24 research outputs found
Terrestrial Locomotion of PogoX: From Hardware Design to Energy Shaping and Step-to-step Dynamics Based Control
We present a novel controller design on a robotic locomotor that combines an
aerial vehicle with a spring-loaded leg. The main motivation is to enable the
terrestrial locomotion capability on aerial vehicles so that they can carry
heavy loads: heavy enough that flying is no longer possible, e.g., when the
thrust-to-weight ratio (TWR) is small. The robot is designed with a pogo-stick
leg and a quadrotor, and thus it is named as PogoX. We show that with a simple
and lightweight spring-loaded leg, the robot is capable of hopping with TWR
. The control of hopping is realized via two components: a vertical height
control via control Lyapunov function-based energy shaping, and a step-to-step
(S2S) dynamics based horizontal velocity control that is inspired by the
hopping of the Spring-Loaded Inverted Pendulum (SLIP). The controller is
successfully realized on the physical robot, showing dynamic terrestrial
locomotion of PogoX which can hop at variable heights and different horizontal
velocities with robustness to ground height variations and external pushes.Comment: 7 pages, 7 figure
Reduced Order Model Inspired Robotic Bipedal Walking: A Step-to-step Dynamics Approximation based Approach
Controlling bipedal robotic walking is a challenging task. The dynamics is hybrid, nonlinear, high-dimensional, and typically underactuated. Complex physical constraints have to be satisfied in the walking generation. The stability in terms of not-falling is also hard to be encoded in the walking synthesis. Canonical approaches for enabling robotic walking typically rely on large-scale trajectory optimizations for generating optimal periodic behaviors on the full-dimensional model of the system; then the stabilities of the controlled behaviors are analyzed through the numerically derived Poincaré maps. This full-dimensional periodic behavior based synthesis, despite being theoretically rigorous, suffers from several disadvantages. The trajectory optimization problem is computationally challenging to solve. Non-trivial expert-tuning is required on the cost, constraints, and initial conditions to avoid infeasibilities and local optimality. It is cumbersome for realizing non-periodical behaviors, and the synthesized walking can be sensitive to model uncertainties.
In this thesis, we propose an alternative approach of walking synthesis that is based on reduced order modeling and dynamics approximation. We formulate a discrete step-to-step (S2S) dynamics of walking, where the step size is treated as the control input to stabilize the pre-impact horizontal center of mass (COM) state of the robot. Stepping planning thus is converted into a feedback control problem. To effectively and efficiently solve this feedback stepping planning problem, an underactuated Hybrid Linear Inverted Pendulum (H-LIP) model is proposed to approximate the dynamics of underactuated bipedal walking; the linear S2S dynamics of the H-LIP then approximates the robot S2S dynamics. The H-LIP based stepping controller is hence utilized to plan the desired step sizes on the robot to control its pre-impact horizontal COM state. Stable walking behaviors are consequently generating by realizing the desired step size in the output construction and stabilizing the output via optimization-based controllers. We evaluate this approach successfully on several bipedal walking systems with an increase in the system complexity: a planar five-linkage walker AMBER, an actuated version of the Spring Loaded Inverted Pendulum (aSLIP) in both 2D and 3D, and finally the 3D underactuated robot Cassie. The generated dynamic walking behaviors on these systems are shown to be highly versatile and robust. Furthermore, we show that this approach can be effectively extended to realizing more complex walking tasks such as global trajectory tracking and walking on rough terrain.</p
Modeling, analysis and control of robot-object nonsmooth underactuated Lagrangian systems: A tutorial overview and perspectives
International audienceSo-called robot-object Lagrangian systems consist of a class of nonsmooth underactuated complementarity Lagrangian systems, with a specific structure: an "object" and a "robot". Only the robot is actuated. The object dynamics can thus be controlled only through the action of the contact Lagrange multipliers, which represent the interaction forces between the robot and the object. Juggling, walking, running, hopping machines, robotic systems that manipulate objects, tapping, pushing systems, kinematic chains with joint clearance, crawling, climbing robots, some cable-driven manipulators, and some circuits with set-valued nonsmooth components, belong this class. This article aims at presenting their main features, then many application examples which belong to the robot-object class, then reviewing the main tools and control strategies which have been proposed in the Automatic Control and in the Robotics literature. Some comments and open issues conclude the article
Robust Control Synthesis and Verification for Wire-Borne Underactuated Brachiating Robots Using Sum-of-Squares Optimization
Control of wire-borne underactuated brachiating robots requires a robust
feedback control design that can deal with dynamic uncertainties, actuator
constraints and unmeasurable states. In this paper, we develop a robust
feedback control for brachiating on flexible cables, building on previous work
on optimal trajectory generation and time-varying LQR controller design. We
propose a novel simplified model for approximation of the flexible cable
dynamics, which enables inclusion of parametric model uncertainties in the
system. We then use semidefinite programming (SDP) and sum-of-squares (SOS)
optimization to synthesize a time-varying feedback control with formal
robustness guarantees to account for model uncertainties and unmeasurable
states in the system. Through simulation, hardware experiments and comparison
with a time-varying LQR controller, it is shown that the proposed robust
controller results in relatively large robust backward reachable sets and is
able to reliably track a pre-generated optimal trajectory and achieve the
desired brachiating motion in the presence of parametric model uncertainties,
actuator limits, and unobservable states.Comment: 8 pages, 12 figures, 2020 IEEE/RSJ International Conference on
Intelligent Robots and Systems (IROS