![]() In order to show the results of the algorithm better, sometimes the navigation algorithm is simulated jointly by using programming language such as MATLAB and dynamic simulation software such as ADAMS, RecurDyn. Using this simulation method, usually only two-dimensional simulation results can be displayed, and the display effect is not good. Five different algorithms, including the Dijkstra algorithm and A* algorithm, were implemented to determine the shortest path for a mobile robot between nodes within various mazes using the simulator. In the study presented in, a path finding simulator for the Pioneer 3DX mobile robot was designed with GUI (Graphical User Interface) in MATLAB. The motion of the robot that moves from the initial position to the desired position following an estimated trajectory was shown in the simulation. In the study presented in, an algorithm for path planning to a target for a mobile robot in an unknown environment was implemented in Borland C++ afterwards, it was tested with Visual Basic and DELPHI programming language. Previously, the algorithm simulation of robots was usually developed using C++, Java, MATLAB and other programing languages. So, algorithm simulations have been widely used in navigation algorithm research. The use of modeling and simulations to develop navigation algorithms provides development flexibility and the capability to conduct extensive testing of the algorithm under a variety of operational environments and robot configurations. The navigation algorithm is the key technology for the autonomous navigation of robots, and it is also a research hotspot in the field of AMR. Research on navigation algorithms is necessary to improve automatic mobile robots in all fields. Robot navigation refers to the robot’s ability to determine its own position in the environment and then to plan a path towards its goal positions based on its knowledge about the environment and sensor values so as to reach its goal positions as efficiently and reliably as possible. It can be defined as the combination of the four fundamental competences: perception, localization, path planning, map building, and interpretation. Navigation is one of the most challenging competencies required of an autonomous mobile robot (AMR). The simulation and test results validate the algorithm simulation method based on the redevelopment of Unit圓d, showing that it is feasible, efficient, and flexible. Finally, the navigation tests of the physical robot are carried out in the physical environment, and the test trajectory is compared with the simulation trajectory. Then, the navigation is simulated in static and dynamic environments using a virtual prototype. Thirdly, taking the Mecanum wheel mobile robot as an example, the 3D robot model is imported into Unit圓D, and the virtual joint, sensor, and navigation algorithm scripts are added to the model. Secondly, the A* algorithm is improved for navigation in unknown 3D space. Firstly, the scripts of the virtual revolute joint, virtual LiDAR sensors, and terrain environment are written. With this method, a virtual robot prototype can be created quickly with the imported 3D robot model, virtual joints, and virtual sensors, and then the navigation simulation can be carried out using the virtual prototype with the algorithm script in the virtual environment. ![]() In order to implement real-time, three-dimensional, and visual navigation algorithm simulation, a method of algorithm simulation based on secondary development of Unit圓D is proposed. Computer simulation is an effective means for the research of robot navigation algorithms.
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