In recent years, robots have been increasingly utilized in applications with complex unknown environments, which makes system modeling challenging. In order to meet the demand from such applications, an experience-based learning approach can be used. In this paper, a novel learning algorithm is proposed, which can learn an unknown system model from given data iteratively using a localization approach to manage the computational costs for real time applications. The algorithm segments the data domain by measuring significance of data. As case studies, the proposed algorithm is tested on the control of the mecanum-wheeled robot and in learning the inverse kinematics of a kinematically-redundant manipulator. As the result, the algorithm achieves the on-line system model learning for real time robotics applications.

While cable-driven systems offer the advantages of being lightweight with low moving inertia, the unilateral driving property of cables generally require them to have a greater number of actuators than their rigid-linked counterparts. This paper investigates the use of springs in an attempt to reduce the number of cables required. Given an n-DOF spring-loaded cable-driven open chain, several important questions arise: (i) How can force-closure analysis be carried out for a given spring and cable routing configuration? (ii) Are n+1 cables still necessary to fully constrain the entire open chain? (iii) What is the influence of spring placement on force-closure and cable tension required? This paper will address these concerns by proposing a systematic approach based on reciprocal screw theory. The analysis shows that an n-DOF spring-loaded cable-driven open chain still requires a minimum of n+1 cables to fully constrain it. From preliminary analysis, spring placement can have a positive effect on altering the cable tension required and increasing the feasible workspace.

It has been mathematically proven that a completely restrained n- degree-of-freedom (n-DOF) single rigid-bodied cable-driven platform requires a minimum of n + 1 cables with positive tension to fully constrain it. However, the force-closure analysis of open chains that are driven by cables is still an open question. For the case of an n -DOF cable-driven open chain, the following two important questions arise. 1) How can the force-closure analysis be carried out for a given cable routing configuration, while retaining the geometric insights of the problem? 2) Are n + 1 cables sufficient to fully constrain the entire chain? This paper addresses these issues by proposing a systematic and novel approach based on the reciprocal screw theory. The key idea is to express wrenches acting on the open chain as linear combinations of the reciprocal screws and determine the total required torques at each joint. This is followed by equating the joint torques that are provided by the cable forces with the joint torques, which are required by the external wrenches, and checking for force closure. The proposed methodology can analyze open chains with arbitrary cable routing configuration. The analysis shows that the entire n-DOF open chain requires a minimum of n + 1 cables to fully constrain it.

Due to the unilateral driving property of cables, it has been mathematically proven that a 6-DOF single rigid-bodied cable-driven platform requires a minimum of 7 cables with positive tension to fully constrain it. However, force-closure analysis of open chains driven by cables is still an open question. For the case of an n-DOF open chain driven by cables, two important questions arise: (i) Are n+1 cables sufficient to fully constrain the entire chain? (ii) How can force-closure analysis be carried out for a given cable routing configuration while retaining the geometric insights of the problem? This paper will address these issues and propose a systematic and novel approach based on the reciprocal screw theory. The analysis shows that the entire n-DOF open chain requires a minimum of n+1 cables to fully constrain it and the proposed methodology can analyze any cable routing configuration.

Cable-driven parallel manipulators (CDPMs) are a special class of parallel manipulators that are driven by cables instead of rigid links. Due to the unilateral property of the cables, all the driving cables in a fully-constrained CDPM must always maintain positive tension. As a result, tension analysis is the most essential issue for these CDPMs. By drawing upon the mathematical theory from convex analysis, a sufficient and necessary tension-closure condition is proposed in this paper. The key point of this tension-closure condition is to construct a critical vector that must be positively expressed by the tension vectors associated with the driving cables. It has been verified that such a tension-closure condition is general enough to cater for CDPMs with different numbers of cables and DOFs. Using the tension-closure condition, a computationally efficient algorithm is developed for the tension-closure pose analysis of CDPMs, in which only a limited set of deterministic linear equation systems need to be resolved. This algorithm has been employed for the tension-closure workspace analysis of CDPMs and verified by a number of computational examples. The computational time required by the proposed algorithm is always shorter as compared to other existing algorithms.

This paper presents the kinematic calibration of a novel 7-degree-of-freedom (DOF) cable-driven robotic arm (CDRA), aimed at improving its absolute positioning accuracy. This CDRA consists of three 'self-calibrated' cable-driven parallel mechanism (CDPM) modules. In order to account for any kinematic errors that might arise when assembling the individual CDPMs, a calibration model is formulated based on the local product-of-exponential formula and the measurement residues in the tool-tip frame poses. An iterative least-squares algorithm is employed to identify the errors in the fixed transformation frames of the sequentially assembled 'self- calibrated' CDPM modules. Both computer simulations and experimental studies were carried out to verify the robustness and effectiveness of the proposed calibration algorithm. From the experimental studies, errors in the fixed kinematic transformation frames were precisely recovered after a minimum of 15 pose measurements.

This paper presents the joint centre self-identification of a novel cable-driven anthropocentric shoulder rehabilitator. For such a wearable rehabilitator, identification of joint centre is critical for kinematic modeling, path planning and motion control. However, the shoulder joint centre location is unknown when a user wears this rehabilitator and it differs among users. These complicate the initial patient preparatory. A joint centre self-identification model is formulated based on the differential change in the cable end-point distance. A computationally effective algorithm is employed to identify the shoulder joint centre. It does not require any external measurement devices because of the redundant actuation scheme in the cable-driven shoulder rehabilitator. In order to verify the effectiveness and robustness of the proposed algorithm, we conducted computer simulation studies. In the simulation example, although large errors (up to plusmn50mm) had been injected into the initial estimates, the joint centre was always accurately identified within four to five iterations.