1,720,971 research outputs found
A GPU-based implementation of a cone convex complementarity approach for simulating rigid body dynamics with frictional contact.
No full textLarge-Scale Parallel Multibody Dynamics with Frictional Contact on the Graphical Processing Unit
No full textArgonne national Laboratories Report ANL/MCS-P1494-050
A soft soil contact model with adaptive level of detail for predicting off-road vehicle mobility
No full textThis work presents a model for soft soil that can interact with wheeled or tracked vehicles in a multibody simulation framework. In sake of high performance, the soil is represented by arbitrary triangular meshes, where the level of detail of the mesh is automatically increased as tire lugs or track shoes come into contact
Deformable soil with adaptive level of detail for tracked and wheeled vehicles
No full textThis paper describes a model for deformable soil based on triangular meshes with vertical deformation. Such soil model can be used in a multi-body simulation environment to study the performance of wheeled or tracked vehicles. The formulation is inspired by the soil contact model (SCM), but unlike the original idea, our implementation uses triangular meshes with arbitrary topology. We leverage on this representation of the soil in order to provide a system that automatically refines the level of detail (LOD) of the mesh when tyres or track shoes sink into the soil; this allows the use of coarse meshes as initial approximations of large areas, for the sake of faster performance and low overhead on computer memory. A general-purpose collision detection algorithm is used to detect the shape of contacting objects, hence allowing the use of generic geometries to represent lugs, tyre threads, or even track shoes or robotic legs. The method has also been tested with deformable tyres
End-to-end learning for off-road terrain navigation using the Chrono open-source simulation platform
Full text linkThis contribution (i) describes an open-source, physics-based simulation infrastructure that can be used to learn and test control policies in off-road navigation; and (ii) demonstrates the use of the simulation platform in an end-to-end learning exercise that relies on simulated sensor data fusion (camera, GPS and IMU). For (i), the 0.5 million lines of open-source code support vehicle dynamics (wheeled/tracked vehicles, rovers), deformable & non-deformable terrains, and virtual sensing. The library has a Python API for interfacing with existing Machine Learning frameworks. For (ii) , we use a Gator off-road vehicle to demonstrate how a policy learned on non-deformable terrain performs when used in hilly conditions while navigating around a course of randomly placed obstacles on deformable terrain. The hilly terrain covers an 80×80 m patch and the soil can be controlled by the user to assume various behavior, e.g. non-deformable, deformable hard (silt-like), deformable soft (snow-like), etc. To the best of our knowledge, there is no other open-source, physics-based engine that can be used to simulate off-road mobility of autonomous agents operating on deformable terrains. The results reported herein can be reproduced with models and data available in a public repository (UW-Madison Simulation Based Engineering Laboratory, Supporting models, scripts, data, https://go.wisc.edu/arflqq, 2021). Animations associated with the tests run are available online (UW-Madison Simulation Based Engineering Laboratory, Supporting simulations, https://go.wisc.edu/256xb9, 2021)
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Enabling Artificial Intelligence Studies in Off-Road Mobility Through Physics-Based Simulation of Multiagent Scenarios
No full textWe describe a simulation environment that enables the design and testing of control policies for off-road mobility of autonomous agents. The environment is demonstrated in conjunction with the training and assessment of a reinforcement learning policy that uses sensor fusion and interagent communication to enable the movement of mixed convoys of human-driven and autonomous vehicles. Policies learned on rigid terrain are shown to transfer to hard (silt-like) and soft (snow-like) deformable terrains. The environment described performs the following: multivehicle multibody dynamics cosimulation in a time/space-coherent infrastructure that relies on the Message Passing Interface standard for low-latency parallel computing; sensor simulation (e.g., camera, GPU, IMU); simulation of a virtual world that can be altered by the agents present in the simulation; training that uses reinforcement learning to "teach" the autonomous vehicles to drive in an obstacle-riddled course. The software stack described is open source. Relevant movies: Project Chrono. Off-road AV simulations, 20202
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