Sigel is a 3D-simulation software that educates virtual robots on how to walk. The tool allows easy access to learn the mechanics of leg movement, thus offering an interactive platform to its users.
Sigel is an innovative software tool that enables virtual robots to learn how to walk in a 3D simulation environment. Utilizing Genetic Programming, it develops control programs for different robot architectures and visualizes them using the widely-used graphics library, OpenGL. Furthermore, Sigel can easily export animations to Povray, enhancing the user experience.
The primary issue that Sigel addresses is the programming of control programs for various walking robots architectures. The solution to this problem employed the Genetic Programming method, which mimics natural evolution and automatic programming concepts, to evolve ideal control programs for each robot architecture.
Sigel features an advanced Genetic Programming system that functions as a search procedure. It effectively searches for optimal solutions by organizing a set of possible solutions, known as individuals, using an administration called Pool. To select the best solutions while eliminating wrong ones, the system implements various natural strategies such as selection.
To test the efficiency of the control program, one needs to observe the behavior of the walking robot on its respective architecture. However, this is quite dangerous for the robot as control failures can damage the robot's architecture extensively. As a result, Sigel also comes with a kinetic simulator that can simulate any kind of robot architecture with exceptional accuracy. Furthermore, a robot description language and a visualization interface were developed to describe the architecture of any robot and simulate the physical world precisely.
Sigel's primary advantage is its ability to manage multiple walking robot architectures using the robot description language and its automatic programming mechanism. With this capability, the control program can be evolved after any architecture modifications without the risk of causing extensive damage. The simulation visualization feature allows users to check the outcome of the evolution; the best-case scenario mimics the robot's behavior in reality with negligible differences.
Sigel's set of functionalities is extensive, allowing users to load and save robots, modify robot language parameters and GP parameters, adjust simulation parameters and environment settings, and generate random individuals.
In conclusion, Sigel is a revolutionary software package that utilizes Genetic Programming to teach robots how to walk on any architecture safely. It comes with functionalities that ensure its efficiency, accuracy, and robustness, and users can adjust the parameters to achieve the best results. The software package requires gcc 2.95.2 or higher and the relevant libstdc++ version, along with OpenGL.
The primary issue that Sigel addresses is the programming of control programs for various walking robots architectures. The solution to this problem employed the Genetic Programming method, which mimics natural evolution and automatic programming concepts, to evolve ideal control programs for each robot architecture.
Sigel features an advanced Genetic Programming system that functions as a search procedure. It effectively searches for optimal solutions by organizing a set of possible solutions, known as individuals, using an administration called Pool. To select the best solutions while eliminating wrong ones, the system implements various natural strategies such as selection.
To test the efficiency of the control program, one needs to observe the behavior of the walking robot on its respective architecture. However, this is quite dangerous for the robot as control failures can damage the robot's architecture extensively. As a result, Sigel also comes with a kinetic simulator that can simulate any kind of robot architecture with exceptional accuracy. Furthermore, a robot description language and a visualization interface were developed to describe the architecture of any robot and simulate the physical world precisely.
Sigel's primary advantage is its ability to manage multiple walking robot architectures using the robot description language and its automatic programming mechanism. With this capability, the control program can be evolved after any architecture modifications without the risk of causing extensive damage. The simulation visualization feature allows users to check the outcome of the evolution; the best-case scenario mimics the robot's behavior in reality with negligible differences.
Sigel's set of functionalities is extensive, allowing users to load and save robots, modify robot language parameters and GP parameters, adjust simulation parameters and environment settings, and generate random individuals.
In conclusion, Sigel is a revolutionary software package that utilizes Genetic Programming to teach robots how to walk on any architecture safely. It comes with functionalities that ensure its efficiency, accuracy, and robustness, and users can adjust the parameters to achieve the best results. The software package requires gcc 2.95.2 or higher and the relevant libstdc++ version, along with OpenGL.
What's New
Version 1.0: N/A