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CoppeliaSim (formerly V-REP) is a powerful cross-platform 3D simulator based on a distributed control architecture: control programs (or scripts) can be directly attached to scene objects and run simultaneously in a threaded or non-threaded fashion. It features advanced physics engines that simulate real-world physics and object interactions such as collisions and object dynamics.
CoppeliaSim control methods CoppeliaSim Python remote API documentation CoppeliaSim C++ remote API documentation
Hexapod model
Hexapod servos numbering:
Central Pattern Generator (CPG) is a biologically inspired neural network that produces rhythmic patterned outputs 1). CPGs are composed of individual neurons connected by mutual inhibition. The most used model and structure of the CPG is a Matsuoka oscillator 2). The main problem is parameter tweaking of individual connections to achieve limit cycles in the neural network 3)
We will use a Matsuoka oscillator formed by four neurons in mutual inhibition.
The CPGs are connected in a network where each leg is driven by one CPG 4). The purpose of the CPG network is to synchronize the oscillations of the connected CPGs to produce a motion gait. The output of each CPG is shaped to produce signals that drive individual joint actuators.
Typical output of the CPG network and the transition between different gaits.
Translation of the CPG output on the actuators can be done directly 1), or using the post-processing of the signal and inverse kinematics 4) 5) to calculate the foot-tip trajectories. We use the direct approach to map the CPG output to the joint angles in our work. The output shaping blocks are fed with the control signal, which determines the speed of the robot legs on the right and left sides, respectively, which enables the robot to have differential steering.
In intelligent robotics, the vital task for the robot is navigation. Hence, the robot has to be aware of its position with respect to the goal and then find a suitable way to achieve it. In our course, we are interested mostly in artificial intelligence and planning; hence, localization is provided in 6 Degrees Of Freedom (DOF) in global coordinates externally.
The open-loop control considers only the current position $(x,y)$ and orientation $\phi$ of the robot, which means that the current state of the robot is described as triplet $(x,y,\phi)$. The goal position is defined by its position $(x_{goal}, y_{goal})$.
Note that the real robot can never reach the precise goal position; hence, a small neighborhood of the goal position with a diameter of usually half size of the robot is considered suitable for navigation.