Periodic Reporting for period 2 - THING (subTerranean Haptic INvestiGator)
Periodo di rendicontazione: 2019-07-01 al 2021-09-30
The goal of THING is to advance the perceptual capabilities of highly mobile legged platforms through haptic perception and active exploration. In this light, THING will deliver: 1) Novel foot designs for enhanced tactile perception and locomotion, 2) Improved perceptual capability, enriching existing modalities (lidar, vision) with haptic information, 3) Heightened physical sense of the environment, including friction, ground stability (difficult through vision alone), and 4) Enhanced mobility through improved perception, prediction, and control.
The project has succeeded in the development of: 1) novel mechatronic designs and prototyping of adaptive feet and ankles for the ANYmal quadruped, 2) new localisation and navigation algorithms fusing haptic information with traditional exteroceptive perception, 3) novel control methods for physical interaction and haptic sensing of the environment, 4) novel planning and optimisation approaches to leverage whole-body impedance and adaptive feet.
We extensively tested the scientific outcomes of the THING project in real-world scenarios, including mines and sewers. In subterranean environments we showed how the robot can robustly navigate the difficult terrain, build a 3D and thermal map of the infrastructure (e.g. indicating which mechanical parts are overheating) and feel the ground to check for surface quality. The project has enabled enhanced mobility and new modes of perception to achieve inspection tasks in challenging industrial environments.
The project has succeeded in the development of: 1) novel mechatronic designs and prototyping of adaptive feet and ankles for the ANYmal quadruped, 2) new localisation and navigation algorithms fusing haptic information with traditional exteroceptive perception, 3) novel control methods for physical interaction and haptic sensing of the environment, 4) novel planning and optimisation approaches to leverage whole-body impedance and adaptive feet.
We extensively tested the scientific outcomes of the THING project in real-world scenarios, including mines and sewers. In subterranean environments we showed how the robot can robustly navigate the difficult terrain, build a 3D and thermal map of the infrastructure (e.g. indicating which mechanical parts are overheating) and feel the ground to check for surface quality. The project has enabled enhanced mobility and new modes of perception to achieve inspection tasks in challenging industrial environments.
Highlights of work performed during the project include:
* Design and release of novel THING passive adaptive feet. Inspired by the anatomy of the human foot, this unique foot present a novel way to sense contact forces, detect slippages, and conform to underlying terrains.
* Design and release of a prototype active adaptive foot. A similar articulated design to the passive adaptive foot, but with an actuated degree-of-freedom to enable grasping and pre-shaping.
* Design and release of prototypes for passive and active ankles with rigid sole. Designed to be robust to absorb shocks during locomotion while increasing contact surface area for increased traction.
* Development of a low-cost, lightweight and IP67 sealed, force-torque sensor, which has been commercialised via spinout BOTA Systems.
* Development of novel machine learning methods for terrain classification from force/torque sensing in the foot.
* Development of a variable impedance planner/controller and teleoperation system for active probing of the ground or surrounding walls for haptic sensing of external geometry.
* Development of VILENS: Visual Inertial LEgged Navigation System. Because of VILENS’ tight integration of visual features with inertial and kinematics, it provides significantly lower drift during state estimation.
* Development and release for a new physics engine Raisim, which outperforms existing simulator pipelines in terms of speed and accuracy.
* Development of a novel online semi-parametric dynamics model learning method. The algorithm combines inertial parameter adaption with online data-driven regression.
* Development of a new model-predictive control framework that incorporates inherent bandwidth limits into motion planning and a trajectory optimiser to discover stepping motions (including sliding contacts) without a predefined contact schedule.
* Development of a novel self-supervised learning method to predict foothold quality. The method allows the robot to relate haptic feedback to previous visual information of the terrain.
* the Consortium conducted multiple tests of the robot and developed technologies, in real-world subterranean environments including City of Zurich sewers, Gonzen iron mines, Nenthead coal mine, KWK Pniówek mine, Swiss Schollberg Mine
* Over 70 peer-reviewed scientific publications
* 4 new open datasets
* Commercialisation of technology developed within the project via startups ANYBotics, BOTA Systems and XStarMotion
* Design and release of novel THING passive adaptive feet. Inspired by the anatomy of the human foot, this unique foot present a novel way to sense contact forces, detect slippages, and conform to underlying terrains.
* Design and release of a prototype active adaptive foot. A similar articulated design to the passive adaptive foot, but with an actuated degree-of-freedom to enable grasping and pre-shaping.
* Design and release of prototypes for passive and active ankles with rigid sole. Designed to be robust to absorb shocks during locomotion while increasing contact surface area for increased traction.
* Development of a low-cost, lightweight and IP67 sealed, force-torque sensor, which has been commercialised via spinout BOTA Systems.
* Development of novel machine learning methods for terrain classification from force/torque sensing in the foot.
* Development of a variable impedance planner/controller and teleoperation system for active probing of the ground or surrounding walls for haptic sensing of external geometry.
* Development of VILENS: Visual Inertial LEgged Navigation System. Because of VILENS’ tight integration of visual features with inertial and kinematics, it provides significantly lower drift during state estimation.
* Development and release for a new physics engine Raisim, which outperforms existing simulator pipelines in terms of speed and accuracy.
* Development of a novel online semi-parametric dynamics model learning method. The algorithm combines inertial parameter adaption with online data-driven regression.
* Development of a new model-predictive control framework that incorporates inherent bandwidth limits into motion planning and a trajectory optimiser to discover stepping motions (including sliding contacts) without a predefined contact schedule.
* Development of a novel self-supervised learning method to predict foothold quality. The method allows the robot to relate haptic feedback to previous visual information of the terrain.
* the Consortium conducted multiple tests of the robot and developed technologies, in real-world subterranean environments including City of Zurich sewers, Gonzen iron mines, Nenthead coal mine, KWK Pniówek mine, Swiss Schollberg Mine
* Over 70 peer-reviewed scientific publications
* 4 new open datasets
* Commercialisation of technology developed within the project via startups ANYBotics, BOTA Systems and XStarMotion
By the end of the project, we have advanced the robotisation of underground inspection tasks to at least TRL 6 with a system prototype demonstration in a relevant environment. In our case, the relevant environment is not a laboratory setting, but rather real copper/iron/coal mines and sewage tunnels. The socio-economic and wider societal implications are that both industries and municipalities may reduce maintenance costs, improve human safety by reducing time spent underground by maintenance staff, and further increase the reliability and efficacy of the inspection data. We also will strengthen Europe’s position in the global marketplace through support of SMEs and commercialisation of developed technology into new markets.