The aim of this chapter is to present a systematic review of robotic simulation environments, while also presenting their evolution over the years. The demand to simulate such complex robotic systems and the need to collect diverse, large-scale, and realistic data to train and test deep learning algorithms before being deployed on real-world scenarios, pushed modern simulation environments to even higher standards. For example, two sliders and one hinge can be used to model a body moving in a plane. Instead simply define all the primitive joints that form the desired composite joint within the same body. Custom nodes can be flexibly defined though PROTO files. Webots allows the creation of new nodes, or the extension of existing ones.
Some basic nodes are the Solid, Device, Robot, Joint, and Motor nodes. The scene itself is defined as World through a. The gradual improvement on the performance of robot perception, cognition, and decision-making algorithms had a great impact on the way that robotic systems understand and interact with their environments, enabling them to even operate autonomously. A body in MJCF can contain multiple joints, thus there is no need to introduce dummy bodies for creating composite joints. A simulation scene in Webots follows an hierarchical structure composed of nodes. Both the progress of computer hardware technologies and the advances in the computer graphics domain, enabled the development of visually realistic and physically accurate simulators.
In the early years, robotic simulators were restricted to 2D environments, without being fully able to simulate the complexity of the real world. Simulation environments have always been a vital part of robotics research, providing tools for modeling and testing novel concepts and algorithms.