Lab01 - Introduction to V-REP and Robot Locomotion Control

Motivations and Goals
Become familiar with the V-REP robotic simulator
Be able to control hexapod walking robot
Tasks (teacher)
Familiarize with a simple “nature inspired” robot locomotion to control a hexapod walking robot
Create a set of “motion primitives” to abstract the robot motion control (2 Points)
Lab resources
Lab scripts: lab01 resource files
V-REP scenes: simple_plain.ttt
V-REP remoteAPI: hexapod_vrep

Robotic Simulator V-REP

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 which allows to simulate real-world physics and object interactions (collisions, object dynamics, etc.).

V-REP control methods
V-REP Python remote API documentation
V-REP C++ remote API documentation

V-REP Python remote API tutorial

Hexapod model for V-REP

Hexapod model

Hexapod servos numbering:

Robot Locomotion Using Central Pattern Generator

Central Pattern Generator (CPG) is a biologically inspired neural network that produce rhythmic patterned outputs ¹⁾. CPGs are composed from 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⁾.

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. In our work we are using the direct approach to map the CPG output to the joint angles.

Task 1
Inspect the provided CPG-based locomotion controller and play with the parameter settings of the CPG to obtain different gaits and gait transitions.

Controlling the Robot in V-REP (2 Points)

In intelligent robotics the vital task for the robot is the 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 this course we ar einterested mostly in the artificial intelligence and planning, hence, the localization is provided in 6 Degrees Of Freedom (DOF) in global coordinates by the simulator.

Task 2
Based on the provided locomotion controller and localization routines implement a function which will guide the robot directly to the goal given by its 2D position in the global reference frame and the desired orientation of the robot, i.e., the state of the robot is described as triple $(x, y, \theta)$. (2 Points)
Note that the real robot can never reach the precise goal position, hence, a small neighborhood of the goal position with a diameter of half size of the robot is considered as suitable for navigation.

Provided materials

Lab exercise materials are available for download
The directory structure of the archive is as follows:

lab01 : source files for lab01
- lab01.py : main file with the locomotion control demo
- oscilator_constants.py : implementation of the central pattern generator according to ⁴⁾
- oscilator_network.py : auxiliary file with constants for the central pattern generator