Dynamic load-carrying capacity of mobile-base flexible joint manipulators

Abstract

A computational technique for obtaining the maximum load-carrying capacity of robotic manipulators with joint elasticity is described while different base positions are considered. The maximum load-carrying capacity which can be achieved by a robotic manipulator during a given trajectory is limited by a number of factors. Probably the most important factors are the actuator limitations, joint elasticity (transmissions, reducers and servo drive system) and relative configuration of the robot with respect to its base. Therefore, both actuator torque capacity constraint considering typical torque-speed characteristics of DC motors and trajectory accuracy constraints considering a series of spherical bounds centred at each desired trajectory are applied as the main constraints. For the desired trajectory of load, different base locations are considered. It is seen that the load-carrying capacity at different base positions is different due to distinct dynamic effects of links and load motions on joint actuators. Then, a general computational algorithm for a multi-link case on a given trajectory and different base location is laid out in detail. Finally, two numerical examples involving a two-link manipulator and a PUMA robot using the method are presented. The obtained results illustrate the effect of base location, dual actuator torque and end effector precision constraints on load-carrying capacity on a given trajectory.

Trajectory planning of mobile robots using indirect solution of optimal control method in generalized point-to-point task

“…This matter has been received great interests recently and treated by some authors: Wu et al [16] studied time optimal path planning for a wheeled mobile robot. Korayem et al [17] presented an analytical method for optimal path planning of a mobile manipulator based on iterative linear programming. Using the iterative linear programming cannot result in proper convergence to the answer, especially for nonlinear systems with high-speed motion.…”

Controlling the differential mobile robot with system uncertainty: Constraint-following and the adaptive robust method

“…To deal with the uncertainties, many classic control approaches have been designed, such as sliding mode control (Mobayen and Javadi, 2017), adaptive control (Park et al., 2011), adaptive sliding mode control (Chen et al, 2009a), H ∞ control (Chen et al, 2009b), robust control (Biglarbegian, 2013), robust adaptive control (Shojaei and Shahri, 2012) and optimal control (Korayem and Gariblu, 2004; Korayem et al., 2005; Ghariblu and Korayem, 2006; Korayem et al., 2009; Korayem et al, 2011a; Korayem et al, 2011b; Korayem et al., 2012). Alternatively, some intelligent control approaches have also been presented, such as fuzzy control (Sanchez et al., 2015), adaptive fuzzy control (Chwa, 2012), adaptive neural control (Tang and Liu, 2014) and fuzzy neural control (Su et al., 2010), to name a few.…”

A self-propelled robotic system with a visco-elastic joint: dynamics and motion analysis

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