Researchers at Carnegie Mellon University say they have developed an open-source software that enables more agile movement in legged robots. The software was developed after researchers in the Robomechanics Lab found they had to rely on simple models for doing research because existing software solutions were not open sourced, did not provide a modular framework, and lacked end-to-end functionality.
So the researchers designed their own locomotion software stack, Quad-SDK, a full-stack framework for agile quadrupedal locomotion. Their software, say the researchers, can simplify the development process for roboticists everywhere because it uses an open-source license, meaning the software can be used and modified as the user wishes.
Quad-SDK comes ready to use, so researchers don’t have to worry about implementing the necessary tools and infrastructure; instead, they can get right to work on behaviors and applications. Unlike other options, Quad-SDK is also compatible with the Robot Operating System (ROS) – middleware that lets different parts of a system talk to each other. For example, if a robot senses an obstacle in its path and needs to transfer information from its perception module to its decision-making module, ROS is what enables that communication.
Locomotion is a layered problem, say the researchers.
“In order to do anything meaningful on a robot,” says team member and Ph.D. student Ardalan Tajbakhsh, “you need to have many components working together seamlessly.”
Quad-SDK provides a framework for robotics researchers and developers to focus their efforts on the core algorithms instead of the software tooling and infrastructure. Other software packages are very good at solving one component, say the researchers, like motion planning, but it’s critical to have end-to-end frameworks that provide the necessary algorithms, tools, and infrastructure for performing high-quality robotics research.
Quad-SDK is full stack, meaning it contains every level of the hierarchy that affects quadruped locomotion, starting with global planning. This layer sits at the top of the stack. It compares it to Google Maps, say the researchers, because it chooses where the robot should roughly go to reach its destination. The next layer, local planner, is what decides the specifics of the route, like where the robot should place its feet. This culminates in a third layer, the robot driver, which sends commands to the quadruped’s joints to execute the desired motion.