44 research outputs found
Motion Planning and Control of A Morphing Quadrotor in Restricted Scenarios
Morphing quadrotors with four external actuators can adapt to different
restricted scenarios by changing their geometric structure. However, previous
works mainly focus on the improvements in structures and controllers, and
existing planning algorithms don't consider the morphological modifications,
which leads to safety and dynamic feasibility issues. In this paper, we propose
a unified planning and control framework for morphing quadrotors to deform
autonomously and efficiently. The framework consists of a milliseconds-level
spatial-temporal trajectory optimizer that takes into account the morphological
modifications of quadrotors. The optimizer can generate full-body safety
trajectories including position and attitude. Additionally, it incorporates a
nonlinear attitude controller that accounts for aerodynamic drag and
dynamically adjusts dynamic parameters such as the inertia tensor and Center of
Gravity. The controller can also online compute the thrust coefficient during
morphing. Benchmark experiments compared with existing methods validate the
robustness of the proposed controller. Extensive simulations and real-world
experiments are performed to demonstrate the effectiveness of the proposed
framework.Comment: 8 pages, 9 figure
A Model-based Hierarchical Controller for Legged Systems subject to External Disturbances
Xin G, Lin H-C, Smith J, Cebe O, Mistry M. A Model-based Hierarchical Controller for Legged Systems subject to External Disturbances. In: IEEE/RSJ Int. Conf. on Robotics and Automation. 2018.Legged robots have many potential applications
in real-world scenarios where the tasks are too dangerous for
humans, and compliance is needed to protect the system against
external disturbances and impacts. In this paper, we propose a
model-based controller for hierarchical tasks of legged systems
subject to external disturbance. The control framework is
based on projected inverse dynamics controller, such that the
control law is decomposed into two orthogonal subspaces,
i.e., the constrained and the unconstrained subspaces. The
unconstrained component controls multiple desired tasks with
impedance responses. The constrained space controller maintains
the contact subject to unknown external disturbances,
without the use of any force/torque sensing at the contact
points. By explicitly modelling the external force, our controller
is robust to external disturbances and errors arising from
incorrect dynamic model information. The main contributions
of this paper include (1) incorporating an impedance controller
to control external disturbances and allow impedance shaping
to adjust the behaviour of the motion under external disturbances,
(2) optimising contact forces within the constrained
subspace that also takes into account the external disturbances
without using force/torque sensors at the contact locations. The
techniques are evaluated on the ANYmal quadruped platform
under a variety of scenarios
Optimisation of Body-ground Contact for Augmenting Whole-Body Loco-manipulation of Quadruped Robots
Legged robots have great potential to perform loco-manipulation tasks, yet it
is challenging to keep the robot balanced while it interacts with the
environment. In this paper we study the use of additional contact points for
maximising the robustness of loco-manipulation motions. Specifically,
body-ground contact is studied for enhancing robustness and manipulation
capabilities of quadrupedal robots. We propose to equip the robot with prongs:
small legs rigidly attached to the body which ensure body-ground contact occurs
in controllable point-contacts. The effect of these prongs on robustness is
quantified by computing the Smallest Unrejectable Force (SUF), a measure of
robustness related to Feasible Wrench Polytopes. We apply the SUF to assess the
robustness of the system, and propose an effective approximation of the SUF
that can be computed at near-real-time speed. We design a hierarchical
quadratic programming based whole-body controller that controls stable
interaction when the prongs are in contact with the ground. This novel concept
of using prongs and the resulting control framework are all implemented on
hardware to validate the effectiveness of the increased robustness and newly
enabled loco-manipulation tasks, such as obstacle clearance and manipulation of
a large object
Robust Footstep Planning and LQR Control for Dynamic Quadrupedal Locomotion
In this paper, we aim to improve the robustness of dynamic quadrupedal
locomotion through two aspects: 1) fast model predictive foothold planning, and
2) applying LQR to projected inverse dynamic control for robust motion
tracking. In our proposed planning and control framework, foothold plans are
updated at 400 Hz considering the current robot state and an LQR controller
generates optimal feedback gains for motion tracking. The LQR optimal gain
matrix with non-zero off-diagonal elements leverages the coupling of dynamics
to compensate for system underactuation. Meanwhile, the projected inverse
dynamic control complements the LQR to satisfy inequality constraints. In
addition to these contributions, we show robustness of our control framework to
unmodeled adaptive feet. Experiments on the quadruped ANYmal demonstrate the
effectiveness of the proposed method for robust dynamic locomotion given
external disturbances and environmental uncertainties
Bounded haptic teleoperation of a quadruped robot’s foot posture for sensing and manipulation
This paper presents a control framework to teleoperate a quadruped robot's
foot for operator-guided haptic exploration of the environment. Since one leg
of a quadruped robot typically only has 3 actuated degrees of freedom (DoFs),
the torso is employed to assist foot posture control via a hierarchical
whole-body controller. The foot and torso postures are controlled by two
analytical Cartesian impedance controllers cascaded by a null space projector.
The contact forces acting on supporting feet are optimized by quadratic
programming (QP). The foot's Cartesian impedance controller may also estimate
contact forces from trajectory tracking errors, and relay the force-feedback to
the operator. A 7D haptic joystick, Sigma.7, transmits motion commands to the
quadruped robot ANYmal, and renders the force feedback. Furthermore, the
joystick's motion is bounded by mapping the foot's feasible force polytope
constrained by the friction cones and torque limits in order to prevent the
operator from driving the robot to slipping or falling over. Experimental
results demonstrate the efficiency of the proposed framework.Comment: Under review. Video Available at
https://www.youtube.com/watch?v=htI8202vfe
Automatic Gait Pattern Selection for Legged Robots
An important issue when synthesizing legged locomotion plans is the combinatorial complexity that arises from gait pattern selection. Though it can be defined manually, the gait pattern plays an important role in the feasibility and optimality of a motion with respect to a task. Replacing human intuition with an automatic and efficient approach for gait pattern selection would allow for more autonomous robots, responsive to task and environment changes. To this end, we propose the idea of building a map from task to gait pattern selection for given environment and performance objective. Indeed, we show that for a 2D half-cheetah model and a quadruped robot, a direct mapping between a given task and an optimal gait pattern can be established. We use supervised learning to capture the structure of this map in a form of gait regions. Furthermore, we propose to construct a warm-starting trajectory for each gait region. We empirically show that these warm-starting trajectories improve the convergence speed of our trajectory optimization problem up to 60 times when compared with random initial guesses. Finally, we conduct experimental trials on the ANYmal robot to validate our method.</p