405 research outputs found
Fast and Continuous Foothold Adaptation for Dynamic Locomotion through CNNs
Legged robots can outperform wheeled machines for most navigation tasks
across unknown and rough terrains. For such tasks, visual feedback is a
fundamental asset to provide robots with terrain-awareness. However, robust
dynamic locomotion on difficult terrains with real-time performance guarantees
remains a challenge. We present here a real-time, dynamic foothold adaptation
strategy based on visual feedback. Our method adjusts the landing position of
the feet in a fully reactive manner, using only on-board computers and sensors.
The correction is computed and executed continuously along the swing phase
trajectory of each leg. To efficiently adapt the landing position, we implement
a self-supervised foothold classifier based on a Convolutional Neural Network
(CNN). Our method results in an up to 200 times faster computation with respect
to the full-blown heuristics. Our goal is to react to visual stimuli from the
environment, bridging the gap between blind reactive locomotion and purely
vision-based planning strategies. We assess the performance of our method on
the dynamic quadruped robot HyQ, executing static and dynamic gaits (at speeds
up to 0.5 m/s) in both simulated and real scenarios; the benefit of safe
foothold adaptation is clearly demonstrated by the overall robot behavior.Comment: 9 pages, 11 figures. Accepted to RA-L + ICRA 2019, January 201
Oncilla robot: a versatile open-source quadruped research robot with compliant pantograph legs
We present Oncilla robot, a novel mobile, quadruped legged locomotion
machine. This large-cat sized, 5.1 robot is one of a kind of a recent,
bioinspired legged robot class designed with the capability of model-free
locomotion control. Animal legged locomotion in rough terrain is clearly shaped
by sensor feedback systems. Results with Oncilla robot show that agile and
versatile locomotion is possible without sensory signals to some extend, and
tracking becomes robust when feedback control is added (Ajaoolleian 2015). By
incorporating mechanical and control blueprints inspired from animals, and by
observing the resulting robot locomotion characteristics, we aim to understand
the contribution of individual components. Legged robots have a wide mechanical
and control design parameter space, and a unique potential as research tools to
investigate principles of biomechanics and legged locomotion control. But the
hardware and controller design can be a steep initial hurdle for academic
research. To facilitate the easy start and development of legged robots,
Oncilla-robot's blueprints are available through open-source. [...
Motion Planning for Quadrupedal Locomotion:Coupled Planning, Terrain Mapping and Whole-Body Control
Planning whole-body motions while taking into account the terrain conditions is a challenging problem for legged robots since the terrain model might produce many local minima. Our coupled planning method uses stochastic and derivatives-free search to plan both foothold locations and horizontal motions due to the local minima produced by the terrain model. It jointly optimizes body motion, step duration and foothold selection, and it models the terrain as a cost-map. Due to the novel attitude planning method, the horizontal motion plans can be applied to various terrain conditions. The attitude planner ensures the robot stability by imposing limits to the angular acceleration. Our whole-body controller tracks compliantly trunk motions while avoiding slippage, as well as kinematic and torque limits. Despite the use of a simplified model, which is restricted to flat terrain, our approach shows remarkable capability to deal with a wide range of noncoplanar terrains. The results are validated by experimental trials and comparative evaluations in a series of terrains of progressively increasing complexity
Unsupervised Contact Learning for Humanoid Estimation and Control
This work presents a method for contact state estimation using fuzzy
clustering to learn contact probability for full, six-dimensional humanoid
contacts. The data required for training is solely from proprioceptive sensors
- endeffector contact wrench sensors and inertial measurement units (IMUs) -
and the method is completely unsupervised. The resulting cluster means are used
to efficiently compute the probability of contact in each of the six
endeffector degrees of freedom (DoFs) independently. This clustering-based
contact probability estimator is validated in a kinematics-based base state
estimator in a simulation environment with realistic added sensor noise for
locomotion over rough, low-friction terrain on which the robot is subject to
foot slip and rotation. The proposed base state estimator which utilizes these
six DoF contact probability estimates is shown to perform considerably better
than that which determines kinematic contact constraints purely based on
measured normal force.Comment: Submitted to the IEEE International Conference on Robotics and
Automation (ICRA) 201
Multi-segmented Adaptive Feet for Versatile Legged Locomotion in Natural Terrain
Most legged robots are built with leg structures from serially mounted links
and actuators and are controlled through complex controllers and sensor
feedback. In comparison, animals developed multi-segment legs, mechanical
coupling between joints, and multi-segmented feet. They run agile over all
terrains, arguably with simpler locomotion control. Here we focus on developing
foot mechanisms that resist slipping and sinking also in natural terrain. We
present first results of multi-segment feet mounted to a bird-inspired robot
leg with multi-joint mechanical tendon coupling. Our one- and two-segment,
mechanically adaptive feet show increased viable horizontal forces on multiple
soft and hard substrates before starting to slip. We also observe that
segmented feet reduce sinking on soft substrates compared to ball-feet and
cylinder-feet. We report how multi-segmented feet provide a large range of
viable centre of pressure points well suited for bipedal robots, but also for
quadruped robots on slopes and natural terrain. Our results also offer a
functional understanding of segmented feet in animals like ratite birds
Unsupervised Contact Learning for Humanoid Estimation and Control
This work presents a method for contact state estimation using fuzzy
clustering to learn contact probability for full, six-dimensional humanoid
contacts. The data required for training is solely from proprioceptive sensors
- endeffector contact wrench sensors and inertial measurement units (IMUs) -
and the method is completely unsupervised. The resulting cluster means are used
to efficiently compute the probability of contact in each of the six
endeffector degrees of freedom (DoFs) independently. This clustering-based
contact probability estimator is validated in a kinematics-based base state
estimator in a simulation environment with realistic added sensor noise for
locomotion over rough, low-friction terrain on which the robot is subject to
foot slip and rotation. The proposed base state estimator which utilizes these
six DoF contact probability estimates is shown to perform considerably better
than that which determines kinematic contact constraints purely based on
measured normal force.Comment: Submitted to the IEEE International Conference on Robotics and
Automation (ICRA) 201
Design of a Biomimetic Mechanical Leg and Accompanying Sensor System for Terrain Detection
Autonomous robots are useful in a wide range of applications. However, finding a balance between speed and stability in an autonomous robot can be difficult. The goal of this project was to design a biomimetically-inspired robotic leg and accompanying sensor system for detecting terrain; the mechanical leg and sensor system designs in combination are intended to enable a quadruped robot to move quickly while maintaining its stability. In order to accomplish this goal, a leg was designed based on the leg of a cheetah and the team performed a variety of mechanical analyses on it. Additionally, the output from a force sensor landing on hard and muddy surfaces was collected and algorithms for determining which of the two surfaces the robot was walking on were developed
- …