34 research outputs found
Nonlinear Stochastic Trajectory Optimization for Centroidal Momentum Motion Generation of Legged Robots
Generation of robust trajectories for legged robots remains a challenging
task due to the underlying nonlinear, hybrid and intrinsically unstable
dynamics which needs to be stabilized through limited contact forces.
Furthermore, disturbances arising from unmodelled contact interactions with the
environment and model mismatches can hinder the quality of the planned
trajectories leading to unsafe motions. In this work, we propose to use
stochastic trajectory optimization for generating robust centroidal momentum
trajectories to account for additive uncertainties on the model dynamics and
parametric uncertainties on contact locations. Through an alternation between
the robust centroidal and whole-body trajectory optimizations, we generate
robust momentum trajectories while being consistent with the whole-body
dynamics. We perform an extensive set of simulations subject to different
uncertainties on a quadruped robot showing that our stochastic trajectory
optimization problem reduces the amount of foot slippage for different gaits
while achieving better performance over deterministic planning
Towards Agility: A Momentum Aware Trajectory Optimisation Framework using Full-Centroidal Dynamics & Implicit Inverse Kinematics
Online planning and execution of acrobatic maneuvers pose significant
challenges in legged locomotion. Their underlying combinatorial nature, along
with the current hardware's limitations constitute the main obstacles in
unlocking the true potential of legged-robots. This letter tries to expose the
intricacies of these optimal control problems in a tangible way, directly
applicable to the creation of more efficient online trajectory optimisation
frameworks. By analysing the fundamental principles that shape the behaviour of
the system, the dynamics themselves can be exploited to surpass its hardware
limitations. More specifically, a trajectory optimisation formulation is
proposed that exploits the system's high-order nonlinearities, such as the
nonholonomy of the angular momentum, and phase-space symmetries in order to
produce feasible high-acceleration maneuvers. By leveraging the full-centroidal
dynamics of the quadruped ANYmal C and directly optimising its footholds and
contact forces, the framework is capable of producing efficient motion plans
with low computational overhead. The feasibility of the produced trajectories
is ensured by taking into account the configuration-dependent inertial
properties of the robot during the planning process, while its robustness is
increased by supplying the full analytic derivatives & hessians to the solver.
Finally, a significant portion of the discussion is centred around the
deployment of the proposed framework on the ANYmal C platform, while its true
capabilities are demonstrated through real-world experiments, with the
successful execution of high-acceleration motion scenarios like the squat-jump
Whole-Body MPC and Online Gait Sequence Generation for Wheeled-Legged Robots
Our paper proposes a model predictive controller as a single-task formulation
that simultaneously optimizes wheel and torso motions. This online joint
velocity and ground reaction force optimization integrates a kinodynamic model
of a wheeled quadrupedal robot. It defines the single rigid body dynamics along
with the robot's kinematics while treating the wheels as moving ground
contacts. With this approach, we can accurately capture the robot's rolling
constraint and dynamics, enabling automatic discovery of hybrid maneuvers
without needless motion heuristics. The formulation's generality through the
simultaneous optimization over the robot's whole-body variables allows for a
single set of parameters and makes online gait sequence adaptation possible.
Aperiodic gait sequences are automatically found through kinematic leg
utilities without the need for predefined contact and lift-off timings,
reducing the cost of transport by up to 85%. Our experiments demonstrate
dynamic motions on a quadrupedal robot with non-steerable wheels in challenging
indoor and outdoor environments. The paper's findings contribute to evaluating
a decomposed, i.e., sequential optimization of wheel and torso motion, and
single-task motion planner with a novel quantity, the prediction error, which
describes how well a receding horizon planner can predict the robot's future
state. To this end, we report an improvement of up to 71% using our proposed
single-task approach, making fast locomotion feasible and revealing
wheeled-legged robots' full potential.Comment: 8 pages, 6 figures, 1 table, 52 references, 9 equation
Motion Planning and Feedback Control of Simulated Robots in Multi-Contact Scenarios
Diese Dissertation präsentiert eine optimale steuerungsbasierte Architektur für die Bewegungsplanung und Rückkopplungssteuerung simulierter Roboter in Multikontaktszenarien. Bewegungsplanung und -steuerung sind grundlegende Bausteine für die Erstellung wirklich autonomer Roboter. Während in diesen Bereichen enorme Fortschritte für Manipulatoren mit festem Sockel und Radrobotern in den letzten Jahren erzielt wurden, besteht das Problem der Bewegungsplanung und -steuerung für Roboter mit Armen und Beinen immer noch ein ungelöstes Problem, das die Notwendigkeit effizienterer und robusterer Algorithmen belegt. In diesem Zusammenhang wird in dieser Dissertation eine Architektur vorgeschlagen, mit der zwei Hauptherausforderungen angegangen werden sollen, nämlich die effiziente Planung von Kontaktsequenzen und Ganzkörperbewegungen für Floating-Base-Roboter sowie deren erfolgreiche Ausführung mit Rückkopplungsregelungsstrategien, die Umgebungsunsicherheiten bewältigen könne
Recent Progress in Legged Robots Locomotion Control
International audiencePurpose of review. In recent years, legged robots locomotion has been transitioning from mostly flat ground in controlled settings to generic indoor and outdoor environments, approaching now real industrial scenarios. This paper aims at documenting some of the key progress made in legged locomotion control that enabled this transition. Recent findings. Legged locomotion control makes extensive use of numerical trajectory optimization and its online implementation, Model Predictive Control. A key progress has been how this optimization is handled, with refined models and refined numerical methods. This led the legged locomotion research community to heavily invest in and contribute to the development of new optimization methods and efficient numerical software
On Centroidal Dynamics and Integrability of Average Angular Velocity
In the literature on robotics and multibody dynamics, the concept of average
angular velocity has received considerable attention in recent years. We
address the question of whether the average angular velocity defines an
orientation framethat depends only on the current robot configuration and
provide a simple algebraic condition to check whether this holds. In the
language of geometric mechanics, this condition corresponds to requiring the
flatness of the mechanical connection associated to the robotic system. Here,
however, we provide both a reinterpretation and a proof of this result
accessible to readers with a background in rigid body kinematics and multibody
dynamics but not necessarily acquainted with differential geometry, still
providing precise links to the geometric mechanics literature. This should help
spreading the algebraic condition beyond the scope of geometric
mechanics,contributing to a proper utilization and understanding of the concept
of average angular velocity.Comment: 8 pages, accepted for IEEE Robotics and Automation Letters (RA-L