Real Time Robotic Motion Planning

The research is motivated by the increasing needs and interests in robots in society and industry, with a goal to significantly advance the computational performance of real-time motion planning of complex robotic systems, such as legged robots. Specifically, we strive to develop massively parallel optimization algorithms and computing hardware to tackle the current computational bottleneck in real-time motion planning of high degree-of-freedom and highly nonlinear robots. This highly interdisciplinary research spans from optimization algorithms to computer hardware, and from mathematical models to robotic systems. The broader impacts of the proposed research stem from its ability to generate theoretical and computational advances in real-time motion planning for a variety of robotic and autonomous systems. Achieving responsive, adaptive motions in a dynamic environment will benefit real-world applications of legged robots, including curbside delivery, planetary science, military and disaster operations, and rehabilitation and medical assistant robotics. Being able to address the real-time motion planning problem in the context of bipeds will also enable inroads into a wide range of robotic platforms, such as robotic manipulators, UAVs, and autonomous driving vehicles.
 

A motion planning problem consists of two components: offline problem formulation and compilation, and online computation and processing.
 

Related Publications


Hereid, A., Harib, O., Hartley, R., Gong, Y. and Grizzle, J. W.
Rapid trajectory optimization using C-FROST with illustration on a cassie-series dynamic walking biped
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2019

[arXiv] [Video]


Hereid, A., Hubicki, C. M., Cousineau, E. A. and Ames, A. D.
Dynamic humanoid locomotion: a scalable formulation for HZD gait optimization
IEEE Transactions on Robotics (T-RO), 2018, Vol. 34(2), pp. 370-387

[DOI] [Video 1] [Video 2]


Hereid, A. and Ames, A. D.
FROST: fast robot optimization and simulation toolkit
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2017, pp. 719-726

[DOI] [Video]


Hereid, A., Cousineau, E. A., Hubicki, C. M. and Ames, A. D.
3D dynamic walking with underactuated humanoid robots: a direct collocation framework for optimizing hybrid zero dynamics
IEEE International Conference on Robotics and Automation (ICRA), 2016, pp. 1447-1454

*Finalist of Best Conference Paper Award

[DOI] [Video]


Hereid, A., Kolathaya, S. and Ames, A. D.
Online hybrid zero dynamics optimal gait generation using legendre pseudospectral optimization
IEEE Conference on Decision and Control (CDC), 2016, pp. 6173-6179

[DOI] [Video]