High Performance Modeling Helps Robot Take a Step

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A project using MapleSIM modeling software at the University of Manchester is helping to perfect the process of humanoid walking in robots. MapleSIM is a physical modeling and simulation tool built on a foundation of symbolic computation technology. It handles all of the complex mathematics involved in the development of engineering models, including multi-domain systems, plant modeling, and control design. MapleSIM can reduce model development time from months to days while producing high-fidelity, high-performance models.

As part of the CICADA project, a number of researchers in the Schools of Electrical and Electronic Engineering and Mathematics are working on learning and control approaches to make the bipedal humanoid robot walk.

Work at the University of Manchester’s Centre for Interdisciplinary Computational and Dynamic Analysis (CICADA) coincides with studies being conducted by Professor Darwin Caldwell at the Italian Institute of Technology. He developed a novel compliant humanoid robot called COMAN.

Part of CICADA’s work looks at walking characteristics and other locomotive actions using a hybrid model. The model uses spring dampers to simulate reaction force, actuator dynamics, and compliant elements to capture the robot’s full dynamic responses.

One of the challenges facing the Manchester team is visualizing experiments quickly and effectively to ensure that the experimentation is valid and relevant. “The ability to visualize in MapleSIM, without having to write our own programs has been invaluable,” said PhD student Houman Dallali. “We can directly generate C++ code to interface with the hardware, and speed up the controller implementation and debugging process.”

The work on the robot involves using MapleSIM to develop hybrid control strategy for flexible, dynamic walking.

With a comprehensive and advanced library of models online, Dallali has been able to construct complex simulations easily using the drag-and-drop modeling environment and then exit existing models with little effort due to MapleSIM’s intuitive interface. The linearization techniques are helpful for robotic modeling. “We are building models faster and completing experiments with better data thanks to MapleSIM’s accuracy and kinematic capabilities, noted Dallali.

The research teams developed a MapleSIM simulation of the robot to improve the walking visualization. The humanoid model in MapleSIM includes the actuator dynamics, compliance and ground reaction forces in a simple graphical interface.

To date, the MapleSIM-assisted robotic research at CICADA will help pave the way for future work. The engineering team will move on to projects for dynamic walking with full body control and extended range of gaits. Other professors and students are working on reinforcement learning for humanoid robots and iterative learning techniques. Dallali added, “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.”


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