2,851 research outputs found
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
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
On Advanced Mobility Concepts for Intelligent Planetary Surface Exploration
Surface exploration by wheeled rovers on Earth's Moon (the two Lunokhods) and Mars (Nasa's Sojourner and the two MERs) have been followed since many years already very suc-cessfully, specifically concerning operations over long time. However, despite of this success, the explored surface area was very small, having in mind a total driving distance of about 8 km (Spirit) and 21 km (Opportunity) over 6 years of operation. Moreover, ESA will send its ExoMars rover in 2018 to Mars, and NASA its MSL rover probably this year. However, all these rovers are lacking sufficient on-board intelligence in order to overcome longer dis-tances, driving much faster and deciding autonomously on path planning for the best trajec-tory to follow. In order to increase the scientific output of a rover mission it seems very nec-essary to explore much larger surface areas reliably in much less time. This is the main driver for a robotics institute to combine mechatronics functionalities to develop an intelligent mo-bile wheeled rover with four or six wheels, and having specific kinematics and locomotion suspension depending on the operational terrain of the rover to operate. DLR's Robotics and Mechatronics Center has a long tradition in developing advanced components in the field of light-weight motion actuation, intelligent and soft manipulation and skilled hands and tools, perception and cognition, and in increasing the autonomy of any kind of mechatronic systems. The whole design is supported and is based upon detailed modeling, optimization, and simula-tion tasks. We have developed efficient software tools to simulate the rover driveability per-formance on various terrain characteristics such as soft sandy and hard rocky terrains as well as on inclined planes, where wheel and grouser geometry plays a dominant role. Moreover, rover optimization is performed to support the best engineering intuitions, that will optimize structural and geometric parameters, compare various kinematics suspension concepts, and make use of realistic cost functions like mass and consumed energy minimization, static sta-bility, and more. For self-localization and safe navigation through unknown terrain we make use of fast 3D stereo algorithms that were successfully used e.g. in unmanned air vehicle ap-plications and on terrestrial mobile systems. The advanced rover design approach is applica-ble for lunar as well as Martian surface exploration purposes. A first mobility concept ap-proach for a lunar vehicle will be presented
Analytic Model for Quadruped Locomotion Task-Space Planning
Despite the extensive presence of the legged locomotion in animals, it is
extremely challenging to be reproduced with robots. Legged locomotion is an
dynamic task which benefits from a planning that takes advantage of the
gravitational pull on the system. However, the computational cost of such
optimization rapidly increases with the complexity of kinematic structures,
rendering impossible real-time deployment in unstructured environments. This
paper proposes a simplified method that can generate desired centre of mass and
feet trajectory for quadrupeds. The model describes a quadruped as two bipeds
connected via their centres of mass, and it is based on the extension of an
algebraic bipedal model that uses the topology of the gravitational attractor
to describe bipedal locomotion strategies. The results show that the model
generates trajectories that agrees with previous studies. The model will be
deployed in the future as seed solution for whole-body trajectory optimization
in the attempt to reduce the computational cost and obtain real-time planning
of complex action in challenging environments.Comment: Accepted to be Published in 2019, 41th Annual International
Conference of the IEEE Engineering in Medicine and Biology Society (EMBC),
Berlin German
Robust Whole-Body Motion Control of Legged Robots
We introduce a robust control architecture for the whole-body motion control
of torque controlled robots with arms and legs. The method is based on the
robust control of contact forces in order to track a planned Center of Mass
trajectory. Its appeal lies in the ability to guarantee robust stability and
performance despite rigid body model mismatch, actuator dynamics, delays,
contact surface stiffness, and unobserved ground profiles. Furthermore, we
introduce a task space decomposition approach which removes the coupling
effects between contact force controller and the other non-contact controllers.
Finally, we verify our control performance on a quadruped robot and compare its
performance to a standard inverse dynamics approach on hardware.Comment: 8 Page
Balancing experiments on a torque-controlled humanoid with hierarchical inverse dynamics
Recently several hierarchical inverse dynamics controllers based on cascades
of quadratic programs have been proposed for application on torque controlled
robots. They have important theoretical benefits but have never been
implemented on a torque controlled robot where model inaccuracies and real-time
computation requirements can be problematic. In this contribution we present an
experimental evaluation of these algorithms in the context of balance control
for a humanoid robot. The presented experiments demonstrate the applicability
of the approach under real robot conditions (i.e. model uncertainty, estimation
errors, etc). We propose a simplification of the optimization problem that
allows us to decrease computation time enough to implement it in a fast torque
control loop. We implement a momentum-based balance controller which shows
robust performance in face of unknown disturbances, even when the robot is
standing on only one foot. In a second experiment, a tracking task is evaluated
to demonstrate the performance of the controller with more complicated
hierarchies. Our results show that hierarchical inverse dynamics controllers
can be used for feedback control of humanoid robots and that momentum-based
balance control can be efficiently implemented on a real robot.Comment: appears in IEEE/RSJ International Conference on Intelligent Robots
and Systems (IROS), 201
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