Contouring control is an important issue in industrial applications. For better product quality and higher productivity, achieving higher contour tracking performance with feed rate as large as possible is essential. However, most existing research has focused on improving contour tracking accuracy only. The potential instability issue due to control input saturation under high feed-rate operations has been largely ignored. In this paper, the minimum time trajectory planning problem is formulated as a step toward solving this practically important problem. A novel back and forward check algorithm is developed to take into account physical constraints of the system when generating the desired trajectory to be followed. Compared with traditional numerical searching methods, the proposed algorithm is very computationally efficient because the optimal solution at each node can be obtained by several analytical equations directly. Moreover, the algorithm can incorporate some velocity-dependent constraints easily. Planning a Lissajous curve is used as the case study to verify the optimality and computational efficiency of the proposed algorithm. Experiments are conducted on an industrial biaxial gantry. Several implementation issues on tracking complicated contours are discussed. Experimental results indicate that the proposed trajectory planning approach enables simultaneous achievement of high contouring tracking accuracy and higher feed rate or shorter operation time.

To realize high-performance contouring tracking of dual-linear-motor-driven (DLMD) gantry, both motion coordination of multi-axes and synchronization of redundant actuators are indispensable factors. However, earlier contouring methods of multi-axes systems only focus on motion coordination, leading to certain inherent limitations and uncertainties when applied to DLMD gantry. Especially, the usually ignored synchronization issue may directly affect the normal operation of system. To this end, this paper proposes a novel contouring control method by integrating both motion coordination between axes and synchronization of redundant actuators. With the knowledge of the complete system dynamics, corresponding modification and extension have been done within the framework of global task coordinate frame (GTCF). Subsequently, both associated issues are effectively dealt with in the task space to simultaneously obtain efficient motion coordination ability and high-level synchronization, along with more accurate contouring error calculation. The proposed method is compared to the earlier GTCF based contouring control method without consideration of synchronization issue. The experimental results fully indicate the effectiveness and superiority of the proposed method to indeed guarantee and further improve the contouring performance.

In this article, a novel real-time acceleration-continuous path-constrained trajectory planning algorithm is proposed with an appealing built-in tradeoff mechanism between the cruise motion and time-optimal motion. Different from existing approaches, the proposed approach smoothens time-optimal trajectories with bang-bang input structures to generate acceleration-continuous trajectories while preserving the completeness property. More importantly, a novel built-in tradeoff mechanism is proposed and embedded into the trajectory planning framework so that the proportion of the cruise motion and time-optimal motion can be flexibly adjusted by changing a user-specified functional parameter. Thus, the user can easily apply the trajectory planning algorithm for various tasks with different requirements on motion efficiency and cruise proportion. Moreover, it is shown that feasible trajectories are computed more quickly than optimal trajectories. Rigorous mathematical analysis and proofs are presented for those aforementioned theoretical results. Comparative simulations and experimental results on an omnidirectional wheeled mobile robot demonstrate that flexible tunings between the cruise and time-optimal motions can be achieved in a higher computational efficiency manner by the proposed algorithm.

Challenge issues, such as the simultaneous achievements of high accuracy and productivity in the motion control of mechatronic systems, are widely investigated by the trajectory planning approach. Particularly, a kind of online trajectory planners are developed by Guarino et al. showing the superiority on flexibility and time efficiency in the practical implementation. In this framework, the assigned joint constraints are first transformed into the corresponding bounds on the parametric velocity and acceleration, respectively. However, these existing planners have to assume that the upper and lower bounds of the parametric acceleration are strictly limited to be positive and negative, respectively, which may not be always true in some practical situations and thus will lead to an incorrect trajectory planning solution. To address this restriction, an improved online trajectory planner is proposed in this paper, which consists of a bound estimator and an improved nonlinear filter. The bound estimator transforms the kinematic constraints from the joint space to the parameter space, whereas the online scaling of the rough referenced parametric trajectory is fulfilled by the improved nonlinear filter. Specifically, the limitations on the parametric acceleration with a positive upper bound and a negative lower bound are removed now. And the global convergence of the improved nonlinear filter is presented theoretically. Additionally, an efficient online critical test curve approach is developed to cope with potential stability issues, which are usually dealt with by employing an offline tuning method in the previous online trajectory planning. Comparative planning results show the drawbacks of the previous online trajectory planner in some situations and also confirm the significant improvements of our proposed online trajectory planner. Several contour tracking experiments are conducted and validate that the proposed trajectory planner can achieve a better contour tracking performance.

This paper proposes a modular teleoperation software platform based on the robot operating system (ROS), which is flexible and portable for different master and slave devices. The main issues for the modularity are to develop its control modules in MATLAB/Simulink and access ROS by the robotics system toolbox, which will facilitate the design, analysis, and adjustment of the control algorithm in teleoperation systems greatly. In addition, a case study for this kind of modular teleoperation system is carried out, which is composed of a Geomagic Touch as the master device and a robotic arm of the Rethink Baxter robot as the slave device. Due to the asymmetric structure property between the master and slave devices, the proper workspace mapping which can cover the whole workspace of the slave robot and simultaneously achieve an excellent mapping accuracy becomes a challenging issue. Different from the traditional methods, a hybrid workspace mapping algorithm is proposed, which uses the joint space mapping to cover the whole workspace of the slave device and the operating space mapping to conduct the elaborate manipulation. A smoothing switch law between the joint space mapping and the operating space mapping is also designed. Comparative experiments are carried out, and the results verify the effectiveness and excellent performance of the proposed design methodology.

Trajectory planning approaches including off-line and on-line algorithms are developed to deal with physical constraints in practical systems. However, by now the existing trajectory planning algorithms have to assume that the desired trajectory function to be planned is fully or at least partly known in advance, and it may not be true in some applications. To overcome this limitation, a general framework of on-line planning a desired trajectory under physical constraints whose function is unknown in advance is proposed. The desired trajectory is first on-line interpolated to achieve a mathematical expression, and then planned under the physical constraints by the bound estimator and nonlinear filter. A heuristic critical test curve algorithm is proposed to solve the potential stability issue. A tele-robotic system, where the function of slave desired trajectory is unknown in advance, is selected as a typical case. The experimental results validate the effectiveness of the proposed planning algorithm.