249 research outputs found
Keep Rollin' - Whole-Body Motion Control and Planning for Wheeled Quadrupedal Robots
We show dynamic locomotion strategies for wheeled quadrupedal robots, which
combine the advantages of both walking and driving. The developed optimization
framework tightly integrates the additional degrees of freedom introduced by
the wheels. Our approach relies on a zero-moment point based motion
optimization which continuously updates reference trajectories. The reference
motions are tracked by a hierarchical whole-body controller which computes
optimal generalized accelerations and contact forces by solving a sequence of
prioritized tasks including the nonholonomic rolling constraints. Our approach
has been tested on ANYmal, a quadrupedal robot that is fully torque-controlled
including the non-steerable wheels attached to its legs. We conducted
experiments on flat and inclined terrains as well as over steps, whereby we
show that integrating the wheels into the motion control and planning framework
results in intuitive motion trajectories, which enable more robust and dynamic
locomotion compared to other wheeled-legged robots. Moreover, with a speed of 4
m/s and a reduction of the cost of transport by 83 % we prove the superiority
of wheeled-legged robots compared to their legged counterparts.Comment: IEEE Robotics and Automation Letter
PM-FSM: Policies Modulating Finite State Machine for Robust Quadrupedal Locomotion
Deep reinforcement learning (deep RL) has emerged as an effective tool for
developing controllers for legged robots. However, vanilla deep RL often
requires a tremendous amount of training samples and is not feasible for
achieving robust behaviors. Instead, researchers have investigated a novel
policy architecture by incorporating human experts' knowledge, such as Policies
Modulating Trajectory Generators (PMTG). This architecture builds a recurrent
control loop by combining a parametric trajectory generator (TG) and a feedback
policy network to achieve more robust behaviors. To take advantage of human
experts' knowledge but eliminate time-consuming interactive teaching,
researchers have investigated a novel architecture, Policies Modulating
Trajectory Generators (PMTG), which builds a recurrent control loop by
combining a parametric trajectory generator (TG) and a feedback policy network
to achieve more robust behaviors using intuitive prior knowledge. In this work,
we propose Policies Modulating Finite State Machine (PM-FSM) by replacing TGs
with contact-aware finite state machines (FSM), which offer more flexible
control of each leg. Compared with the TGs, FSMs offer high-level management on
each leg motion generator and enable a flexible state arrangement, which makes
the learned behavior less vulnerable to unseen perturbations or challenging
terrains. This invention offers an explicit notion of contact events to the
policy to negotiate unexpected perturbations. We demonstrated that the proposed
architecture could achieve more robust behaviors in various scenarios, such as
challenging terrains or external perturbations, on both simulated and real
robots. The supplemental video can be found at: https://youtu.be/78cboMqTkJQ
LeggedWalking on Inclined Surfaces
The main contribution of this MS Thesis is centered around taking steps
towards successful multi-modal demonstrations using Northeastern's
legged-aerial robot, Husky Carbon. This work discusses the challenges involved
in achieving multi-modal locomotion such as trotting-hovering and
thruster-assisted incline walking and reports progress made towards overcoming
these challenges. Animals like birds use a combination of legged and aerial
mobility, as seen in Chukars' wing-assisted incline running (WAIR), to achieve
multi-modal locomotion. Chukars use forces generated by their flapping wings to
manipulate ground contact forces and traverse steep slopes and overhangs.
Husky's design takes inspiration from birds such as Chukars. This MS thesis
presentation outlines the mechanical and electrical details of Husky's legged
and aerial units. The thesis presents simulated incline walking using a
high-fidelity model of the Husky Carbon over steep slopes of up to 45 degrees.Comment: Masters thesi
Excitation and Stabilization of Passive Dynamics in Locomotion using Hierarchical Operational Space Control
This paper describes a hierarchical operational space control (OSC) method based on least square optimization and outlines different ways to reduce the dimensionality of the optimization vector. The framework allows to emulate various behaviors by prioritized task-space motion, joint torque, and contact force optimization. Moreover, a methodology is introduced to partially excite the natural dynamics of the robot by open-loop motor regulation while the entire behavior is stabilized by hierarchical OSC. As a major contribution, the presented control strategies are tested and validated in real hardware walking, trotting, and pronking experiments using a fully torque controllable quadrupedal robot
Robust Whole-Body Motion Control of Legged Robots
We introduce a robust control architecture for the whole-body motion control
of torque controlled robots with arms and legs. The method is based on the
robust control of contact forces in order to track a planned Center of Mass
trajectory. Its appeal lies in the ability to guarantee robust stability and
performance despite rigid body model mismatch, actuator dynamics, delays,
contact surface stiffness, and unobserved ground profiles. Furthermore, we
introduce a task space decomposition approach which removes the coupling
effects between contact force controller and the other non-contact controllers.
Finally, we verify our control performance on a quadruped robot and compare its
performance to a standard inverse dynamics approach on hardware.Comment: 8 Page
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