Collaborative manipulation of rigid objects

In the past decades a number of control methods for the coordinated motion of manip-ulators have been developed. Nakino et al. [2] used force sensors for the coordination and control of two arms. Luh and Zheng [3] formulated a closed loop kinematic chain where the position and orientation of two robots had satisfied the necessary constraints. In their master-slave approach, if the trajectory of the master arm is planned and executed, the slave arm trajectory was derived from the constrained relations and correspondingly the coordi-nation was achieved. Ishida [5] proposed a force control algorithm which uses a PID con-troller to move the object in a parallel and rotational mode. The master-slave principle was employed and interactive forces between the two arms were measured using a wrist force sensor. The master robot arm was position controlled and slave arm was position and/or force controlled based upon the information given by the master arm. Alford and Belyeu [6]

utilized the concept of Ishida [5] for the position control of two arms. In their case, given the trajectory of the master arm, the slave arm trajectory is modified in run time. Zheng and Sias [7] studied the collision effects between the end-effectors which caused changes in joint velocities, and impulsive force generated at the end-effectors was used to detect the position and orientation of the two arms. Tarn et al. [8, 9] developed a nonlinear feedback

control method to control two Puma robot arms and also the position/velocity errors and force/torque errors were reduced. However, the master-slave approach failed due to the kinematic and dynamic uncertainties in an un-calibrated slave robot joint measurements.

In order to resolve this issue, hybrid position/force control algorithm was developed.

The Hybrid position/force control scheme developed by Raibert and Craig [10] cre-ated a new arena for controlling the manipulators in non - deterministic environments. In the case of hybrid position/force control, the position and force information are separately fed back and compared with the desired value. The corrective action is taken separately by applying position and force control laws, and then converting it into joint torques using the Jacobian. By selecting 0’s and 1’s in the matrices, the position and force control action is determined. However, it was only applied to a single arm robot. As far as the two robot co-ordination was concerned, Hayati [11] proposed a control architecture based on the Raibert hybrid control strategy [10] for multi arm robots grasping a rigid object. Uchiyama et al.

[12, 13] and Dauchez et al. [14] have used Hayati’s [11] algorithm for their applications and further investigations to control the coordination between two robot arms. They have considered the static force relationship and it can be used only for low speed operations [15]. Experimental results of Kopf and Yabuta [16] showed that the hybrid control law achieves better coordination than master-slave control scheme. However, Duffy [17] iden-tified some fallacies in the hybrid position/force control scheme. In the master-slave and hybrid control approach, the controllers need the accurate information of the dynamic pa-rameters. However, in the real time applications, industrial manipulators have uncertainties while grasping the load which cannot be handled by master-slave and hybrid position/force control methods. Hence, nonlinear control algorithms have to be adopted.

In order to adapt to the uncertainties, an adaptive scheme [18] which controls the motion of the object, internal force and contact force with respect to the environment was developed and simulated. Several adaptive based control schemes [19]-[21] have been pro-posed by various researchers. However, these methods use structure information of the robot. Furthermore, object dynamics can lack robustness to unmodeled dynamics such as arm or object flexibility, actuator lags, and sensor noise. Although many of them proposed and simulated the various control algorithms without friction and neglecting gravitation effects, they provided a great insight into further development. A few of them had im-plemented their control strategy in the experiments. Bonitz and Hsia [22, 23] introduced a robust internal force based impedance control for the manipulators coordination. Under this control scheme, nonlinear dynamic terms of the robot are compensated. The developed controller was implemented through experiments by using the two Puma robots. Uzmaya et al. [24] performed the simulation considering uncertainties such as contact and friction

constraints for grasp, bearing conditions and structural flexibility using adaptive, robust and inverse dynamics controllers. Gueaieb et al. [25] proposed a hybrid (combination of a conventional adaptive controller and an adaptive fuzzy controller) intelligent controller to handle unwanted parametric and modeling uncertainties. The simulation was carried out and it was evident from the results that the controller was very effective. Caccavale et al.

[26] developed centralized impedance control which was aimed at conferring the compliant behavior of the object and decentralized impedance control to avoid large internal loading of the object. These control algorithms were implemented in the two 6 Degrees of Free-dom (DOF) manipulators test bed. Moosavian and Papadopoulos [27] also incorporated

impedance control to achieve the free motions and contact tasks without changing the con-trol modes. The simulation results confirm that the two manipulators achieve good tracking performance. In order to handle load transportation of two robots, a sliding mode control [28] has been implemented. The comparative study on PID and sliding mode controllers through simulation results showed that, the tracking error is minimized in sliding control.