomous aerial camera. Ross received his M.Sc. in Aeronautics and Astronautics at Stanford University and his B.S. in Aerospace Engineering at Illinois Institute of Technology.

Ross’s work focused on training robots on how to avoid obstacles, even at high speeds, while achieving their objectives. His experiments with quadrotors evading a fencing blade while navigating an indoor environment stand as, arguably, one of the first demonstrations of real-time kinodynamic motion planning on a quadrotor systems.


Currently at MIT Lincoln Labs (via SpaceX)

ASL Publications

  1. R. Allen and M. Pavone, “A Real-Time Framework for Kinodynamic Planning in Dynamic Environments with Application to Quadrotor Obstacle Avoidance,” Robotics and Autonomous Systems, vol. 115, pp. 174–193, 2019.

    Abstract: The objective of this paper is to present a full-stack, real-time motion planning framework for kinodynamic robots and then show how it is applied and demonstrated on a physical quadrotor system operating in a laboratory environment. The proposed framework utilizes an offline-online computation paradigm, neighborhood classification through machine learning, sampling-based motion planning with an optimal cost distance metric, and trajectory smoothing to achieve real-time planning for aerial vehicles. This framework accounts for dynamic obstacles with an event-based replanning structure and a locally reactive control layer that minimizes replanning events. The approach is demonstrated on a quadrotor navigating moving obstacles in an indoor space and stands as, arguably, one of the first demonstrations of full-online kinodynamic motion planning, with execution cycles of 3 Hz to 5 Hz. For the quadrotor, a simplified dynamics model is used during the planning phase to accelerate online computation. A trajectory smoothing phase, which leverages the differentially flat nature of quadrotor dynamics, is then implemented to guarantee a dynamically feasible trajectory.

    @article{AllenPavone2018,
      author = {Allen, R. and Pavone, M.},
      title = {A Real-Time Framework for Kinodynamic Planning in Dynamic Environments with Application to Quadrotor Obstacle Avoidance},
      journal = {{Robotics and Autonomous Systems}},
      volume = {115},
      pages = {174--193},
      year = {2019},
      doi = {10.1016/j.robot.2018.11.017},
      url = {../wp-content/papercite-data/pdf/Allen.Pavone.RAS18.pdf},
      owner = {bylard},
      timestamp = {2019-01-07}
    }
    
  2. R. Allen, “A Real-Time Framework for Kinodynamic Planning with Application to Quadrotor Obstacle Avoidance,” PhD thesis, Stanford University, Dept. of Aeronautics and Astronautics, Stanford, California, 2016.

    Abstract: This thesis presents a full-stack, real-time planning framework for kinodynamic robots that is enabled by a novel application of machine learning for reachability analysis. As products of this work, three contributions are discussed in detail in this thesis. The first contribution is the novel application of machine learning for rapid approximation of reachable sets for dynamical systems. The second contribution is the synthesis of machine learning, sampling-based motion planning, and optimal control into a cohesive planning framework that is built on an offline-online computation paradigm. The final contribution is the application of this planning framework on a quadrotor system to produce, arguably, one of the first demonstrations of fully-online kinodynamic motion planning. During physical experiments, the framework is shown to execute planning cycles at a rate 3 Hz to 5 Hz, a significant improvement over existing techniques. For the quadrotor, a simplified dynamics model is used during the planning phase to accelerate online computation. A trajectory smoothing phase, which leverages the differentially flat nature of quadrotor dynamics, is then implemented to guarantee a dynamically feasible trajectory. An event-based replanning structure is implemented to handle the case of dynamic, even adversarial, obstacles. A locally reactive control layer, inspired by potential fields methods, is added to the framework to help minimizes replanning events and produce graceful avoidance maneuvers in the presence of high speed obstacles.

    @phdthesis{Allen2016,
      author = {Allen, R.},
      title = {A Real-Time Framework for Kinodynamic Planning with Application to Quadrotor Obstacle Avoidance},
      school = {{Stanford University, Dept. of Aeronautics and Astronautics}},
      year = {2016},
      address = {Stanford, California},
      month = jun,
      owner = {bylard},
      timestamp = {2017-01-28},
      url = {../wp-content/papercite-data/pdf/Allen.PhD16.pdf}
    }
    
  3. R. Allen and M. Pavone, “A Real-Time Framework for Kinodynamic Planning with Application to Quadrotor Obstacle Avoidance,” in AIAA Conf. on Guidance, Navigation and Control, San Diego, CA, 2016.

    Abstract: The objective of this paper is to present a full-stack, real-time kinodynamic planning framework and demonstrate it on a quadrotor for collision avoidance. Specifically, the proposed framework utilizes an offline-online computation paradigm, neighborhood classification through machine learning, sampling-based motion planning with an optimal control distance metric, and trajectory smoothing to achieve real-time planning for aerial vehicles. The approach is demonstrated on a quadrotor navigating obstacles in an indoor space and stands as, arguably, one of the first demonstrations of full-online kinodynamic motion planning; exhibiting execution times under 1/3 of a second. For the quadrotor, a simplified dynamics model is used during the planning phase to accelerate online computation. A trajectory smoothing phase, which leverages the differentially flat nature of quadrotor dynamics, is then implemented to guarantee a dynamically feasible trajectory.

    @inproceedings{AllenPavone2016b,
      author = {Allen, R. and Pavone, M.},
      title = {A Real-Time Framework for Kinodynamic Planning with Application to Quadrotor Obstacle Avoidance},
      booktitle = {{AIAA Conf. on Guidance, Navigation and Control}},
      year = {2016},
      address = {San Diego, CA},
      doi = {10.2514/6.2016-1374},
      month = jan,
      owner = {bylard},
      timestamp = {2017-01-28},
      url = {../wp-content/papercite-data/pdf/Allen.Pavone.AIAAGNC16.pdf}
    }
    
  4. R. Allen, M. Pavone, and M. Schwager, “Flying Smartphones: When Portable Computing Sprouts Wings,” IEEE Pervasive Computing, vol. 15, no. 3, pp. 83–88, 2016.

    Abstract: Lightweight, highly autonomous drones that can actively interact with the world are emerging as the next step-change in consumer electronic technology, much in the same way that smart phones revolutionized personal computing..

    @article{AllenPavoneEtAl2016,
      author = {Allen, R. and Pavone, M. and Schwager, M.},
      title = {Flying Smartphones: When Portable Computing Sprouts Wings},
      journal = {{IEEE Pervasive Computing}},
      volume = {15},
      number = {3},
      pages = {83--88},
      year = {2016},
      doi = {10.1109/MPRV.2016.43},
      url = {../wp-content/papercite-data/pdf/Allen.Pavone.Schwager.PC16.pdf},
      owner = {bylard},
      timestamp = {2017-01-28}
    }
    
  5. R. Allen and M. Pavone, “Toward A Real-Time Framework for Solving the Kinodynamic Motion Planning Problem,” in Proc. IEEE Conf. on Robotics and Automation, Seattle, Washington, 2015.

    Abstract: n this paper we propose a framework combining techniques from sampling-based motion planning, machine learning, and trajectory optimization to address the kinody- namic motion planning problem in real-time environments. This framework relies on a look-up table that stores precomputed optimal solutions to boundary value problems (assuming no obstacles), which form the directed edges of a precomputed motion planning roadmap. A sampling-based motion planning algorithm then leverages such a precomputed roadmap to compute online an obstacle-free trajectory. Machine learning techniques are employed to minimize the number of online solutions to boundary value problems required to compute the neighborhoods of the start state and goal regions. This approach is demonstrated to reduce online planning times up to six orders of magnitude. Simulation results are presented and discussed. Problem-specific framework modifications are then discussed that would allow further computation time reductions.

    @inproceedings{AllenPavone2015,
      author = {Allen, R. and Pavone, M.},
      title = {Toward A Real-Time Framework for Solving the Kinodynamic Motion Planning Problem},
      booktitle = {{Proc. IEEE Conf. on Robotics and Automation}},
      year = {2015},
      address = {Seattle, Washington},
      doi = {10.1109/ICRA.2015.7139288},
      month = may,
      owner = {bylard},
      timestamp = {2017-01-28},
      url = {../wp-content/papercite-data/pdf/Allen.Pavone.ICRA15.pdf}
    }
    
  6. 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}
    }
    
  7. R. Allen, M. Pavone, C. McQuin, I. Nesnas, J. C. Castillo-Rogez, T.-N. Nguyen, and J. A. Hoffman, “Internally-Actuated Rovers for All-Access Surface Mobility: Theory and Experimentation,” in Proc. IEEE Conf. on Robotics and Automation, Karlsruhe, Germany, 2013.

    Abstract: The future exploration of small Solar System bodies will, in part, depend on the availability of mobility platforms capable of performing both large surface coverage and short traverses to specific locations. Weak gravitational fields, however, make the adoption of traditional mobility systems difficult. In this paper we present a planetary mobility platform (called "spacecraft/rover hybrid") that relies on internal actuation. A hybrid is a small ( 5 kg), multifaceted robot enclosing three mutually orthogonal flywheels and surrounded by external spikes or contact surfaces. By accelerating/decelerating the flywheels and by exploiting the low-gravity environment, such a platform can perform both long excursions (by hopping) and short, precise traverses (through controlled "tumbles"). This concept has the potential to lead to small, quasi-expendable, yet maneuverable rovers that are robust as they have no external moving parts. In the first part of the paper we characterize the dynamics of such platforms (including fundamental limitations of performance) and we discuss control and planning algorithms. In the second part, we discuss the development of a prototype and present experimental results both in simulations and on physical test stands emulating low-gravity environments. Collectively, our results lay the foundations for the design of internally-actuated rovers with controlled mobility (as opposed to random hopping motion).

    @inproceedings{AllenPavoneEtAl2013,
      author = {Allen, R. and Pavone, M. and McQuin, C. and Nesnas, Issa and {Castillo-Rogez}, Julie C. and Nguyen, {Tam-Nguyen} and Hoffman, Jeffrey A.},
      title = {Internally-Actuated Rovers for All-Access Surface Mobility: Theory and Experimentation},
      booktitle = {{Proc. IEEE Conf. on Robotics and Automation}},
      year = {2013},
      address = {Karlsruhe, Germany},
      doi = {10.1109/ICRA.2013.6631363},
      month = may,
      owner = {bylard},
      timestamp = {2017-01-28},
      url = {../wp-content/papercite-data/pdf/Allen.Pavone.ea.ICRA13.pdf}
    }