742 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
Detection and estimation of moving obstacles for a UAV
In recent years, research interest in Unmanned Aerial Vehicles (UAVs) has been grown rapidly because of their potential use for a wide range of applications. In this paper, we proposed a vision-based detection and position/velocity estimation of moving obstacle for a UAV. The knowledge of a moving obstacle's state, i.e., position, velocity, is essential to achieve better performance for an intelligent UAV system specially in autonomous navigation and landing tasks. The novelties are: (1) the design and implementation of a localization method using sensor fusion methodology which fuses Inertial Measurement Unit (IMU) signals and Pozyx signals; (2) The development of detection and estimation of moving obstacles method based on on-board vision system. Experimental results validate the effectiveness of the proposed approach. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved
Stereo-vision-based navigation of a six-legged walking robot in unknown rough terrain
In this paper we presents a visual navigation algorithm for the six-legged
walking robot DLR Crawler in rough terrain. The algorithm is based
on stereo images from which depth images are computed using the semi-
global matching (SGM) method. Further, a visual odometry is calculated
along with an error measure. Pose estimates are obtained by fusing iner-
tial data with relative leg odometry and visual odometry measurements
using an indirect information filter. The visual odometry error measure
is used in the filtering process to put lower weights on erroneous visual
odometry data, hence, improving the robustness of pose estimation. From
the estimated poses and the depth images, a dense digital terrain map is
created by applying the locus method. The traversability of the terrain
is estimated by a plane fitting approach and paths are planned using a
D* Lite planner taking the traversability of the terrain and the current
motion capabilities of the robot into account. Motion commands and the
traversability measures of the upcoming terrain are sent to the walking
layer of the robot so that it can choose an appropriate gait for the terrain.
Experimental results show the accuracy of the navigation algorithm and
its robustness against visual disturbances
Recommended from our members
Control Implementation of Dynamic Locomotion on Compliant, Underactuated, Force-Controlled Legged Robots with Non-Anthropomorphic Design
The control of locomotion on legged robots traditionally involves a robot that takes a standard legged form, such as the anthropomorphic humanoid, the dog-like quadruped, or the bird-like biped. Additionally, these systems will often be actuated with position-controlled servos or series-elastic actuators that are connected through rigid links. This work investigates the control implementation of dynamic, force-controlled locomotion on a family of legged systems that significantly deviate from these classic paradigms by incorporating modern, state-of-the-art proprioceptive actuators on uniquely configured compliant legs that do not closely resemble those found in nature. The results of this work can be used to better inform how to implement controllers on legged systems without stiff, position-controlled actuators, and also provide insight on how intelligently designed mechanical features can potentially simplify the control of complex, nonlinear dynamical systems like legged robots. To this end, this work presents the approach to control for a family of non-anthropomorphic bipedal robotic systems which are developed both in simulation and with physical hardware. The first is the Non-Anthropomorphic Biped, Version 1 (NABi-1) that features position-controlled joints along with a compliant foot element on a minimally actuated leg, and is controlled using simple open-loop trajectories based on the Zero Moment Point. The second system is the second version of the non-anthropomorphic biped (NABi-2) which utilizes the proprioceptive Back-drivable Electromagnetic Actuator for Robotics (BEAR) modules for actuation and fully realizes feedback-based force controlled locomotion. These systems are used to highlight both the strengths and weaknesses of utilizing proprioceptive actuation in systems, and suggest the tradeoffs that are made when using force control for dynamic locomotion. These systems also present case studies for different approaches to system design when it comes to bipedal legged robots
Visual-Inertial and Leg Odometry Fusion for Dynamic Locomotion
Implementing dynamic locomotion behaviors on legged robots requires a
high-quality state estimation module. Especially when the motion includes
flight phases, state-of-the-art approaches fail to produce reliable estimation
of the robot posture, in particular base height. In this paper, we propose a
novel approach for combining visual-inertial odometry (VIO) with leg odometry
in an extended Kalman filter (EKF) based state estimator. The VIO module uses a
stereo camera and IMU to yield low-drift 3D position and yaw orientation and
drift-free pitch and roll orientation of the robot base link in the inertial
frame. However, these values have a considerable amount of latency due to image
processing and optimization, while the rate of update is quite low which is not
suitable for low-level control. To reduce the latency, we predict the VIO state
estimate at the rate of the IMU measurements of the VIO sensor. The EKF module
uses the base pose and linear velocity predicted by VIO, fuses them further
with a second high-rate IMU and leg odometry measurements, and produces robot
state estimates with a high frequency and small latency suitable for control.
We integrate this lightweight estimation framework with a nonlinear model
predictive controller and show successful implementation of a set of agile
locomotion behaviors, including trotting and jumping at varying horizontal
speeds, on a torque-controlled quadruped robot.Comment: Submitted to IEEE International Conference on Robotics and Automation
(ICRA), 202
- …