Myoelectric control for prosthetic hands is an important topic in the field of rehabilitation. Intuitive and intelligent myoelectric control can help amputees to regain upper limb function. However, current research efforts are primarily focused on developing rich myoelectric classifiers and biomimetic control methods, limiting prosthetic hand manipulation to simple grasping and releasing tasks, while rarely exploring complex daily tasks. In this article, we conduct a systematic review of recent achievements in two areas, namely, intention recognition research and control strategy research. Specifically, we focus on advanced methods for motion intention types, discrete motion classification, continuous motion estimation, unidirectional control, feedback control, and shared control. In addition, based on the above review, we analyze the challenges and opportunities for research directions of functionality-augmented prosthetic hands and user burden reduction, which can help overcome the limitations of current myoelectric control research and provide development prospects for future research.

In this article, a new method is proposed to help the mobile robot to avoid many kinds of collisions effectively, which combined past experience with modified artificial potential field method. In the process of the actual global obstacle avoidance, system will invoke case-based reasoning algorithm using its past experience to achieve obstacle avoidance when obstacles are recognized as known type; otherwise, it will invoke the modified artificial potential field method to solve the current problem and the new case will also be retained into the case base. In case-based reasoning, we innovatively consider that all the complex obstacles are retrieved by two kinds of basic build-in obstacle models (linear obstacle and angle-type obstacle). Our proposed experience mixing with modified artificial potential field method algorithm has been simulated in MATLAB and implemented on actual mobile robot platform successfully. The result shows that the proposed method is applicable to the dynamic real-time obstacle avoidance under unknown and unstructured environment and greatly improved the performances of robot path planning not only to reduce the time consumption but also to shorten the moving distance.

To address the challenges of sample utilization efficiency and managing temporal dependencies, this paper proposes an efficient path planning method for mobile robot in dynamic environments based on an improved twin delayed deep deterministic policy gradient (TD3) algorithm. The proposed method, named PL-TD3, integrates prioritized experience replay (PER) and long short-term memory (LSTM) neural networks, which enhance both sample efficiency and the ability to handle time-series data. To verify the effectiveness of the proposed method, simulation and practical experiments were designed and conducted. In the simulation experiments, both static and dynamic obstacles were included in the test environment, along with experiments to assess generalization capabilities. The algorithm demonstrated superior performance in terms of both execution time and path efficiency. The practical experiments, based on the assumptions from the simulation tests, further confirmed that PL-TD3 has improved the effectiveness and robustness of path planning for mobile robot in dynamic environments.

With the development of the technologies such as computer vision, natural language interaction, process control technology, and sensor technology, the discussion of automatic sorting system of robot hits a hotspot in the robot community. This paper presents an automatic sorting system for industrial robot with the technologies of 3D visual perception, natural language interaction and automatic programming. Therein, a ‘rule-scene’ matching and interaction algorithm is proposed to combine all these modules together. By utilising our algorithm, robot can interact with human beings according to the real-time actual three-dimensional scene information and can guide users to give correct rules through speech when the rule is invalid. After getting the correct rules, robot can sort the object automatically using the automatic programming and execution algorithm designed in this study. In the experimental section, the designed system is applied to a fruit-sorting scene, which proves the effectiveness and practicability of the system.

Due to the important role of the manipulator dynamic model in manipulation control, the identification of the dynamic parameters of manipulators has become a research hotspot once again. In this paper, we present an overview of the modeling of manipulator dynamics, the optimization methods of excitation trajectory, the identification methods for dynamic parameters, and the identification of friction model parameters. First, the process and basic methods of identification of manipulation dynamic parameters are summarized, and the optimization methods for excitation trajectory are analyzed in detail. Further, friction model parameter identification and the physical feasibility of dynamic parameters are discussed. These are research hotspots associated with the identification of dynamic parameters of manipulators. The backgrounds and solutions of the problems of physical feasibility and identification of friction parameters are reviewed in this paper. Finally, neural networks and deep learning methods are discussed. The neural networks and deep learning methods have been used to improve the accuracy of identification. However, deep learning methods and neural networks need more in-depth analysis and experiments. At present, the instrumental variable method with complete physical feasibility constraints is an optimal choice for dynamic parameter identification. Moreover, this review aims to present the important theoretical foundations and research hotspots for the identification of manipulation dynamic parameters and help researchers determine future research areas.

We propose a new SLAM method based on fast simultaneous localization and mapping (FastSLAM). The technique presented uses an improved quantum-behaved particles swarm optimization (QPSO) to improve the proposal distribution of particles and optimize the estimated particles. This method makes the sampled particles closer to the true pose of the robot and improves the estimation accuracy of robot poses and landmarks. In the QPSO algorithm, the Gaussian disturbance is added to increase the diversity of the particles. By using this technique the premature convergence of particles swarm is overcome. In the resample step, the threshold value is used to evaluate the particle diversity. When the particle diversity is below the threshold value, the linear optimization is used to produce new sample particles, which increases the particle diversity and eliminates the loss of diversity. Simulations and experiments show that the proposed approach improves the accuracy of SLAM. The accuracy of estimated poses and landmarks with the proposed method is better than that with the traditional SLAM method.