672 research outputs found
A Benchmarking of DCM Based Architectures for Position and Velocity Controlled Walking of Humanoid Robots
This paper contributes towards the development and comparison of
Divergent-Component-of-Motion (DCM) based control architectures for humanoid
robot locomotion. More precisely, we present and compare several DCM based
implementations of a three layer control architecture. From top to bottom,
these three layers are here called: trajectory optimization, simplified model
control, and whole-body QP control. All layers use the DCM concept to generate
references for the layer below. For the simplified model control layer, we
present and compare both instantaneous and Receding Horizon Control
controllers. For the whole-body QP control layer, we present and compare
controllers for position and velocity control robots. Experimental results are
carried out on the one-meter tall iCub humanoid robot. We show which
implementation of the above control architecture allows the robot to achieve a
walking velocity of 0.41 meters per second.Comment: Submitted to Humanoids201
Walking Stabilization Using Step Timing and Location Adjustment on the Humanoid Robot, Atlas
While humans are highly capable of recovering from external disturbances and
uncertainties that result in large tracking errors, humanoid robots have yet to
reliably mimic this level of robustness. Essential to this is the ability to
combine traditional "ankle strategy" balancing with step timing and location
adjustment techniques. In doing so, the robot is able to step quickly to the
necessary location to continue walking. In this work, we present both a new
swing speed up algorithm to adjust the step timing, allowing the robot to set
the foot down more quickly to recover from errors in the direction of the
current capture point dynamics, and a new algorithm to adjust the desired
footstep, expanding the base of support to utilize the center of pressure
(CoP)-based ankle strategy for balance. We then utilize the desired centroidal
moment pivot (CMP) to calculate the momentum rate of change for our
inverse-dynamics based whole-body controller. We present simulation and
experimental results using this work, and discuss performance limitations and
potential improvements
Straight-Leg Walking Through Underconstrained Whole-Body Control
We present an approach for achieving a natural, efficient gait on bipedal
robots using straightened legs and toe-off. Our algorithm avoids complex height
planning by allowing a whole-body controller to determine the straightest
possible leg configuration at run-time. The controller solutions are biased
towards a straight leg configuration by projecting leg joint angle objectives
into the null-space of the other quadratic program motion objectives. To allow
the legs to remain straight throughout the gait, toe-off was utilized to
increase the kinematic reachability of the legs. The toe-off motion is achieved
through underconstraining the foot position, allowing it to emerge naturally.
We applied this approach of under-specifying the motion objectives to the Atlas
humanoid, allowing it to walk over a variety of terrain. We present both
experimental and simulation results and discuss performance limitations and
potential improvements.Comment: Submitted to 2018 IEEE International Conference on Robotics and
Automatio
Disturbance rejection for legged robots through a hybrid observer
A legged robot needs to move in unstructured environments continuously subject to disturbances. Existing disturbance observers are not enough when significant forces act on both the center of mass and the robot’s legs, and they usually employ indirect measures of the floating base’s velocity. This paper presents a solution combining a momentum-based observer for the angular term and an acceleration-based observer for the translational one, employing directly measurable values from the sensors. Due to this combination, we define this observer as ”hybrid,” and it can detect disturbances acting on both the legged robot’s center of mass and its legs. The estimation is employed in a whole-body controller. The framework is tested in simulation on a quadruped robot subject to significant disturbances, and it is compared with existing observer-based techniques
Analyzing Whole-Body Pose Transitions in Multi-Contact Motions
When executing whole-body motions, humans are able to use a large variety of
support poses which not only utilize the feet, but also hands, knees and elbows
to enhance stability. While there are many works analyzing the transitions
involved in walking, very few works analyze human motion where more complex
supports occur.
In this work, we analyze complex support pose transitions in human motion
involving locomotion and manipulation tasks (loco-manipulation). We have
applied a method for the detection of human support contacts from motion
capture data to a large-scale dataset of loco-manipulation motions involving
multi-contact supports, providing a semantic representation of them. Our
results provide a statistical analysis of the used support poses, their
transitions and the time spent in each of them. In addition, our data partially
validates our taxonomy of whole-body support poses presented in our previous
work.
We believe that this work extends our understanding of human motion for
humanoids, with a long-term objective of developing methods for autonomous
multi-contact motion planning.Comment: 8 pages, IEEE-RAS International Conference on Humanoid Robots
(Humanoids) 201
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