Webots is a free and open-source 3D robot simulator used in industry, education and research.
The Webots project started in 1996, initially developed by Dr. Olivier Michel at the Swiss Federal Institute of Technology (EPFL) in Lausanne, Switzerland and then from 1998 by Cyberbotics Ltd. as a proprietary licensed software. Since December 2018, it is released under the free and open-source Apache 2 license.[1]
Webots includes a large collection of freely modifiable models of robots, sensors, actuators and objects. In addition, it is also possible to build new models from scratch or import them from 3D CAD software. When designing a robot model, the user specifies both the graphical and the physical properties of the objects. The graphical properties include the shape, dimensions, position and orientation, colors, and texture of the object. The physical properties include the mass, friction factor, as well as the spring and damping constants. Simple fluid dynamics is present in the software.
Webots uses a fork of the ODE (Open Dynamics Engine) for detecting of collisions and simulating rigid body dynamics. The ODE library allows one to accurately simulate physical properties of objects such as velocity, inertia and friction.
Webots includes a set of sensors and actuators frequently used in robotic experiments, e.g. lidars, radars, proximity sensors, light sensors, touch sensors, GPS, accelerometers, cameras, emitters and receivers, servo motors (rotational & linear), position and force sensor, LEDs, grippers, gyros, compass, IMU, etc.
The robot controller programs can be written outside of Webots in C, C++, Python, ROS, Java and MATLAB using a simple API.
Webots offers the possibility to take screenshots and record simulations. Webots worlds are stored in cross-platform *.wbt files whose format is based on the VRML language. One can also import and export Webots worlds and objects in the VRML format. Users can interact with a running simulation by moving robots and other objects with the mouse. Webots can also stream a simulation on web browsers using WebGL.
Simulation of a Robotis-Op3in WebotsSimulation of a Pioneer 3-AT (Adept Mobile Robots) mounted with a SICK LMS 291 in WebotsSimulation of a Pioneer 3-DX (Adept Mobile Robots) in WebotsA simulation model of the PR2 robot in Webots.A simulation model of the Khepera III robot with gripper in Webots.A simulation model of a salamander robot in Webots which has a deformable skin.A simulation model of the Boston Dynamics Atlas robot in Webots.
Web interface
Since August 18, 2017, the robotbenchmark.net website has offered free access to a series of robotics benchmarks based on Webots simulations through the Webots web interface. Webots instances are running in the cloud and the 3D views are displayed in the user browser. From this web interface, users can program robots in Python and learn robot control in a step-by-step procedure.
Controller programming example
This is a simple example of C/C++ controller programming with Webots: a trivial collision avoidance behavior. Initially, the robot runs forwards, then when an obstacle is detected it rotates around itself for a while and then resumes the forward motion.
#include<webots/robot.h>#include<webots/motor.h>#include<webots/distance_sensor.h>#define TIME_STEP 64intmain(){// initialize Webotswb_robot_init();// get handle and enable distance sensorWbDeviceTagds=wb_robot_get_device("ds");wb_distance_sensor_enable(ds,TIME_STEP);// get handle and initialize the motorsWbDeviceTagleft_motor=wb_robot_get_device("left_motor");WbDeviceTagright_motor=wb_robot_get_device("right_motor");wb_motor_set_position(left_motor,INFINITY);wb_motor_set_position(right_motor,INFINITY);wb_motor_set_velocity(left_motor,0.0);wb_motor_set_velocity(right_motor,0.0);// control loopwhile(1){// read sensorsdoublev=wb_distance_sensor_get_value(ds);// if obstacle detectedif(v>512){// turn aroundwb_motor_set_velocity(left_motor,-600);wb_motor_set_velocity(right_motor,600);}else{// go straightwb_motor_set_velocity(left_motor,600);wb_motor_set_velocity(right_motor,600);}// run a simulation stepwb_robot_step(TIME_STEP);}wb_robot_cleanup();return0;}
^Louis-Emmanuel Martinet, Denis Sheynikhovich, Karim Benchenane, and Angelo Arleo (2011) Spatial Learning and Action Planning in a Prefrontal Cortical Network Model, PLoS Comput Biol 7(5): e1002045. doi:10.1371/journal.pcbi.1002045
^Mannella F., Mirolli M., Baldassarre G., A computational model of the amygdala nuclei's role in second order conditioning. In M. Asada et al. (eds.), From Animals to Animats 10: Proceedings of the Tenth International Conference on the Simulation of Adaptive Behavior (SAB2008), pp. 321-330. LNAI 5040 Berlin: Springer.
