Sampling planning algorithms are crucial in high-dimensional path planning for unmanned aerial vehicle (UAV), particularly in underground environments where Global Positioning System (GPS) signals are absent. The Rapidly-exploring Random Tree (RRT) algorithm, however, faces challenges due to the use of pseudo-random sequences, resulting in issues such as under-sampling, over-sampling, and they also suffer from high computational costs and redundant paths. To address these limitations, the (Halton-based Clustering) HBC-RRT algorithm is proposed. This algorithm utilizes the Halton sequence, replacing the pseudo-random sequence, which fundamentally resolves the problems of under-sampling and over-sampling in the RRT method. Additionally, a novel sampler is introduced, inspired by the fruit fly guidance mechanism, which optimizes the dual-tree sampling process by selecting either the optimal sampling candidate strategy or virtual sub-goal points. The optimal sampling candidate strategy conducts global sampling in three-dimensional space, effectively reducing sampling time. The virtual sub-goal points accelerate the dual-tree connection process, enhancing the directional guidance. The fruit fly mechanism reconstructs the path, leading to a reduction in UAV path costs. The performance of the HBC-RRT algorithm is evaluated through simulation experiments, demonstrating superior results in comparison to four baseline algorithms, including reductions in path length, convergence time, and memory usage. The practical applicability of the algorithm is further validated through real-world experiments involving a UAV and radar in an underground environment.

Humanoid robots with anthropomorphic hands can assist or replace humans in performing complex tasks in dynamic scenarios, such as industrial manufacturing. This article presents a rigid-soft coupling anthropomorphic hand system aimed at improving grasping performance by focusing on dexterity, adaptability, and system integration. The hand adopts a bionic design inspired by the human hand, featuring rigid-soft fingers fabricated using a multimaterial layered manufacturing approach and optimized thumb parameters to maximize the enveloping grasp range. A lightweight, embedded control system is implemented alongside a gesture-coordinated control method based on disturbance observer, further enhancing functionality. Weighing approximately 600 g and resembling adult hand dimensions, the hand demonstrates human-like dexterity. Its ability to leverage environmental constraints for grasping reduces dependence on perception and planning, enabling secure and efficient interactions. This study offers meaningful insights into deploying humanoid robots for intricate tasks in dynamic environments.

As a successful application of machine learning in remote sensing and natural language processing, image captioning of remote-sensing images has been promoted and developed. Remote sensing images are large in width, complex in features, and contain abundant information. It is a difficult task to extract available visual features based domain knowledge behind sufficiently and to utilize extracted feature for image captioning generation sufficiently. In order to overcome this difficulty, we propose a novel model based “encoder-decoder” framework, termed remote sensing image captioning with sequential attention and flexible word correlation (SA-FWC). In the encoder, we fuse features of different layers in VGG16 to extract global and local information. In the decoder, we propose sequential attention and flexible word correlation (SA-FWC) to utilize extracted visual information to generate accurate image captioning sufficiently. Specially, to utilize visual features from the encoding layer sufficiently, highlight important information and reduce redundant information, long short-term memory (LSTM) in SA-FWC is used for obtaining better feature representations. Feature fusion strategy and self-attention mechanism to utilize visual features sufficiently. Additionally, we provide a data augmentation strategy based minimal training sample pairs. In the experiments, four evaluation metrics are used to evaluate the experimental results, and the effects of various parameters on the experimental results are discussed. The experimental results (BELU-0.72, ROUGE-0.65, METEOR-0.37, and CIDEr-2.83) show that the proposed method is effective and outperforms other network structures.

Digital twin and data-driven technologies provide an idea for realizing complex product quality prediction. Aiming at the issues of real-time visual monitoring, operating status analysis, and quality prediction in complex die-casting intelligent manufacturing, a digital twin and data-driven quality prediction architecture is proposed. The virtual-real interaction digital twin of die-casting manufacturing cells is established. The collaborative working mode of physical cells, virtual cells, and real-time monitoring is constructed to predict product quality. The learning method of die-casting parameter data and appearance defect data is proposed to realize the real-time quality prediction in die-casting process and the appearance defect quality prediction after processing, respectively. The data preprocessing and XGBoost-based learning method is proposed for real-time quality prediction of die-casting process. A single-shot refinement neural network for aluminum casting tiny defects detection (Refine-ACTDD) based on deep learning is proposed to solve the high-precision defect detection problems of small appearance defects of complex castings and large interference of complex background. Taking the complex aluminum die-casting as an example, the applications of quality prediction are verified. The method provides a new technical approach for high-precision quality prediction of complex die-casting manufacturing.

Soft robots are compliant to wrap large objects and adaptive to unstructured environments, while rigid robots can bend discretely at joints and pinch small objects easily. In this paper, we aim to create a novel robot that can behave like a rigid or soft robot adaptively, without additional stiffness-tunable mechanism. This approach makes the robot lightweight and practical. First, we present the fabricating process of the soft-rigid robot developed in the work. A steel flexure is especially optimized and embedded in the flexible body of the robot, which enhances the bending stiffness in the axial direction dramatically while keeping the body in other directions flexible and adaptive. Second, the kinematic model and analysis are developed to characterize the different configurations of the soft-rigid robot influenced by various parameters. Third, experiments are conducted to evaluate the performance of the flexible body and validate the accuracy of the kinematic model. Finally, a two-fingered gripper is built based on the soft-rigid robots, and grasping experiments are conducted to demonstrate how the soft-rigid robots could enhance the capability of the gripper in real-word.

Aiming at the obstacles in robot visual navigation path, a robot obstacle avoidance method based on local descriptors and feasible path is proposed to achieve local path planning. The local grid of the robot is divided and the obstacle model is established. The risk function and feasible path of the robot is defined. For visual navigation, the navigation direction angle is obtained based on the horizontal coordinate of local descriptors matching pair. While for obstacles in robot navigation path, the local obstacle avoidance control strategy is presented to find a feasible path around the obstacles. The method can choose an optimal path for obstacle avoidance and then return to the planned path through environmental interaction during visual navigation. The experimental result shows that this method can effectively achieve obstacle avoidance task during robot visual navigation.

In arc welding processes, real time control algorithms have to be developed in order to achieve desired weld quality. However, there could exist big uncertainties and noise in the process, which nullifies the conventional online control method. Besides, due to the modelling difficulty and low experimental efficiency, this task is usually performed offline. In this paper, a real time parameter optimization method is developed to find the optimal welding parameters to achieve desired characteristic performance. Gaussian Process Regression (GPR), a non-parametric modelling technique, is employed to model the relationship between input welding parameters and output characteristic performance. The GPR surrogated Bayesian Optimization Algorithm (GPRBOA) is proposed to optimize the welding parameters. Lower Confidence Bound (LCB) and Upper Confidence Bound (UCB) acquisition functions are utilized. Gas tungsten arc welding experiments were performed and the corresponding experimental data are collected and utilized to validate the proposed modelling method. The predicted characteristic performance is compared with the original data and it shows that the modelling method can accurately predict the weld bead geometry. The control algorithms were demonstrated and the results are presented. This paper opens a door for real time parameter tuning to achieve desired performance, hence the proposed method will innovate the arc welding process.

In recent years, Global Positioning System (GPS) spoofing is becoming more attentions in our society. How to detect GPS spoofing is a challenging work. In this paper, a GPS spoofing detection scheme is proposed based on a quadrotor unmanned aerial vehicle (UAV) model. The idea is that it compares the error between the UAV model estimator and the actual signal with a given threshold to justify whether the UAV is spoofed. Simulation is given to show that the proposed detection scheme is effective for the controlled UAV when its GPS signal is spoofed.

This paper proposes a framework called Episodic memory-driving Markov decision processes (EM-MDPs) for incremental self-learning of robotic experience and cognitive behavior control under uncertainty. The framework simulates the organization process of episodic memory by introducing the neuron stimulation mechanism. Firstly, episode model is built, and the activation and stimulation mechanism of state neurons is proposed based on cognitive neuroscience. Secondly, episodic self-learning is also proposed by utilizing sparse distributed memory (SDM) through Hebbian rules, to realize memory real-time storage, incremental accumulation and integration. Finally, a robotic cognitive behavior control approach is established. Neuron synaptic potential is introduced for event localization. Robot can evaluate the past events sequence, predict the current state and plan the desired behavior. Two main challenges in robot behavior control under uncertainty are addressed in the paper: high computational complexity and perceptual aliasing. The proposed system is evaluated in several real life environments for mobile robot. The applicability and the usefulness of the developed method are validated by the results obtained.