407 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
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
Analytic Model for Quadruped Locomotion Task-Space Planning
Despite the extensive presence of the legged locomotion in animals, it is
extremely challenging to be reproduced with robots. Legged locomotion is an
dynamic task which benefits from a planning that takes advantage of the
gravitational pull on the system. However, the computational cost of such
optimization rapidly increases with the complexity of kinematic structures,
rendering impossible real-time deployment in unstructured environments. This
paper proposes a simplified method that can generate desired centre of mass and
feet trajectory for quadrupeds. The model describes a quadruped as two bipeds
connected via their centres of mass, and it is based on the extension of an
algebraic bipedal model that uses the topology of the gravitational attractor
to describe bipedal locomotion strategies. The results show that the model
generates trajectories that agrees with previous studies. The model will be
deployed in the future as seed solution for whole-body trajectory optimization
in the attempt to reduce the computational cost and obtain real-time planning
of complex action in challenging environments.Comment: Accepted to be Published in 2019, 41th Annual International
Conference of the IEEE Engineering in Medicine and Biology Society (EMBC),
Berlin German
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
CAJun: Continuous Adaptive Jumping using a Learned Centroidal Controller
We present CAJun, a novel hierarchical learning and control framework that
enables legged robots to jump continuously with adaptive jumping distances.
CAJun consists of a high-level centroidal policy and a low-level leg
controller. In particular, we use reinforcement learning (RL) to train the
centroidal policy, which specifies the gait timing, base velocity, and swing
foot position for the leg controller. The leg controller optimizes motor
commands for the swing and stance legs according to the gait timing to track
the swing foot target and base velocity commands using optimal control.
Additionally, we reformulate the stance leg optimizer in the leg controller to
speed up policy training by an order of magnitude. Our system combines the
versatility of learning with the robustness of optimal control. By combining RL
with optimal control methods, our system achieves the versatility of learning
while enjoys the robustness from control methods, making it easily transferable
to real robots. We show that after 20 minutes of training on a single GPU,
CAJun can achieve continuous, long jumps with adaptive distances on a Go1 robot
with small sim-to-real gaps. Moreover, the robot can jump across gaps with a
maximum width of 70cm, which is over 40% wider than existing methods.Comment: Please visit https://yxyang.github.io/cajun/ for additional result
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