Dynamics Design and Analysis of Direct-Drive Aerostatic Slideways in a Multi-Physics Simulation Environment

2013 ◽  
Vol 41 (4) ◽  
pp. 315-328 ◽  
Author(s):  
Tianjian Li ◽  
Hui Ding ◽  
Kai Cheng
Keyword(s):  
Simulation Environment ◽  
Direct Drive ◽  
Physics Simulation

Physics Simulation Environment for Swarms of Self-Assembly Autonomous Robots

2004 ◽  
Vol 37 (14) ◽  
pp. 79-84
Author(s):  
Giovanni Cosimo Pettinaro ◽  
Ivo Widjaja Kwee ◽  
Luca Maria Gambardella
Keyword(s):  
Self Assembly ◽  
Autonomous Robots ◽  
Simulation Environment ◽  
Physics Simulation

scholarly journals An In-Memory Physics Environment as a World Model for Robot Motion Planning

2020 ◽  
Author(s):  
Navin K Ipe
Keyword(s):  
Motion Planning ◽  
Computational Intelligence ◽  
Fitness Landscape ◽  
Mental Simulation ◽  
Robot Motion ◽  
Robot Motion Planning ◽  
Simulation Environment ◽  
World Model ◽  
Simulated Environment ◽  
Physics Simulation

This paper investigates the possibility of utilizing a physics simulation environment as the imagination of a robot, where it creates a replica of the detected terrain in a physics simulation environment in its memory, and “imagines” a simulated version of itself in that memory, performing actions and navigation on the terrain. The physics of the environment simulates the movement of robot parts and its interaction with the objects in the environment and the terrain, thus avoiding the need for explicitly programming many calculations. The robot chooses the best possible action from multiple simulations of movement, and executes it in the real world. Moreover, as the complexity of motion increases with each degree of freedom of the robot’s joints, this paper also explores the utility of uniform pseudo-randomness to explore the fitness landscape of robot motility, and compares it with Computational Intelligence algorithms. Such techniques could potentially simplify the algorithmic complexity of programming multi-jointed robots, and also be capable of dynamically adjusting the “mental” simulation of the robot when it encounters environments with different gravity, viscosity or traction, merely by adjusting parameters of the simulated environment.

scholarly journals An In-Memory Physics Environment as a World Model for Robot Motion Planning

2020 ◽  
Author(s):  
Navin Ipe
Keyword(s):  
Motion Planning ◽  
Robot Motion ◽  
Robot Motion Planning ◽  
Simulation Environment ◽  
World Model ◽  
Physics Simulation

This paper investigates the utilization of a physics simulation environment as the imagination of a robot, where it creates a replica of the detected terrain in a physics simulation environment in its memory, and “imagines” a simulated version of itself in that memory, performing actions and navigation on the terrain. The physics of the environment simulates the movement of robot parts and its interaction with the objects in the environment and the terrain, thus avoiding the need for explicitly programming many calculations.

scholarly journals Gaining a Sense of Touch Object Stiffness Estimation Using a Soft Gripper and Neural Networks

Electronics ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 96
Author(s):  
Michal Bednarek ◽  
Piotr Kicki ◽  
Jakub Bednarek ◽  
Krzysztof Walas
Keyword(s):  
Direct Interaction ◽  
Real Data ◽  
Parameters Estimation ◽  
Physical Parameters ◽  
Simulation Environment ◽  
Physics Simulation ◽  
Measurement Units ◽  
Sense Of Touch ◽  
Soft Manipulation ◽  
Significant Attention

Soft grippers are gaining significant attention in the manipulation of elastic objects, where it is required to handle soft and unstructured objects, which are vulnerable to deformations. The crucial problem is to estimate the physical parameters of a squeezed object to adjust the manipulation procedure, which poses a significant challenge. The research on physical parameters estimation using deep learning algorithms on measurements from direct interaction with objects using robotic grippers is scarce. In our work, we proposed a trainable system which performs the regression of an object stiffness coefficient from the signals registered during the interaction of the gripper with the object. First, using the physics simulation environment, we performed extensive experiments to validate our approach. Afterwards, we prepared a system that works in a real-world scenario with real data. Our learned system can reliably estimate the stiffness of an object, using the Yale OpenHand soft gripper, based on readings from Inertial Measurement Units (IMUs) attached to the fingers of the gripper. Additionally, during the experiments, we prepared three datasets of IMU readings gathered while squeezing the objects—two created in the simulation environment and one composed of real data. The dataset is the contribution to the community providing the way for developing and validating new approaches in the growing field of soft manipulation.

scholarly journals Optimization of Snake-like Robot Locomotion Using GA: Serpenoid Design

MENDEL ◽  
2020 ◽  
Vol 26 (1) ◽  
pp. 1-6
Author(s):  
Tomáš Hůlka ◽  
Radomil Matoušek ◽  
Ladislav Dobrovský ◽  
Monika Dosoudilová ◽  
Lars Nolle
Keyword(s):  
Fitness Function ◽  
Function Evaluation ◽  
Virtual Model ◽  
Simulation Environment ◽  
Simulation Framework ◽  
Snake Robot ◽  
Robot Locomotion ◽  
Gait Parameters ◽  
Physics Simulation ◽  
Optimal Gait

This work investigates the locomotion efficiency of snake-like robots through evolutionary optimization using the simulation framework PhysX (NVIDIA). The Genetic Algorithm (GA) is used to find the optimal forward head serpentine gait parameters, and the snake speed is taken into consideration in the optimization. A fitness function covering robot speed is based on a complex physics simulation in PhysX. A general serpenoid form is applied to each joint. Optimal gait parameters are calculated for a virtual model in a simulation environment. The fitness function evaluation uses the Simulation In the Loop (SIL) technique, where the virtual model is an approximation of a real snake-like robot. Experiments were performed using an 8-link snake robot with a given mass and a different body friction. The aim of the optimization was speed and length of the trace.

scholarly journals An In-Memory Physics Environment as a World Model for Robot Motion Planning

2020 ◽  
Author(s):  
Navin Ipe
Keyword(s):  
Motion Planning ◽  
Robot Motion ◽  
Robot Motion Planning ◽  
Simulation Environment ◽  
World Model ◽  
Physics Simulation

This paper investigates the utilization of a physics simulation environment as the imagination of a robot, where it creates a replica of the detected terrain in a physics simulation environment in its memory, and “imagines” a simulated version of itself in that memory, performing actions and navigation on the terrain. The physics of the environment simulates the movement of robot parts and its interaction with the objects in the environment and the terrain, thus avoiding the need for explicitly programming many calculations.

Virtual Experimental Investigation for Industrial Robotics in Gazebo Environment

2018 ◽  
Author(s):  
Murat Aksu ◽  
John L. Michaloski ◽  
Frederick M. Proctor
Keyword(s):  
Experimental Approach ◽  
Current System ◽  
Industrial Robots ◽  
Simulation Environment ◽  
Sensors And Actuators ◽  
Virtual Sensors ◽  
Distributed Framework ◽  
Physics Simulation ◽  
Physics Based Simulation ◽  
Automation Tools

Measuring the agility performance of the industrial robots as they are performing in unstructured and dynamic environments is a thought-provoking research topic. This paper investigates the development of industrial robotic simulation algorithms for the effective application of robots in those changing environments. The distributed framework for this investigation is the Robot Operating System (ROS) which is extensively used in robotic applications. ROS-Industrial (ROS I), which extends the capabilities of ROS to manufacturing, allows us to interoperate between industrial robots, sensors, communication buses and other kinds of automation tools. Gazebo is used as the open-source 3D simulator to design a virtual industrial robotic system, which is a prevailing tool as a node in the ROS environment. An effort is underway to replicate the in-house experimental robotic kitting lab with a graphical physics simulation that can be shared worldwide. This graphical physics simulation is not tied to a specific robotic control system. An experimental approach will be presented detailing the issues related to a physics based simulation of kitting with multiple collaborative robots, multiple tools, parts, tool changers, safety system, and sensors. In this realm, the ability for the simulation environment to encompass the current system as well as additional more complex sensors and actuators will be discussed. To make this simulation environment more realistic, Gaussian noise will be introduced to the data generated by virtual sensors. We expect that this experimental approach will be a seamless way for users to verify and validate their control systems even if they do not have a physical robot at their facilities.

Santos: A physics-based digital human simulation environment

2006 ◽  
Author(s):  
Karim Abdel-Malek ◽  
Jasbir Arora ◽  
Jingzhou Yang ◽  
Timothy Marler ◽  
Steve Beck ◽  
...  
Keyword(s):  
Simulation Environment ◽  
Digital Human ◽  
Human Simulation
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