Pattern recognition (PR) methods are commonly utilized in the extraction of motion intentions from myoelectric signals, which is realized by relating several electromyogram (EMG) patterns to specific types of motion. Researchers have reported that the hand-engineering features widely used in PR-based methods can be significantly affected by external confounding factors that diminish their accuracy and robustness in clinical settings. Moreover, since only simple mapping is carried out from the feature space to the task space (involving, for the most part, discrete motion intentions), there is no opportunity to exploit fully the underlying mechanism of synergic neuromuscular control. Inspired by deep learning, we have proposed a novel convolutional neural network (CNN) structure based on the characteristics of raw EMG signals that can effectively decode complex wrist movements with three degrees of freedom (DOF) directly from raw EMG signals rather than relying on hand-engineering features. Our method has the potential to incorporate more information than other models by enlarging the training dataset. We demonstrate here that our method performs significantly better (in terms of R2) than the current state-of-art regression method (i.e., support vector regression), especially when confounding factors are involved. We further found that this CNN-based decoding method can be generalized when multiple healthy subjects are taken into account. For a new subject, our method can provide an appropriate control over 3-DOF cursor movements on a screen even without a specific training.

Current constrained reinforcement learning (RL) methods guarantee constraint satisfaction only in expectation, which is inadequate for safety-critical decision problems. Since a constraint satisfied in expectation remains a high probability of exceeding the cost threshold, solving constrained RL problems with high probabilities of satisfaction is critical for RL safety. In this work, we consider the safety criterion as a constraint on the conditional value-at-risk (CVaR) of cumulative costs, and propose the CVaR-constrained policy optimization algorithm (CVaR-CPO) to maximize the expected return while ensuring agents pay attention to the upper tail of constraint costs. According to the bound on the CVaR-related performance between two policies, we first reformulate the CVaR-constrained problem in augmented state space using the state extension procedure and the trust-region method. CVaR-CPO then derives the optimal update policy by applying the Lagrangian method to the constrained optimization problem. In addition, CVaR-CPO utilizes the distribution of constraint costs to provide an efficient quantile-based estimation of the CVaR-related value function. We conduct experiments on constrained control tasks to show that the proposed method can produce behaviors that satisfy safety constraints, and achieve comparable performance to most safe RL (SRL) methods.

SUMMARY: In this paper, autonomous motion control approaches to generate the coordinated motion of a dual-arm space robot for target capturing are presented. Two typical cases are studied: (a) The coordinated dual-arm capturing of a moving target when the base is free-floating; (b) one arm is used for target capturing, and the other for keeping the base fixed inertially. Instead of solving all the variables in a unified differential equation, the solution equation of the first case is simplified into two sub-equations and practical methods are used to solve them. Therefore, the computation loads are largely reduced, and feasible trajectories can be determined. For the second case, we propose to deal with the linear and angular momentums of the system separately. The linear momentum conservation equation is used to design the configuration and the mounted pose of a balance arm to keep the inertial position of the base's center of mass, and the angular momentum conservation equation is used to estimate the desired momentum generated by the reaction wheels for maintaining the inertial attitude of the base. Finally, two typical tasks are simulated. Simulation results verify the corresponding approaches.

Cable-Constrained Synchronous Rotating Mechanism (CCSRM) has an important application prospect in the field of cable-driven robots, which can greatly reduce the number of driving motors while ensuring the light and slender body. However, there are obvious cable friction effect and elastic deformation in CCSRM. These nonlinear characteristics have a significant impact on synchronous motion performance. In this letter, a model of CCSRM considering cable friction is proposed, which integrates the effects of cable pretension, elastic deformation, and friction between cable and pulley on system characteristics. The distribution law of cable tension under the influence of friction force and the phenomenon of motion hysteresis caused in reverse rotation are emphatically discussed. Then, an improved LuGre friction model is proposed to solve the problem of line contact friction between cables and pulleys. Further a dynamic model of CCSRM is established to simulate the motion characteristics of the whole process, including the discontinuous friction phenomenon in reverse rotation. Finally, an experimental prototype of two-axis synchronous rotating system is built, and the friction coefficient is identified. The experimental results show that the dynamic model can well simulate the motion characteristics of CCSRM.

In this paper, we focus on the integration of motion planning and control for high-performance automated vehicles. We propose a novel tube-based nonlinear model predictive control (TNMPC) scheme that combines the strengths of MPC and boundary layer sliding control (BLSC) to drive the controlled vehicle along a real-time and high-performance trajectory while simultaneously ensuring practical satisfaction of constraints. Specifically, our approach uses an extended kinematic vehicle model with slip angle parameters to provide explicit expressions for modeling error, thereby facilitating the design of BLSC gains. We then reveal an inequality relation between the slip angles and acceleration, which allows the BLSC gains to be represented as functions of the optimization variables. This innovative approach enables the inclusion of constraints on the BLSC gains into the TNMPC motion planning problem, striking a balance among performance, modeling error, and practical control capability. The feasibility of the kinematic model is also guaranteed without the need for the commonly adopted acceleration constraint. Moreover, tightening of the state and input constraints in TNMPC is achieved through coordinate transformation of the tubes, interval analysis, and robust optimization theory. Simulations in racing scenarios demonstrate the robustness and effectiveness of the proposed TNMPC scheme, illustrating its ease of adaptability across various road friction conditions.