Clearpath engineers use MATLAB with Computer Vision System Toolbox™, Optimization Toolbox™, and Robotics System Toolbox™ to prototype algorithms and analyze and visualize data for robotics research and development.
On a recent project, Saini and his team used MATLAB and Computer Vision System Toolbox to develop an algorithm that detects objects in 2D lidar point clouds and matches the objects against a library of standard object templates. They later used the prototype MATLAB algorithms as a golden reference for building and verifying the final production version of the algorithm.
On another project, the team used MATLAB to develop fleet management algorithms that use agent-based modeling to guide a group of OTTO robots as they work together to complete missions. The team used Optimization Toolbox to minimize specific metrics—for example, completing the mission in the shortest time or within the shortest distance travelled by the robots. The individual robots in the fleet ran control algorithms developed and tuned in MATLAB.
The Clearpath research group takes on a range of projects, from evaluating and incorporating new sensors to basic research, using MATLAB and Robotics System Toolbox to analyze ROS data.
For example, while Baranov’s team was evaluating and integrating a new 3D lidar sensor, they identified a problem with the lidar output. The team used Robotics System Toolbox to import data from rosbag log files generated by the sensor. Working in MATLAB, they analyzed and plotted the timing of the lidar pulses to diagnose the source of the problem: a damaged mirror array within the sensor.
The team followed a similar approach to characterize the stopping distance of Clearpath robots. They used an indoor motion capture system to log data as the robots applied their brakes while traveling at various speeds and across a variety of surfaces.
Using MATLAB and Robotics System Toolbox, the team developed automated testing scripts that caused the robot to accelerate to a specific speed and then stop. The scripts then repositioned the robot and repeated the acceleration and braking.
Following the tests, the team imported the motion capture system data into MATLAB for postprocessing. They plotted acceleration and velocity, and identified inflection points to construct detailed braking profiles for the robots.