Renato Gasoto, Miles Macklin, Xuan Liu, Yinan Sun, Kenny Erleben, Cagdas Onal1, and Jie Fu
In this work we present a framework that is capable of accurately representing soft robotic actuators in a multiphysics environment in real-time. We propose a constraint-based dynamics model of a 1-dimensional pneumatic soft actuator that accounts for internal pressure forces, as well as the effect of actuator latency and damping under inflation and deflation and demonstrate its accuracy a full soft robotic snake with the composition of multiple 1D actuators. We verify our model’s accuracy in static deformation and dynamic locomotion open-loop control experiments. To achieve real-time performance we leverage the parallel computation power of GPUs to allow interactive control and feedback.
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Open Source Software for General Robust Interface Tracking by H2020 MSCA-ITN RAINBOW
Author Fredrik Holsten, Morten Pol Engell-Nørregård, Sune Darkner, and Kenny Erleben
Soft robots are advantageous in terms of flexibility, safety and adaptability. It is challenging to find efficient computational approaches for planning and controlling their motion. This work takes a direct data-driven approach to learn the kinematics of the three-dimensional shape of a soft robot, by using visual markers. No prior information about the robot at hand is required. The model is oblivious to the design of the robot and type of actuation system. This allows adaptation to erroneous manufacturing. We present a highly versatile and inexpensive learning cube environment for collecting and analysing data. We prove that using multiple, lower order models of data opposed to one global, higher order model, will reduce the required data quantity, time complexity and memory complexity significantly without compromising accuracy. Further, our approach allows for embarrassingly parallelism. Yielding an overall much more simple and efficient approach.
ICRA 2019 Authors copy
AuthorKeno Dreßel, Kenny Erleben, Paul Kry and Sheldon Andrews
Automated acquisition of friction data is an interesting approach to more successfully bridge the reality gap in simulation than conventional mathematical models. To advance this area of research, we present a novel inexpensive computer vision platform as a solution for collecting and processing friction data, and we make available the open source software and data sets collected with our vision robotic approach. This paper is focused on gathering data on anisotropic static friction behavior as this is ideal for inexpensive vision approach we propose. The data set and experimental setup provide a solid foundation for a wider robotics simulation community to conduct their own experiments.
CRV 2019Authors copy
Kenny Erleben and Sheldon Andrews
Inverse kinematics (IK) is a central component of systems for motion capture, character animation, robotics motion planning and control. The field of computer graphics has developed fast stationary point methods, such as the Jacobian Transpose method and cyclic coordinate descent. Most of the work that uses Newton’s method and its variants avoids directly computing the Hessian, and instead approximations are sought, such as in the BFGS class of solvers.
In this work, we present a numerical method for computing the exact Hessian of an IK system with prismatic, revolute, and spherical joints. For the latter, formulations are presented for joints parameterized by Euler angles which can be represented for instance by using quaternions. Our method is applicable to human skeletons in computer animation applications and some, but not all, robots. Our results show that using exact Hessians can give performance advantages and higher accuracy compared to standard numerical methods used for solving IK problems. Furthermore, we provide code that allows other researchers to plug-in exact Hessians in their own work with little effort.
Computation of contact points is a critical sub-component of physics-based animation. The success and correctness of simulation results are very sensitive to the quality of the contact points. Hence, quality plays a critical role when comparing methods, and this is highly relevant for simulating objects with sharp edges. The importance of contact point quality is largely overlooked and lacks rigor and as such may become a bottleneck in moving the research field forward.
We establish a taxonomy of contact point generation methods and lay down an analysis of what normal contact quality implies. The analysis enables us to establish a novel methodology for assessing and studying quality for mesh-based shapes. The core idea is based on a test suite of three complex cases and a small portfolio of simple cases. We apply our methodology to eight local contact point generation methods and conclude that the selected local methods are unable to provide correct information in all cases. The immediate benefit of the proposed methodology is a foundation for others to evaluate and select the best local method for their specific application. In the longer perspective, the presented work suggests future research focusing on semi-local methods.
ACM TOG paper
Best Poster Prize: Authors
Fredrik Holsten, Sune Darkner, Morten P. Engell-Nørregård, and Kenny Erleben
Soft robots are attractive because they have the potential of being safer, faster and cheaper than traditional rigid robots. If we can predict the shape of a soft robot for a given set of control parameters, then we can solve the inverse problem: to find an optimal set of control parameters for a given shape. This work takes a data-driven approach to create multiple local inverse models. This has two benefits: (1) We overcome the reality gap and (2) we gain performance and naive parallelism from using local models. Furthermore, we empirically prove that our approach outperforms a higher order global model.
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