Quadrupedal locomotion on uneven terrain with sensorized feet
May 27, 2020
Video submission for ICRA 2020: Quadrupedal locomotion on uneven terrain with sensorized feet
Paper: https://doi.org/10.1109/LRA.2020.2969160
Paper Abstract:
Sensing of the terrain shape is crucial for legged robots deployed in the real world since the knowledge of the local terrain inclination at the contact points allows for an optimized force distribution that minimizes the risk of slipping. In this letter, we present a reactive locomotion strategy for torque controllable quadruped robots based on sensorized feet. Since the present approach works without exteroceptive sensing, it is robust against degraded vision. Inertial and force/torque sensors implemented in specially designed feet with articulated passive ankle joints measure the local terrain inclination and interaction forces. The proposed controller exploits the contact null-space in order to minimize the tangential forces to prevent slippage even in case of extreme contact conditions. We experimentally tested the proposed method in laboratory experiments and validated the approach with the quadrupedal robot ANYmal.
Towards autonomous inspection of concrete deterioration in sewers with legged robots
Jun 1, 2020
We demonstrate a method to assess the concrete deterioration in sewers by performing a tactile inspection motion with a sensorized foot of a legged robot.
Title: Towards autonomous inspection of concrete deterioration in sewers with legged robots
Authors: Hendrik Kolvenbach, David Wisth, Russell Buchanan, Giorgio Valsecchi, Ruben Grandia, Maurice Fallon, Marco Hutter
This robot can tell when sewers need repairing by scratching the walls
Jun 5, 2020
"This robot can tell when sewers need repairing by scratching the walls"A four-legged robot that inspects concrete can walk through underground sewage tunnels and detect when they need repairing.
Hendrik Kolvenbach at the Swiss Federal Institute of Technology (ETH Zurich) in Switzerland and his colleagues have developed a robot that scratches one of its legs against concrete to determine the condition it is in.
by Donna Lu
June 4, 2020
Perceptive whole body planning for multi-legged robots in confined spaces
Jun 15, 2020
Legged robots are exceedingly versatile and have the potential to navigate complex, confined spaces due to their many degrees of freedom. As a result of the computational complexity, there exist no online planners for perceptive whole-body locomotion of robots in tight spaces. In this paper, we present a new method for perceptive planning for multi-legged robots, which generates body poses, footholds, and swing trajectories for collision avoidance. Measurements from an onboard depth camera are used to create a 3D map of the terrain around the robot. We randomly sample body poses then smooth the resulting trajectory while satisfying several constraints such as robot kinematics and collision avoidance. Footholds and swing trajectories are computed based on the terrain, and the robot body pose is optimized to ensure stable locomotion while not colliding with the environment. Our method is designed to run online on a real robot and generate trajectories several meters long. We first tested our algorithm in several simulations with varied confined spaces using the quadrupedal robot ANYmal. We also simulated experiments with the hexapod robot Weaver to demonstrate applicability to different legged robot configurations. Then, we demonstrated our whole body planner in several online experiments both indoors and in realistic scenarios at an emergency rescue training facility. ANYmal, which has a nominal standing height of 80 cm and width of 59 cm, navigated through several representative disaster areas with openings as small as 60 cm. 3 m trajectories were re-planned with 500 ms update times.
Article accepted for Journal of Field Robotics
Authors: Russell Buchanan, Lorenz Wellhausen, Marko Bjelonic, Tirthankar Bandyopadhyay, Navinda Kottege, Marco Hutter
ANYmal C legged robot showcase
Jul 8, 2020
ANYmal C, the autonomous legged robot designed for industrial challenging environments, provides the mobility, autonomy and inspection intelligence to enable safe and efficient inspection operations. In this virtual showcase, discover how ANYmal climbs stairs, recovers from a fall, performs an autonomous mission and avoids obstacles, docks to charge by itself, digitizes analogue sensors and monitors the environment.
Haptic sequential Monte Carlo localization for quadrupedal locomotion in vision-denied scenarios
Jul 23, 2020
by Russell Buchanan, Marco Camurri, Maurice Fallon
Continuous robot operation in extreme scenarios such as underground mines or sewers is difficult because exteroceptive sensors may fail due to fog, darkness, dirt or malfunction. So as to enable autonomous navigation in these kinds of situations, we have developed a type of proprioceptive localization which exploits the foot contacts made by a quadruped robot to localize against a prior map of an environment, without the help of any camera or LIDAR sensor. The proposed method enables the robot to accurately re-localize itself after making a sequence of contact events over a terrain feature. The method is based on Sequential Monte Carlo and can support both 2.5D and 3D prior map representations. We have tested the approach online and onboard the ANYmal quadruped robot in two different scenarios: the traversal of a custom built wooden terrain course and a wall probing and following task. In both scenarios, the robot is able to effectively achieve a localization match and to execute a desired pre-planned path. The method keeps the localization error down to 10cm on feature rich terrain by only using its feet, kinematic and inertial sensing.
Perceptive locomotion in rough terrain - Online foothold optimization
Aug 7, 2020
Paper Abstract:
Compared to wheeled vehicles, legged systems have a vast potential to traverse challenging terrain. To exploit the full potential, it is crucial to tightly integrate terrain perception for foothold planning. We present a hierarchical locomotion planner together with a foothold optimizer that finds locally optimal footholds within an elevation map. The map is generated in real-time from on-board depth sensors. We further propose a terrain-aware contact schedule to deal with actuator velocity limits. We validate the combined locomotion pipeline on our quadrupedal robot ANYmal with a variety of simulated and real-world experiments. We show that our method can cope with stairs and obstacles of heights up to 33 % of the robot’s leg length.
The paper is accepted to IEEE Robotics and Automation Letters (RA-L) and IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2020).
ANYmal tackles construction safety
Oct 20, 2020
ANYbotics partnered with Losinger Marazzi to explore ANYmal’s potential of patrolling construction sites to identify and report safety issues. With such a complex environment, only a robot designed to navigate difficult terrain is able to bring digitalization to such a physically demanding industry.
Learning quadrupedal locomotion over challenging terrain
Oct 21, 2020
We present a radically robust locomotion controller for quadrupedal robots which is trained using reinforcement learning in simulation. It uses no external sensing like cameras or Lidar and relies only on internal sensors like IMU and joint encoders.
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