5,698 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
Motion planning algorithms for stratified kinematic systems with application to the hexapod robot
The paper addresses the motion planning problem of legged robots. Kinematic models of these robots are stratified, i.e. the equations of motion differ on different strata. An improved version of the motion planning algorithm proposed in the literature is compared with two alternative solutions via the example of the six-legged (hexapod) robot. The first alternative solution uses explicit integration of the vector fields while the second one exploits the flatness of a restricted subsystem
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
Online Dynamic Motion Planning and Control for Wheeled Biped Robots
Wheeled-legged robots combine the efficiency of wheeled robots when driving
on suitably flat surfaces and versatility of legged robots when stepping over
or around obstacles. This paper introduces a planning and control framework to
realise dynamic locomotion for wheeled biped robots. We propose the Cart-Linear
Inverted Pendulum Model (Cart-LIPM) as a template model for the rolling motion
and the under-actuated LIPM for contact changes while walking. The generated
motion is then tracked by an inverse dynamic whole-body controller which
coordinates all joints, including the wheels. The framework has a hierarchical
structure and is implemented in a model predictive control (MPC) fashion. To
validate the proposed approach for hybrid motion generation, two scenarios
involving different types of obstacles are designed in simulation. To the best
of our knowledge, this is the first time that such online dynamic hybrid
locomotion has been demonstrated on wheeled biped robots
Hierarchical Reinforcement Learning for Precise Soccer Shooting Skills using a Quadrupedal Robot
We address the problem of enabling quadrupedal robots to perform precise
shooting skills in the real world using reinforcement learning. Developing
algorithms to enable a legged robot to shoot a soccer ball to a given target is
a challenging problem that combines robot motion control and planning into one
task. To solve this problem, we need to consider the dynamics limitation and
motion stability during the control of a dynamic legged robot. Moreover, we
need to consider motion planning to shoot the hard-to-model deformable ball
rolling on the ground with uncertain friction to a desired location. In this
paper, we propose a hierarchical framework that leverages deep reinforcement
learning to train (a) a robust motion control policy that can track arbitrary
motions and (b) a planning policy to decide the desired kicking motion to shoot
a soccer ball to a target. We deploy the proposed framework on an A1
quadrupedal robot and enable it to accurately shoot the ball to random targets
in the real world.Comment: Accepted to 2022 IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS 2022
Real-Time Motion Planning of Legged Robots: A Model Predictive Control Approach
We introduce a real-time, constrained, nonlinear Model Predictive Control for
the motion planning of legged robots. The proposed approach uses a constrained
optimal control algorithm known as SLQ. We improve the efficiency of this
algorithm by introducing a multi-processing scheme for estimating value
function in its backward pass. This pass has been often calculated as a single
process. This parallel SLQ algorithm can optimize longer time horizons without
proportional increase in its computation time. Thus, our MPC algorithm can
generate optimized trajectories for the next few phases of the motion within
only a few milliseconds. This outperforms the state of the art by at least one
order of magnitude. The performance of the approach is validated on a quadruped
robot for generating dynamic gaits such as trotting.Comment: 8 page
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