We propose a registration algorithm for on-line measuring the rotational velocity of a table tennis ball in which the nearest neighbor interpolation method is presented to estimate the intensities of the pixels on the later frame by using the information of the previous frame. The conjugate gradient method is used to seek the optimum of the rotational parameter. Experimental results with both a ball rotated by a rotational machine and a flying ball shot from a catapult machine sufficiently demonstrate the effectiveness of the proposed method.

In flexible manufacturing, robots need to swiftly adapt to constantly changing production tasks. However, it remains a challenging problem for robots to grasp objects of specific categories through language instructions to complete production tasks in cluttered scenes. To address this issue, this article proposes a language-guided category push–grasp synergy network following a cognitive-decision framework. First, inspired by how humans can understand the world through interactions with the environment, we propose an environment state difference embodied self-supervision method that enables robots to autonomously collect embodied multimodal data and generate ground truths that eliminate annotation errors for cognition network training. Second, we develop a language-guided embodied multimodal object cognition network that fuses color and depth image information, enhancing the object cognition ability of robots in cluttered scenes and enabling dynamic semantic segmentation based on language commands. Finally, we propose an object manipulation space metric to measure the manipulable space of target objects, linking the reward function with metric changes before and after actions, thereby enhancing the system's perception of the manipulation space and improving operational performance. Experiments conducted in both simulated and real-world environments demonstrate that our proposed method outperforms existing state-of-the-art methods and can be generalized for grasping novel objects.

Gestures recognition based on surface electromyography (sEMG) has been widely used for human-computer interaction. However, there are few research studies on overcoming the influence of physiological factors among different individuals. In this paper, a cross-individual gesture recognition method based on long short-term memory (LSTM) networks is proposed, named cross-individual LSTM (CI-LSTM). CI-LSTM has a dual-network structure, including a gesture recognition module and an individual recognition module. By designing the loss function, the individual information recognition module assists the gesture recognition module to train, which tends to orthogonalize the gesture features and individual features to minimize the impact of individual information differences on gesture recognition. Through cross-individual gesture recognition experiments, it is verified that compared with other selected algorithm models, the recognition accuracy obtained by using the CI-LSTM model can be improved by an average of 9.15%. Compared with other models, CI-LSTM can overcome the influence of individual characteristics and complete the task of cross-individual hand gestures recognition. Based on the proposed model, online control of the prosthetic hand is realized.