Trajectory generation for underactuated control of a suspended mass
|Title||Trajectory generation for underactuated control of a suspended mass|
|Publication Type||Conference Paper|
|Year of Publication||2012|
|Authors||Schultz, J. A., and T. D. Murphey|
|Conference Name||2012 IEEE International Conference on Robotics and Automation (ICRA)|
|Keywords||differential drive robot, dynamic model, dynamical feasible system synthesis, embedded system, embedded systems, Feedback, generalized force, generalized velocity, infinite dimensional optimization algorithm, kinematic reduction, magnetic actuators, magnetic suspension mass, mixed kinematic-dynamic model, mobile robots, nonlinear dynamical systems, nonlinear dynamics, nonlinear optimization, nonlinear programming, robot dynamics, robot kinematics, suspended mass, suspensions (mechanical components), system actuator, trajectory control, trajectory generation problem, underactuated system, winch system, Winches|
The underactuated system under consideration is a magnetically-suspended, differential drive robot utilizing a winch system to articulate a suspended mass. A dynamic model of the system is first constructed, and then a nonlinear, infinite-dimensional optimization algorithm is presented. The system model uses the principles of kinematic reduction to produce a mixed kinematic-dynamic model that isolates the modeling of the system actuators from the modeling of the rest of the system. In this framework, the inputs become generalized velocities instead of generalized forces facilitating real-world implementation with an embedded system. The optimization algorithm automatically deals with the complexities introduced by the nonlinear dynamics and underactuation to synthesize dynamically feasible system trajectories for a wide array of trajectory generation problems. Applying this algorithm to the mixed kinematic-dynamic model, several example problems are solved and the results are tested experimentally. The experimental results agree quite well with the theoretical showing promise in extending the capabilities of the system to utilize more advanced feedback techniques and to handle more complex, three-dimensional problems.