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A key methodology for the development of autonomous robots is testing using simulated robot motion and sensing systems. An impor- tant issue when simulating teams of heterogeneous autonomous robots is performance versus accuracy. In this paper the multi-robot-simulation framework (MuRoSimF) is presented which allows the exible and trans- parent exc...
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Citations
... MuRoSimF: The Multi-Robot-Simulation-Framework [14] provides components for the simulation of a robot's motion and sensing capabilities on different levels of detail. MuRoSimF allows to test each component of the software separately by replacing all other part by ground truth data. ...
The Darmstadt Rescue Robot Team is a new team estab- lished from a PhD program funded by the German Research Foundation at TU Darmstadt. It combines expertise from Computer Science and Me- chanical Engineering. Several team members have already contributed in the past to highly successful teams in the RoboCup four-legged and hu- manoid leagues.
... Using computer accelerated graphics card for fast manipulation of 3D scenes has been addressed by robotic researchers from some yars ago, specially in the simulation domain [4,2,1], but it remains less explored in real-time localization. In real-time particle filter localization [8,3], expected environment constraints and expected observations are computed massively at each iteration from each particle position, causing the software modules in charge of such computations being a key piece for the final success of the whole system. ...
This paper provides a detailed description of a set of algorithms to efficiently manipulate 3D geometric models to compute
physical constraints and range observation models, data that is usually required in real-time mobile robotics or simulation.
Our approach uses a standard file format to describe the environment and processes the model using the openGL library, a widely-used programming interface for 3D scene manipulation. The paper also presents results on a test model for
benchmarking, and on a model of a real urban environment, where the algorithms have been effectively used for real-time localization
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... Two major approaches are usually applied to simulate the distances measured by the sensor: calculating ray-object intersections using ray-casting or rendering the scene from the sensor's point of view and subsequently reading back the depth buffer. As shown in [1,2] either method has specific advantages and drawbacks, depending on the geometry and resolution of the simulated device. Nevertheless many existing robot simulators are limited to only one of these methods, or impose limitations on the devices simulated with either method. ...
... MuRoSimF provides methods for the simulation of wheeled (e. g. [1]) and legged (e. g. [16]) locomotion as well as for different sensors of autonomous mobile robots. It allows an unrestricted combination of these simulation methods to create simulations with different levels of accuracy and abstraction. ...
... As discussed in [1,2] the view-rays have an equal distribution in the image plane. This contradicts the equal angular distribution usually found in laser range finders. ...
Distance sensors are an important class of external sensors used in many autonomous robots. Thus it is of importance to provide proper simulation for these sensors to enable software-in-the-loop testing of a robot’s control software. Two different methods for distance calculation are commonly applied for the simulation of such sensors, namely reading back the depth buffer from 3D renderings and the calculation of ray-object-intersections. Many simulators impose restrictions on either method, none of the widely used robot simulators reviewed in this paper currently considers material dependent simulation of the distances measured.
In this paper extended versions of both methods are presented which provide additional information on the object perceived by distance sensors. Several methods for incorporating distance- and object-information into a complete distance-sensor simulation-module are discussed. Implementations of either method are compared for their performance depending on the sensor resolution on different computer systems. These measurements show, that the break even of the methods strongly depends on the hardware, thus stressing the importance of providing either method in a robot simulation in a transparent way in order to obtain optimal performance of the simulation.
... The simulator offers a custom-developed physics engine and 3D visualization. MuRoSimF is a special simulation environment for robot teams, but with flexible level of detail [20]. The simulation algorithms can be distributed over different CPUs. ...
Simulation is well established in robotics and nearly every robot project benefits from it. Thereby the understanding on what in-sights a simulation shall bring or how it shall assist the development is totally different in every project. The main reason is that simulation cov-ers all aspects in the robot development process as for example learning and tuning control parameters or testing the composition of algorithms in a robot architecture. Further, the broad application field led to a large number of different simulation environments where, depending on the application, some are designed to simulate special purpose hardware and others aim to target a wide range of robotic systems. In this paper we present an overview on different use cases for simulation in robotics. Dependent on the use cases we propose a categorization for robot simu-lators/emulators and present some concrete environments.
The term “Swarm intelligence” outlines a broad scope, which is generally defined as the collective behaviour of many individuals towards a certain task, each operating autonomously in a decentralised manner. Swarms are inspired by the type of biological behaviours of animals and this technology involves decentralised local control and communication, which ensures the problem-solving process is more efficient through modification and infusion into swarm robotic technology. One such application is Mixed Reality, which links both the real and virtual environments through Augmented Reality (AR) and Augmented Virtuality (AV). Enabling a robot to sense both physical and virtual environments via augmented means allows for the ability to interact with both environments. Swarm robotics related experiments and applications are relatively expensive compared to other types of robotics experiments. The most common solution is to use computer-based virtual simulations. However, as it lacks the real-world guarantee of execution, this study introduces a hybrid solution that can simultaneously execute swarm behavioural experiments on actual robots and virtually deployed robots, which can be used to test the functionalities of swarm robots with a large number of different environmental arrangements easier than physically creating them. As identifying the necessary requirements for implementing a mixed reality environment for swarm robotics simulation was this study’s main focus, the basic interaction models were designed to conduct experiments with both physical and virtual robots in near real-time in a mixed reality environment. In addition, an open-source, distributed, and modular mixed reality simulation framework was implemented with support libraries and applications.
To enable the systematic evaluation of complex technical systems by engineers from various disciplines advanced 3D simulation environments that model all relevant aspects are used. In these Virtual Testbeds real-time capabilities are very hard to achieve without applying multi-threading strategies, due to the high complexity of the simulation. We present a novel simulation architecture that facilitates a modular approach to perform parallel simulations of arbitrary environments, where no explicit knowledge of the underlying simulation algorithms or model partitioning is needed by the engineer. Simulation components such as rigid-body dynamics, kinematics, renderer, controllers etc. can be distributed among different threads without concern about the specific technical realization. We achieve this by managing self-contained independent copies of the simulation model, each bound to one thread.
Industrial robots are used in a great variety of applications for handling, welding, assembling and milling operations. Especially for machining operations, industrial robots represent a cost-saving and flexible alternative compared to standard machine tools. Reduced pose and path accuracy, especially under process force load due to the high mechanical compliance, restrict the use of industrial robots for machining applications with high accuracy requirements. In this chapter, a method is presented to predict and compensate path deviation of robots resulting from process forces. A process force simulation based on a material removal calculation is presented. Furthermore, a rigid multi-body dynamic system’s model of the robot is extended by joint elasticities and tilting effects, which are modeled by spring-damper-models at actuated and additional virtual axes. By coupling the removal simulation with the robot model the interaction of the milling process with the robot structure can be analyzed by evaluating the path deviation and surface structure. With the knowledge of interaction along the milling path a general model-based path correction strategy is introduced to significantly improve accuracy in milling operations.
In this paper a new class library for the computation of the forward multi-body-system (MBS) dynamics of robots and biomechanical models of human motion is presented. By the developed modular modeling approach the library can be flexibly extended by specific modeling elements like joints with specific geometry or different muscle models and thus can be applied efficiently for a number of dynamic simulation and optimization problems. The library not only provides several methods for solving the forward dynamics problem (like articulated body or composite rigid body algorithms) which can transparently be exchanged. Moreover, the numerical solution of optimal control problems, like in the forward dynamics optimization of human motion, is significantly facilitated by the computation of the sensitivity matrix with respect to the control variables. Examples are given to demonstrate the efficiency of the approach. (© 2012 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)
In the course of the demographic change companies will have to tackle the challenges of an ageing workforce. Since older employees, especially blue-collar workers are more likely to show an attrition of their working ability, manufacturers need to take preventive and compensative measures: first to maintain health and well being of their employees, second to integrate those with already reduced working ability and third to further increase productivity. On workplace level the concept of a production assistance robot offers a promising approach, combining support and relief for the worker with the benefits of automation directly in the value added chain. This paper summarizes several approaches, where adding simple low level intelligence to industrial robots results in economically and ergonomically effective assistance functions.
Using robotic simulators, researchers are able to improve the algorithms of their robotics platforms before testing them in real environments. In fact, the safe environment provided by simulation is important, especially for robots that are constantly in contact with human beings, such as assistive robots and intelligent wheelchairs. Here, we propose to take advantage of an available general robotics simulator to model the IntellWheel's intelligent wheelchair prototype and its environment, enabling patient's drills and creating a test bed to refine and to experiment new methodologies of autonomous navigation, obstacle avoidance and human-machine interaction. As a result of the evaluation of four general simulators, the USARSim simulator has demonstrated to be more suitable to serve as basis for the IntellWheels simulation prototype. The development of a rough model of the intelligent wheelchair and of an appropriate test environment proved that with some modifications USARSim is able to provide a very realistic simulation platform for Intelligent Wheelchairs.
