Abstract
This paper presents a nonlinear model predictive control algorithm for online motion planning and tracking of an omnidirectional autonomous robot. The formalism is based on a Hamiltonian minimization to optimize a control path as evaluated by a cost function. This minimization is constrained by a nonlinear plant model, which confines the solution space to those paths which are physically feasible. The cost function penalizes tracking error, control amplitude, and the presence in a potential field cast by moving obstacles and Boards. An experiment is presented demonstrating the successful navigation of a field of stationary obstacles. Simulations are presented demonstrating that the algorithm enables the robot to react dynamically to moving obstacles.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Canadian Journal of Electrical and Computer Engineering
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
