4,858 research outputs found
Motion Planning of Legged Robots
We study the problem of computing the free space F of a simple legged robot
called the spider robot. The body of this robot is a single point and the legs
are attached to the body. The robot is subject to two constraints: each leg has
a maximal extension R (accessibility constraint) and the body of the robot must
lie above the convex hull of its feet (stability constraint). Moreover, the
robot can only put its feet on some regions, called the foothold regions. The
free space F is the set of positions of the body of the robot such that there
exists a set of accessible footholds for which the robot is stable. We present
an efficient algorithm that computes F in O(n2 log n) time using O(n2 alpha(n))
space for n discrete point footholds where alpha(n) is an extremely slowly
growing function (alpha(n) <= 3 for any practical value of n). We also present
an algorithm for computing F when the foothold regions are pairwise disjoint
polygons with n edges in total. This algorithm computes F in O(n2 alpha8(n) log
n) time using O(n2 alpha8(n)) space (alpha8(n) is also an extremely slowly
growing function). These results are close to optimal since Omega(n2) is a
lower bound for the size of F.Comment: 29 pages, 22 figures, prelininar results presented at WAFR94 and IEEE
Robotics & Automation 9
Dynamic Motion Modelling for Legged Robots
An accurate motion model is an important component in modern-day robotic
systems, but building such a model for a complex system often requires an
appreciable amount of manual effort. In this paper we present a motion model
representation, the Dynamic Gaussian Mixture Model (DGMM), that alleviates the
need to manually design the form of a motion model, and provides a direct means
of incorporating auxiliary sensory data into the model. This representation and
its accompanying algorithms are validated experimentally using an 8-legged
kinematically complex robot, as well as a standard benchmark dataset. The
presented method not only learns the robot's motion model, but also improves
the model's accuracy by incorporating information about the terrain surrounding
the robot
Feedback MPC for Torque-Controlled Legged Robots
The computational power of mobile robots is currently insufficient to achieve
torque level whole-body Model Predictive Control (MPC) at the update rates
required for complex dynamic systems such as legged robots. This problem is
commonly circumvented by using a fast tracking controller to compensate for
model errors between updates. In this work, we show that the feedback policy
from a Differential Dynamic Programming (DDP) based MPC algorithm is a viable
alternative to bridge the gap between the low MPC update rate and the actuation
command rate. We propose to augment the DDP approach with a relaxed barrier
function to address inequality constraints arising from the friction cone. A
frequency-dependent cost function is used to reduce the sensitivity to
high-frequency model errors and actuator bandwidth limits. We demonstrate that
our approach can find stable locomotion policies for the torque-controlled
quadruped, ANYmal, both in simulation and on hardware.Comment: Paper accepted to IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS 2019
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
Robust Legged Robot State Estimation Using Factor Graph Optimization
Legged robots, specifically quadrupeds, are becoming increasingly attractive
for industrial applications such as inspection. However, to leave the
laboratory and to become useful to an end user requires reliability in harsh
conditions. From the perspective of state estimation, it is essential to be
able to accurately estimate the robot's state despite challenges such as uneven
or slippery terrain, textureless and reflective scenes, as well as dynamic
camera occlusions. We are motivated to reduce the dependency on foot contact
classifications, which fail when slipping, and to reduce position drift during
dynamic motions such as trotting. To this end, we present a factor graph
optimization method for state estimation which tightly fuses and smooths
inertial navigation, leg odometry and visual odometry. The effectiveness of the
approach is demonstrated using the ANYmal quadruped robot navigating in a
realistic outdoor industrial environment. This experiment included trotting,
walking, crossing obstacles and ascending a staircase. The proposed approach
decreased the relative position error by up to 55% and absolute position error
by 76% compared to kinematic-inertial odometry.Comment: 8 pages, 12 figures. Accepted to RA-L + IROS 2019, July 201
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