Browsing by Author "Ames, Aaron D."

Now showing 1 - 5 of 5
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    Distributed Planning of Collaborative Locomotion: A Physics-Based and Data-Driven Approach
    Fawcett, Randall T.; Ames, Aaron D.; Hamed, Kaveh Akbari (IEEE, 2023-11-14)
    This work aims to provide a computationally effective and distributed trajectory planner at the intersection of physics-based and data-driven techniques for the collaborative locomotion of holonomically constrained quadrupedal robots that can account for and attenuate interaction forces between subsystems. More specifically, this work lays the foundation for using an interconnected single rigid body model in a predictive control framework such that interaction forces can be utilized at the planning layer, wherein these forces are parameterized via a behavioral systems approach. Furthermore, the proposed trajectory planner is distributed such that each agent can locally plan for its own trajectory subject to coupling dynamics, resulting in a much more computationally efficient method for real-time planning. The optimal trajectory obtained by the planner is then provided to a full-order nonlinear whole-body controller for tracking at the low level. The efficacy and robustness of the proposed approach are verified both in simulation and on hardware subject to various disturbances, payloads, and uneven terrains.
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    H2- and H∞-Optimal Model Predictive Controllers for Robust Legged Locomotion
    Pandala, Abhishek; Ames, Aaron D.; Hamed, Kaveh Akbari (IEEE, 2024-05-31)
    This paper formally develops robust optimal predictive control solutions that can accommodate disturbances and stabilize periodic legged locomotion. To this end, we build upon existing optimization-based control paradigms, particularly quadratic programming (QP)-based model predictive controllers (MPCs). We present conditions under which the closed-loop reduced-order systems (i.e., template models) with MPC have the continuous differentiability property on an open neighborhood of gaits. We then linearize the resulting discrete-time, closed-loop nonlinear template system around the gait to obtain a linear time-varying (LTV) system. This periodic LTV system is further transformed into a linear system with a constant state-transition matrix using discrete-time Floquet transform. The system is then analyzed to accommodate parametric uncertainties and to synthesize robust optimal H2 and H∞ feedback controllers via linear matrix inequalities (LMIs). The paper then extends the theoretical results to the single rigid body (SRB) template dynamics and numerically verifies them. The proposed robust optimal predictive controllers are used in a layered control structure, where the optimal reduced-order trajectories are provided to a full-order nonlinear whole-body controller (WBC) for tracking at the low level. The developed layered controllers are numerically and experimentally validated for the robust locomotion of the A1 quadrupedal robot subject to various disturbances and uneven terrains. Our numerical results suggest that the H2-and H∞-optimal MPC controllers significantly improve the robust stability of the gaits compared to the normal MPC.
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    Layered control for cooperative locomotion of two quadrupedal robots: Centralized and distributed approaches
    Kim, Jeeseop; Fawcett, Randall T.; Kamidi, Vinay R.; Ames, Aaron D.; Akbari Hamed, Kaveh (IEEE, 2023)
    This paper presents a layered control approach for real-time trajectory planning and control of robust cooperative locomotion by two holonomically constrained quadrupedal robots. A novel interconnected network of reduced-order models, based on the single rigid body (SRB) dynamics, is developed for trajectory planning purposes. At the higher level of the control architecture, two different model predictive control (MPC) algorithms are proposed to address the optimal control problem of the interconnected SRB dynamics: centralized and distributed MPCs. The distributed MPC assumes two local quadratic programs that share their optimal solutions according to a one-step communication delay and an agreement protocol. At the lower level of the control scheme, distributed nonlinear controllers are developed to impose the full-order dynamics to track the prescribed reduced-order trajectories generated by MPCs. The effectiveness of the control approach is verified with extensive numerical simulations and experiments for the robust and cooperative locomotion of two holonomically constrained A1 robots with different payloads on variable terrains and in the presence of disturbances. It is shown that the distributed MPC has a performance similar to that of the centralized MPC, while the computation time is reduced significantly.
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    Robust Predictive Control for Quadrupedal Locomotion: Learning to Close the Gap Between Reduced- and Full-Order Models
    Pandala, Abhishek; Fawcett, Randall T.; Rosolia, Ugo; Ames, Aaron D.; Hamed, Kaveh Akbari (IEEE, 2022-07-01)
    Template-based reduced-order models have provided a popular methodology for real-time trajectory planning of dynamic quadrupedal locomotion. However, the abstraction and unmodeled dynamics in template models significantly increase the gap between reduced- and full-order models. This letter presents a computationally tractable robust model predictive control (RMPC) formulation, based on convex quadratic programs (QP), to bridge this gap. The RMPC framework considers the single rigid body model subject to a set of unmodeled dynamics and plans for the optimal reduced-order trajectory and ground reaction forces (GRFs). The generated optimal GRFs of the high-level RMPC are then mapped to the full-order model using a low-level nonlinear controller based on virtual constraints and QP. The proposed hierarchical control framework is employed for locomotion over rough terrains. We leverage deep reinforcement learning to train a neural network to compute the set of unmodeled dynamics for the RMPC framework. The proposed controller is finally validated via extensive numerical simulations and experiments for robust and blind locomotion of the A1 quadrupedal robot on different terrains.
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    Toward a Data-Driven Template Model for Quadrupedal Locomotion
    Fawcett, Randall T.; Afsari, Kereshmeh; Ames, Aaron D.; Hamed, Kaveh Akbari (IEEE, 2022-07-01)
    This work investigates a data-driven template model for trajectory planning of dynamic quadrupedal robots. Many state-of-the-art approaches involve using a reduced-order model, primarily due to computational tractability. The spirit of the trajectory planning approach in this work draws on recent advancements in the area of behavioral systems theory. Here, we aim to capitalize on the knowledge of well-known template models to construct a data-driven model, enabling us to obtain an information rich reduced-order model. In particular, this work considers input-output states similar to that of the single rigid body model and proceeds to develop a data-driven representation of the system, which is then used in a predictive control framework to plan a trajectory for quadrupeds. The optimal trajectory is passed to a low-level and nonlinear model-based controller to be tracked. Preliminary experimental results are provided to establish the efficacy of this hierarchical control approach for trotting and walking gaits of a high-dimensional quadrupedal robot on unknown terrains and in the presence of disturbances.