The University of Manchester is carrying out a project to help perfect the process of ‘humanoid walking’ in robots. The new Centre for Interdisciplinary Computational and Dynamic Analysis (CICADA) at Manchester has been working with Professor Darwin Caldwell at the Italian Institute of Technology, Genova, who has been developing a compliant humanoid robot (COMAN) based on the previously developed humanoid robot iCub at IIT.
Part of CICADA’s work looks at walking characteristics and other locomotive actions using a hybrid model. The model uses spring/dampers to simulate ground reaction force, actuator dynamics and compliant elements to capture the robot’s full dynamic response.
A challenge faced by the team, lead by Dr Martin Brown and Dr Gustavo Medrano-Cerda, however, has been visualising experiments quickly and effectively, to avoid slowing down the process and to ensure that experimentation is valid and relevant. So, to help, MapleSim physical modelling software from Adept Scientific is being used.
“The ability to visualise in MapleSim, without having to write our own programs, has been invaluable,” says PhD student Houman Dallali. “What’s more, we can directly generate C++ code to interface with the hardware and speed up the controller implementation/debugging process.”
Using the advanced library of models online, Dallali has been able to construct complex simulations easily using the ‘drag and drop’ modelling environment and then edit existing models with little effort.
The inclusion of linearisation techniques in the MapleSim offering is also important for robotic modelling. “We are building models faster and completing experiments with better data thanks to MapleSim’s accuracy and kinematics capabilities,” explained Dallali.
The speed and success of the MapleSim-aided research means that the CICADA team will quickly move on to projects for dynamic walking with full body control and extended range of gaits.
Brown and his PhD student, Onder Tutsoy, are working on ‘reinforcement learning’ for humanoid robots as well as ‘iterative learning’ techniques.
Dallali said: “In the future, we will be adding logic and learning approaches to our code and looking to develop applications from the research, such as better prosthetics and walking aids.”
The rewards for developing robots capable of reproducing human movement and actions go far beyond engineering, having applications and ramifications for medical treatment, computer technology, defence, exploration and much more.
Adept Scientific
