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Locomotion mode recognition across multiple sessions and days is an indispensable step towards the practical use of the robotic transtibial prosthesis. In this study, we proposed an adaptive recognition strategy to against the time-varying features of on-prosthesis mechanical signals in inter-session and inter-day recognition tasks. The strategy was designed with an automatic training algorithm which could update the classifiers with the data of the most recent completed gait cycles to seize the changes of the features brought by external disturbances. After implementation, we measured multiple experimental sessions on six transtibial amputees with intervals from a few hours (inter-session experiment) to several months (108 days at most in inter-day experiment). In each session, they performed five locomotion modes and eight locomotion transitions using the robotic prosthesis. Between each two experimental sessions, the subjects were required to doff the robotic prosthesis (including the socket). The proposed adaptive recognition algorithm significantly improved recognition accuracies in both experiments. In inter-session experiment, the proposed method increased the recognition accuracy from 89.3% to 92.8% than previous non-adaptive recognition method. In inter-day experiments, it increased the recognition accuracy from 60% to 88.8%. If taking three modes (level walking, stair ascending/descending) and four locomotion transitions into calculation, the recognizer produced an accuracy up to 96.6% (swing phase) for static mode and an accuracy of 96.9% for locomotion transitions on inter-day tasks without manual intervenes. Compared with state-of-the-art, our study extends the ability of the robotic transtibial prosthesis in locomotion mode recognition across multiple sessions and days. Future efforts are worth being paid in this direction to get more promising results.

This paper presents an event-triggered model predictive control (MPC) strategy with learning terminal cost for robotic manipulators containing model uncertainty and input constraints. In the proposed MPC structure, an adaptive predictive model for the robotic system is established by radial basis function neural networks (RBFNNs) firstly. Then, a terminal cost adjusted by the global learning mechanism is constructed. Both global steady-state optimization and transient fast convergence are achieved by adding the learning terminal cost to the MPC scheme. After that, a triggering condition of the MPC solving is developed based on the predictive model’s weights and the predictive tracking error. Besides, the condition to avoid Zeno behavior is obtained. The recursive feasibility of the proposed MPC strategy is verified, and the ultimately uniformly boundedness (UUB) of all variables is proved according to the Lyapunov theorem. Finally, experiments based on an xMate7 Pro robot are conducted to demonstrate the effectiveness of the presented method. Note to Practitioners—The tracking control of robotic manipulators is a common and important problem in industrial applications, such as grasping, loading, unloading, et al. There exist some limitations in existing control methods. For example, general control strategies such as adaptive control, sliding mode control neglect the balance between control costs and expected performance; existing optimal control approaches rarely consider global steady-state optimization and transient fast convergence under the influence of model uncertainty. This paper is motivated by these limitations of robotic control design and inspired by model predictive control and optimal control theory. It develops a novel strategy for tracking control of robotic manipulators, which includes three following items: (1) adopt an approximation model constructed by neural networks as the predictive model for estimating the robotic system; (2) introduce a critic network into the terminal cost of the objective function of MPC for learning global optimal solution; (3) establish an event-triggered mechanism for solving of the optimization problem. The proposed method is verified by experimental results. In our future work, the proposed control strategy will be applied to more industrial processes. [ABSTRACT FROM AUTHOR]

Policy search in reinforcement learning (RL) is a practical approach to interact directly with environments in parameter spaces, that often deal with dilemmas of local optima and real-time sample collection. A promising algorithm, known as guided policy search (GPS), is capable of handling the challenge of training samples using trajectory-centric methods. It can also provide asymptotic local convergence guarantees. However, in its current form, the GPS algorithm cannot operate in sequential multitask learning scenarios. This is due to its batch-style training requirement, where all training samples are collectively provided at the start of the learning process. The algorithm’s adaptation is thus hindered for real-time applications, where training samples or tasks can arrive randomly. In this paper, the GPS approach is reformulated, by adapting a recently proposed, lifelong-learning method, and elastic weight consolidation. Specifically, Fisher information is incorporated to impart knowledge from previously learned tasks. The proposed algorithm, termed sequential multitask learning-GPS, is able to operate in sequential multitask learning settings and ensuring continuous policy learning, without catastrophic forgetting. Pendulum and robotic manipulation experiments demonstrate the new algorithms efficacy to learn control policies for handling sequentially arriving training samples, delivering comparable performance to the traditional, and batch-based GPS algorithm. In conclusion, the proposed algorithm is posited as a new benchmark for the real-time RL and robotics research community. [ABSTRACT FROM AUTHOR]

In various industries, there is a need for low-cost, flexible robotic grasping systems that handle and classify different items, which usually involve low-cost grippers and reliable manipulations. One-degree-offreedom (1-DoF) two-pin or three-pin grippers are widely used in the automation of manufacturing processes because of their low cost and high reliability. However, some uncertainties introduced by the object and the gripper may affect the robustness of the grasp. The principle of caging enables us to deal with these uncertainties, and it is therefore important to study caging as a practical solution to improve the grasps of 1-DoF industrial grippers. [ABSTRACT FROM AUTHOR]

A precise robotic assembly for large-scale objects is an important application in manufacture. In this paper, we design a new assembly system for large-scale objects based on automatic guidance and alignment with two cameras and three laser distance sensors. The corresponding strategy consists of three stages. First, the object is guided from a remote position to a close one based on our proposed link-line model-based polygon detection and Computer Aided Design (CAD) - View method. Second, the object is aligned to the installation framework based on the proposed visual and laser alignment method. Finally, the object is automatically inserted into the installation framework. An accurate easily applying and close-loop hand-eye calibration method is also proposed. The proposed assembly system has high assembly precision and efficiency. In our test for a 710 \text mm \times 625 mm optical module, the maximum 6-DOF localization error of guidance and alignment is 0.53 mm in translation and 0.10° in orientation. The mechanical constraint-based controlling accuracy, i.e., the final assembly accuracy, is up to 0.2 mm in translation. [ABSTRACT FROM PUBLISHER]

Reports on RASS Society activity in China. [ABSTRACT FROM PUBLISHER]

In general, localization is a very important step in the manufacturing process, which can be considered as a prior process of assembly, machining, transportation, etc. Localization can be achieved with or without sensors. Compared with localization with sensors, localization without sensors can be more reliable, cheaper, and can have lower requirements on the environment. For example, localization without sensors can be achieved in a dark environment. However, it is more restrictive to the condition of the system. Localization of 2-D objects without sensors has been deeply investigated. Some theoretical results on 3-D-object localization without sensors have been given. On the other hand, some work has been carried out that tries to find methods to reduce uncertainties in the 3-D orientation of a polyhedron (i.e., the orientation of the polyhedron in a 3-D space). However, to the best of our knowledge, no practical and effective methods have been proposed, so far, to localize a polyhedron from any initial 3-D orientation to a unique 3-D orientation without sensors. This paper aims to find conditions and strategies for 3-D objects to be rotated from an unknown initial stable state to a unique stable state without sensors. The main contributions of this paper are given as follows: 1) It is discovered and proved that there are two classes of 3-D objects that can be rotated into a unique state from an arbitrary initial state without sensory feedback. 2) For these two classes of objects, the practical strategies are presented, and one example for each class is given to show the validity of the strategy. 3) Based on the above results, the robotic system and localization operations are illustrated, and some experimental results are given. [ABSTRACT FROM PUBLISHER]