736 research outputs found
Frequency-Aware Model Predictive Control
Transferring solutions found by trajectory optimization to robotic hardware
remains a challenging task. When the optimization fully exploits the provided
model to perform dynamic tasks, the presence of unmodeled dynamics renders the
motion infeasible on the real system. Model errors can be a result of model
simplifications, but also naturally arise when deploying the robot in
unstructured and nondeterministic environments. Predominantly, compliant
contacts and actuator dynamics lead to bandwidth limitations. While classical
control methods provide tools to synthesize controllers that are robust to a
class of model errors, such a notion is missing in modern trajectory
optimization, which is solved in the time domain. We propose frequency-shaped
cost functions to achieve robust solutions in the context of optimal control
for legged robots. Through simulation and hardware experiments we show that
motion plans can be made compatible with bandwidth limits set by actuators and
contact dynamics. The smoothness of the model predictive solutions can be
continuously tuned without compromising the feasibility of the problem.
Experiments with the quadrupedal robot ANYmal, which is driven by
highly-compliant series elastic actuators, showed significantly improved
tracking performance of the planned motion, torque, and force trajectories and
enabled the machine to walk robustly on terrain with unmodeled compliance
Hierarchical Experience-informed Navigation for Multi-modal Quadrupedal Rebar Grid Traversal
This study focuses on a layered, experience-based, multi-modal contact
planning framework for agile quadrupedal locomotion over a constrained rebar
environment. To this end, our hierarchical planner incorporates
locomotion-specific modules into the high-level contact sequence planner and
solves kinodynamically-aware trajectory optimization as the low-level motion
planner. Through quantitative analysis of the experience accumulation process
and experimental validation of the kinodynamic feasibility of the generated
locomotion trajectories, we demonstrate that the experience planning heuristic
offers an effective way of providing candidate footholds for a legged contact
planner. Additionally, we introduce a guiding torso path heuristic at the
global planning level to enhance the navigation success rate in the presence of
environmental obstacles. Our results indicate that the torso-path guided
experience accumulation requires significantly fewer offline trials to
successfully reach the goal compared to regular experience accumulation.
Finally, our planning framework is validated in both dynamics simulations and
real hardware implementations on a quadrupedal robot provided by Skymul Inc
Creating a Dynamic Quadrupedal Robotic Goalkeeper with Reinforcement Learning
We present a reinforcement learning (RL) framework that enables quadrupedal
robots to perform soccer goalkeeping tasks in the real world. Soccer
goalkeeping using quadrupeds is a challenging problem, that combines highly
dynamic locomotion with precise and fast non-prehensile object (ball)
manipulation. The robot needs to react to and intercept a potentially flying
ball using dynamic locomotion maneuvers in a very short amount of time, usually
less than one second. In this paper, we propose to address this problem using a
hierarchical model-free RL framework. The first component of the framework
contains multiple control policies for distinct locomotion skills, which can be
used to cover different regions of the goal. Each control policy enables the
robot to track random parametric end-effector trajectories while performing one
specific locomotion skill, such as jump, dive, and sidestep. These skills are
then utilized by the second part of the framework which is a high-level planner
to determine a desired skill and end-effector trajectory in order to intercept
a ball flying to different regions of the goal. We deploy the proposed
framework on a Mini Cheetah quadrupedal robot and demonstrate the effectiveness
of our framework for various agile interceptions of a fast-moving ball in the
real world.Comment: First two authors contributed equally. Accompanying video is at
https://youtu.be/iX6OgG67-Z
First Steps Towards Full Model Based Motion Planning and Control of Quadrupeds: A Hybrid Zero Dynamics Approach
The hybrid zero dynamics (HZD) approach has become a powerful tool for the gait planning and control of bipedal robots. This paper aims to extend the HZD methods to address walking, ambling and trotting behaviors on a quadrupedal robot. We present a framework that systematically generates a wide range of optimal trajectories and then provably stabilizes them for the full-order, nonlinear and hybrid dynamical models of quadrupedal locomotion. The gait planning is addressed through a scalable nonlinear programming using direct collocation and HZD. The controller synthesis for the exponential stability is then achieved through the Poincaré sections analysis. In particular, we employ an iterative optimization algorithm involving linear and bilinear matrix inequalities (LMIs and BMIs) to design HZD-based controllers that guarantee the exponential stability of the fixed points for the Poincaré return map. The power of the framework is demonstrated through gait generation and HZD-based controller synthesis for an advanced quadruped robot, —Vision 60, with 36 state variables and 12 control inputs. The numerical simulations as well as real world experiments confirm the validity of the proposed framework
Autonomous Locomotion Mode Transition Simulation of a Track-legged Quadruped Robot Step Negotiation
Multi-modal locomotion (e.g. terrestrial, aerial, and aquatic) is gaining
increasing interest in robotics research as it improves the robots
environmental adaptability, locomotion versatility, and operational
flexibility. Within the terrestrial multiple locomotion robots, the advantage
of hybrid robots stems from their multiple (two or more) locomotion modes,
among which robots can select from depending on the encountering terrain
conditions. However, there are many challenges in improving the autonomy of the
locomotion mode transition between their multiple locomotion modes. This work
proposed a method to realize an autonomous locomotion mode transition of a
track-legged quadruped robot steps negotiation. The autonomy of the
decision-making process was realized by the proposed criterion to comparing
energy performances of the rolling and walking locomotion modes. Two climbing
gaits were proposed to achieve smooth steps negotiation behaviours for energy
evaluation purposes. Simulations showed autonomous locomotion mode transitions
were realized for negotiations of steps with different height. The proposed
method is generic enough to be utilized to other hybrid robots after some
pre-studies of their locomotion energy performances
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