4 research outputs found
Hierarchical Adaptive Loco-manipulation Control for Quadruped Robots
Legged robots have shown remarkable advantages in navigating uneven terrain.
However, realizing effective locomotion and manipulation tasks on quadruped
robots is still challenging. In addition, object and terrain parameters are
generally unknown to the robot in these problems. Therefore, this paper
proposes a hierarchical adaptive control framework that enables legged robots
to perform loco-manipulation tasks without any given assumption on the object's
mass, the friction coefficient, or the slope of the terrain. In our approach,
we first present an adaptive manipulation control to regulate the contact force
to manipulate an unknown object on unknown terrain. We then introduce a unified
model predictive control (MPC) for loco-manipulation that takes into account
the manipulation force in our robot dynamics. The proposed MPC framework thus
can effectively regulate the interaction force between the robot and the object
while keeping the robot balance. Experimental validation of our proposed
approach is successfully conducted on a Unitree A1 robot, allowing it to
manipulate an unknown time-varying load up to ( of the robot's
weight). Moreover, our framework enables fast adaptation to unknown slopes (up
to ) or different surfaces with different friction coefficients.Comment: Accepted to appear at IEEE International Conference on Robotics and
Automation (ICRA), 202
Hierarchical Adaptive Control for Collaborative Manipulation of a Rigid Object by Quadrupedal Robots
Despite the potential benefits of collaborative robots, effective
manipulation tasks with quadruped robots remain difficult to realize. In this
paper, we propose a hierarchical control system that can handle real-world
collaborative manipulation tasks, including uncertainties arising from object
properties, shape, and terrain. Our approach consists of three levels of
controllers. Firstly, an adaptive controller computes the required force and
moment for object manipulation without prior knowledge of the object's
properties and terrain. The computed force and moment are then optimally
distributed between the team of quadruped robots using a Quadratic Programming
(QP)-based controller. This QP-based controller optimizes each robot's contact
point location with the object while satisfying constraints associated with
robot-object contact. Finally, a decentralized loco-manipulation controller is
designed for each robot to apply manipulation force while maintaining the
robot's stability. We successfully validated our approach in a high-fidelity
simulation environment where a team of quadruped robots manipulated an unknown
object weighing up to 18 kg on different terrains while following the desired
trajectory.Comment: Accepted to appear at IEEE/RSJ International Conference on
Intelligent Robots and Systems, IROS, 202
Adaptive Force-Based Control of Dynamic Legged Locomotion over Uneven Terrain
Agile-legged robots have proven to be highly effective in navigating and
performing tasks in complex and challenging environments, including disaster
zones and industrial settings. However, these applications normally require the
capability of carrying heavy loads while maintaining dynamic motion. Therefore,
this paper presents a novel methodology for incorporating adaptive control into
a force-based control system. Recent advancements in the control of quadruped
robots show that force control can effectively realize dynamic locomotion over
rough terrain. By integrating adaptive control into the force-based controller,
our proposed approach can maintain the advantages of the baseline framework
while adapting to significant model uncertainties and unknown terrain impact
models. Experimental validation was successfully conducted on the Unitree A1
robot. With our approach, the robot can carry heavy loads (up to 50% of its
weight) while performing dynamic gaits such as fast trotting and bounding
across uneven terrains