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Robotic simulators are normally used in the design and testing of control algorithms for different platforms. But they can also be seen in a more broad context of Artificial Creatures. This a natural development of the cognitive paradigm proposed by Brooks. Related to this, in recent years the Robot Operating System (ROS) has emerged as a de facto...
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... The findings provide insights into optimizing somewhat autonomous wheelchair design, which will assist in the development of more accurate and dependable control systems for improved mobility and safety. Jiale Huang [9] Using Coppeliasim, the research offers a motion strategy and tracking control approach for Unmanned Grounded Vehicles (UGV) negotiating uneven terrain. The study focuses on improving UGV performance in difficult situations. ...
This paper focuses on validating the design of a Load cell by investigating its accuracy to check weight. Coppeliasim, the simulation software, is used in these projects because it is easy to access the resources and integrate the hardware and software. The load cell holds significant importance in robotics applications, particularly in scenarios like calculating the weight of objects lifted by robotic arms. This paper highlights the pivotal role of load cells in robotics by presenting a detailed guide on integrating a physical load cell with a dynamic simulation environment (CoppeliaSim) using Arduino. The Coppeliasim model in Lua language for a load cell allows initialisation, actuation, and UI functions to be executed sequentially for the optimisation of control and operation. The accuracy assessment of the load cell model, constructed through the outlined steps in this paper, revealed a precision of 99.514% for individual loads and 98.023% for cumulative load scenarios.
... Two of the simulators in the survey were selected according to the results announced. These authors found that Gazebo is the best-known simulator and CoppeliaSim is the best-rated simulator [4]. ...
Real-time applications of autonomous systems are simulated in the computer environment to ensure that they operate error-free or with minimal errors. Coppeliasim, used in this field, is a platform where several sample models, robots, sensors and actuators are used together, a virtual world is created and interacted with it throughout the working period. Having a comprehensive toolbox, autonomous vehicle training and virtual reality, Coppeliasim's compatibility with Solidworks, a very useful design program for drawing, seems to be a great advantage. Due to these features, CoppeliaSim is very important in predicting and solving problems that may arise in many different applications. The propeller movement of the drone, which we designed with the Solidworks 2020 program and transferred to the Coppeliasim platform using the URDF exporter method, was carried out with the Coppeliasim simulator. In our work, Coppeliasim is synchronized with the simulator and MATLAB API codes. While the drone propellers were working on the Coppeliasim platform, angular speed and timing controls were made using the codes we prepared in the MATLAB program. Additionally, this work shows that drones or different autonomous systems can be controlled and designed before real-time operation using the Coppeliasim simulator and the MATLAB program.
... The primary objective of this article is to help mobile roboticists to select a suitable simulator for their application and development needs. Four popular robot simulators, namely CoppeliaSim (formerly called V-REP) [9], Gazebo [10], MORSE [11] and Webots [12], shown in Figure 1, were selected because they are widely and actively used in robotics community [2,4,13,14]. Clearly, without quantitative comparisons, it proves difficult to select the best one for a given project. ...
... The real time factor is a commonly used metric to measure the computational load of a simulator [13]. The real time factor was calculated by taking the ratio of the sum of the simulated time step to the sum of the desired real time step. ...
The number of available tools for dynamic simulation of robots has been growing rapidly in recent years. However, to the best of our knowledge, there are very few reported quantitative comparisons of the most widely-used robot simulation tools. This article attempts to partly fill this gap by providing quantitative and objective comparisons of four widely-used simulation packages for mobile robots. The comparisons reported here were conducted by obtaining data from a real Husky A200 mobile robot driving on mixed terrains as ground truth and by simulating a 3D mobile robot model in a developed identical simulation world of these terrains for each simulator. We then compared the simulation outputs with real, measured results by weighted metrics. Based on our experiments and selected metrics, we conclude that CoppeliaSim is currently the best performing simulator, although Gazebo is not far behind and is a good alternative.
... However they neither recorded the resources usage nor did they try a different simulator for the real world task. Their work was then extended by Nogueira [14] who compared 2 simulators and their integration with ROS, the ease of building the world and the CPU usage. ...
... Pitonakova et al. [15] adopted the methodology in [14]. They compared three simulators and then ran extensive tests to record each simulator performance on tasks involving multiple robotic arms. ...
... We decided to include it in our research as it has been compared in previous simulation software reviews, e.g. [14], [15], [18], [16], [19]. ...
Simulation software is a powerful tool for robotics research, allowing the virtual representation of the real world. However with the rise of the Robot Operating System (ROS), there are new simulation software packages that have not been compared within the literature. This paper proposes a systematic review of simulation software that are compatible with ROS version 2. The focus is research in robotics arm manipulation as it represents the most often used robotic application in industry and their future applicability to digital twins. For this, we thus benchmark simulation software under similar parameters, tasks and scenarios, and evaluate them in terms of their capability for long-term operations, success at completing a task, repeatability and resource usage. We find that there is no best simulation software overall, but two simulation packages (Ignition and Webots) have higher stability than other while, in terms of resources usage, PyBullet and Coppeliasim consume less than their competitors.
... In addition to preparing software for real world experiments, a simulator was used for more precise measurement experiments and simulating curved surfaces for detection. Gazebo, a robotics simulation engine [24], was the primary and most obvious choice due to its capability to: integrate with ROS [25], simulate the selected cameras and the kinematic arm, and execute movement commands from MATLAB. ...
This work proposes a novel solution for detecting and tracing spatially varying edges of large manufacturing workpieces, using a consumer grade RGB depth camera, with only a partial view of the workpiece and without prior knowledge. The proposed system can visually detect and trace various edges, with a wide array of degrees, to an accuracy of 15 mm or less, without the need for any previous information, setup or planning. A combination of physical experiments on the setup and more complex simulated experiments were conducted. The effectiveness of the system is demonstrated via simulated and physical experiments carried out on both acute and obtuse edges, as well as typical aerospace structures, made from a variety of materials, with dimensions ranging from 400 mm to 600 mm. Simulated results show that, with artificial noise added, the solution presented can detect aerospace structures to an accuracy of 40 mm or less, depending on the amount of noise present, while physical aerospace inspired structures can be traced with a consistent accuracy of 5 mm regardless of the cardinal direction. Compared to current industrial solutions, the lack of required planning and robustness of edge detection means it should be able to complete tasks more quickly and easily than the current standard, with a lower financial and computational cost than the current techniques being used within.
... Both Gazebo and V-REP have a 3D dynamic multi-robot environment [32]. In addition, the V-REP simulator seems to be more user friendly than Gazebo, whereas Gazebo is more hardware-demanding than V-REP [33]. ...
Educational robotics (ER) seems to have a positive effect on students and, in many cases, might help them to successfully assimilate knowledge and skills. Thus, this paper focuses on ER and carries out a literature review on educational robotics simulators with Graphical User Interfaces (GUIs). The review searches for relevant papers which were published in the period 2013–2020 and extracted the characteristics of the simulators used. The simulators that we describe in this article cover various robotic technologies, offering students an easy way to engage with virtual robots and robotics mechanisms, such as wheeled robots or drones. Using these simulators, students might cover their educational needs or prepare themselves for educational robotic competitions by working in as realistic as possible conditions without hardware restrictions. In many cases, simulators might reduce the required cost to obtain a robotic system and increase availability. Focusing on educational robotics simulators, this paper presents seventeen simulators emphasizing key features such as: user’s age, robot’s type and programming language, development platform, capabilities, and scope of the simulator.
... In order for autonomous flight path finding to be practical, algorithms and technologies must be verified through simulation in a realistic environment. Thus far, many robot simulators have been developed such as Gazebo and Vrep with which we can simulate the physical movement of UAVs [33][34][35]. Gazebo [33] is a widely tool used for the development of robots with various physical characteristics, such as conveyor belts, unmanned probes, and line tracers. This tool can incorporate various sensor modules for these robots, and thus we can make a UAV equipped with Wi-Fi signal detection sensor or a camera sensor. ...
... Thus far, many robot simulators [33][34][35] and simple machine learning test tools [43] have been developed. However, with solely using the existing robot simulators or simplified machine learning visualizers, we can test only robot motion operations or machine-learning algorithms but not consider both.To overcome this limitation, this paper proposes a holistic UAV simulation platform by integrating robot simulators and ML-based path planning algorithms. ...
Recently, as UAVs (unmanned aerial vehicles) have become smaller and higher-performance, they play a very important role in the Internet of Things (IoT). Especially, UAVs are currently used not only in military fields but also in various private sectors such as IT, agriculture, logistics, construction, etc. The range is further expected to increase. Drone-related techniques need to evolve along with this change. In particular, there is a need for the development of an autonomous system in which a drone can determine and accomplish its mission even in the absence of remote control from a GCS (Ground Control Station). Responding to such requirements, there have been various studies and algorithms developed for autonomous flight systems. Especially, many ML-based (Machine-Learning-based) methods have been proposed for autonomous path finding. Unlike other studies, the proposed mechanism could enable autonomous drone path finding over a large target area without size limitations, one of the challenges of ML-based autonomous flight or driving in the real world. Specifically, we devised Multi-Layer HVIN (Hierarchical VIN) methods that increase the area applicable to autonomous flight by overlaying multiple layers. To further improve this, we developed Fisheye HVIN, which applied an adaptive map compression ratio according to the drone’s location. We also built an autonomous flight training and verification platform. Through the proposed simulation platform, it is possible to train ML-based path planning algorithms in a realistic environment that takes into account the physical characteristics of UAV movements.
... These include: the operating system (Ubuntu), the robotspecific operating system (ROS) [15], [16], but also the development tools needed to develop and simulate a robot (Gazebo, RViz) [17], [18]. ...
... However, the benefit of using V-REP versus Gazebo is that the former benefits from built-in inverse kinematic which makes it possible to perform the simulations in a timely manner. A comparative study has been performed previously in [29] which compared V-REP software and Gazebo. It is concluded that V-REP is a more user-friendly and intuitive software than Gazebo in which intelligent optimization can be carried out with more comfort. ...
Industry 4.0 is the fourth generation of industry which will theoretically revolutionize manufacturing methods through the integration of machine learning and artificial intelligence approaches on the factory floor to obtain robustness and speed-up process changes. In particular, the use of the digital twin in a manufacturing environment makes it possible to test such approaches in a timely manner using a realistic 3D environment that limits incurring safety issues and danger of damage to resources. To obtain superior performance in an Industry 4.0 setup, a modified version of a binary gravitational search algorithm is introduced which benefits from an exclusive or (XOR) operator and a repository to improve the exploration property of the algorithm. Mathematical analysis of the proposed optimization approach is performed which resulted in two theorems which show that the proposed modification to the velocity vector can direct particles to the best particles. The use of repository in this algorithm provides a guideline to direct the particles to the best solutions more rapidly. The proposed algorithm is evaluated on some benchmark optimization problems covering a diverse range of functions including unimodal and multimodal as well as those which suffer from multiple local minima. The proposed algorithm is compared against several existing binary optimization algorithms including existing versions of a binary gravitational search algorithm, improved binary optimization, binary particle swarm optimization, binary grey wolf optimization and binary dragonfly optimization. To show that the proposed approach is an effective method to deal with real world binary optimization problems raised in an Industry 4.0 environment, it is then applied to optimize the assembly task of an industrial robot assembling an industrial calculator. The optimal movements obtained are then implemented on a real robot. Furthermore, the digital twin of a universal robot is developed, and its path planning is done in the presence of obstacles using the proposed optimization algorithm. The obtained path is then inspected by human expert and validated. It is shown that the proposed approach can effectively solve such optimization problems which arises in Industry 4.0 environment.
... A further and more thorough comparison can be found in [23,24] where a summary and evaluation of both simulators is given. ...
The deployment of robot controllers into the real robotic platform is cumbersome and time consuming, especially when testing scenarios involve several robots or are sites not easily accessible. Besides this, most of the time, testing on the real platforms or real conditions provides little value in the early stages of controller design and prototype, phases where debugging and suitability of the controller are the main objectives. This paper proposes a simulation strategy for developing and testing controllers for Unmanned Aerial and Surface Vehicle coordination and interaction with the environment. The simulation strategy is based on V-REP and Matlab/Simulink which provide a large set of features, modularity and compatibility across platforms. Results show that this approach significantly reduces development and delivery times by providing an off-the-shelf simulation environment and a step-by-step implementation guidelines. The source code to deploy the simulations is available in an open-source repository.
