ABSTRACTThis study investigates an adaptive prescribed‐time control scheme for marine surface vehicles (MSVs). Based on the introduced time‐varying gain function, this article provides a sufficient condition for practical prescribed‐time stability. The aim is to provide a theoretical basis for the prescribed‐time controller design. The upper bound of the settling time can be accurately preset by adjusting the design parameters. Furthermore, adaptive parameter technique is utilized to handle the unknown lumped disturbances. Theory confirms that the tracking error of the MSV satisfies the conditions for practical prescribed‐time stability. The superiority and effectiveness of the scheme are verified through simulation analysis.

ABSTRACTExisting place recognition methods overly rely on effective geometric features in the data. When directly applied to underground tunnels with repetitive spatial structures and blurry texture features, these methods may result in potential misjudgments, thereby reducing positioning accuracy. Additionally, the substantial computational demands of current methods make it challenging to support real‐time feedback of positioning information. To address the challenges mentioned above, we first introduced the Feature Reconstruction Convolution Module, aimed at reconstructing prevalent similar feature patterns in underground tunnels and aggregating discriminative feature descriptors, thereby enhancing environmental discrimination. Subsequently, the Sinusoidal Self‐Attention Module was implemented to actively filter local descriptors, allocate weights to different descriptors, and determine the most valuable feature descriptors in the network. Finally, the network was further enhanced with the integration of the Rotation‐Equivariant Downsampling Module, designed to expand the receptive field, merge features, and reduce computational complexity. According to experimental results, our algorithm achieves a maximum score of 0.996 on the SubT‐Tunnel data set and 0.995 on the KITTI data set. Moreover, the method only consists of 0.78 million parameters, and the computation time for a single point cloud frame is 17.3 ms. These scores surpass the performance of many advanced algorithms, emphasizing the effectiveness of our approach.

ABSTRACTLow‐level control plays a critical role in the overall flight performance of drones. Despite some progress, existing methods do not adequately consider the complex electromechanical characteristics of actuators and aerodynamic disturbances of propellers, which deteriorates low‐level control performance. To tackle these issues, we propose a new nonlinear low‐level controller for higher precision control. First, a high‐precision low‐level model that integrates the actuator dynamics and angular velocity dynamics is presented. Based on this model, a dynamic compensator is designed using the internal model method to handle aerodynamic disturbances. Next, a high‐gain observer is designed to estimate the actuator states without adding new sensors, and an observer‐based low‐level controller is proposed through a recursive design process. Finally, the local asymptotic stability of the closed‐loop system is analyzed. The effectiveness and superiority of the proposed method are validated through simulations and physical experiments.

ABSTRACTAccurate positioning for autonomous driven underground mining vehicles (UMVs) and coal mine robots (CMRs) is indeed one of the cores in the intelligentization of coal mining. Completely different from positioning on the ground and in parking scenes, there will be great difficulties in realizing the accurate active positioning for UMVs shuttled in dark and narrow roadways. We propose an effective visual and inertial fusion positioning method for autonomous CMRs and roadheaders in challenging roadway scenarios based on the odometer‐aided inertial navigation system and visual pose estimation system. Velocity information of the odometer is adapted to restrain the error accumulation of inertial positioning based on a Kalman filter. The hybrid visual feature detection algorithm is put forward to improve the accuracy and robustness of visual observation information in a dark environment. Autonomous experiments for CMRs and roadheaders are separately performed in the narrow roadway and dark passageway to demonstrate the applicability of our localization method. The proposed approach outperforms the subsystems and existing methods in accuracy and has outstanding stability.

ABSTRACTThis paper presents a novel wheeled robot with adaptive suspensions to maintain a stable body posture when driving uneven roads. Each robot suspension is driven by a brushless geared motor, eliminating the traditional mechanical shock absorber. We design a virtual spring‐damping system using force control to solve the problem of uneven support and high‐frequency oscillation on four wheels. Subsequently, we incorporated a flexible posture control that uses the chassis posture as feedback and linearized the control to improve the dynamic response. Finally, the prototype is experimented on a simulated road and verified that the prototype and the proposed control algorithm can realize the expected requirements.

ABSTRACTTechnological advancements have spurred the development of unmanned ground vehicles (UGVs) and their innovative military applications and strategies. Such applications include expendable UGVs. However, public research concerning expendable military UGVs remains sparse. Particularly, the unclassified literature does not contain studies regarding their capabilities and effectiveness in combat. This study introduces a new low‐cost expendable UGV called Laykka. Moreover, the study presents a virtual simulation experiment to evaluate Laykkas' operational capabilities and their impact on advancing mechanized infantry units. The experiment involved armored reserve officer students assuming the roles of infantry troops forming an attacking opposing force while staff officers controlled simulated infantry troops operating the UGVs. A total of 16 battle simulations were fought. The simulated UGVs operated by a single soldier were able to stall the advancement of the mechanized infantry company's primary combat units three times out of four and a smaller force 11 out of 12 times. The best stalling effect was observed using a mix of UGVs with different module types. These modules allowed reconnaissance, loitering mine, and anti‐tank operations. The simulation experiment revealed that the UGV was an effective defensive tool due to its self‐destructive capability, causing marked battle damage, disruption, and confusion to the opposing forces.

ABSTRACTAt present, rescue firefighting relies mainly on firefighting robots, and robots with perception and decision‐making functions are the key elements for achieving intelligent firefighting. However, traditional firefighting robots often lack the ability for autonomous perception and decision‐making when extinguishing multiple fire sources, leading to low rescue efficiency and increased risk for rescue personnel, especially when making firefighting decisions in extreme fire scenes, which poses a challenge. To effectively handle firefighting tasks and ensure operational efficiency, a robot firefighting decision‐making method based on drone visual guidance is proposed. First, we introduce a novel Attention and Scale U‐Net (ASUNet) model to accurately capture crucial target information, including fire location and size, in a fire scene. The ASUNet model adopts an effective multiscale fusion strategy and attention mechanism to enhance the model's performance. Subsequently, based on the results of the ASUNet model, through pixel segmentation clustering and a genetic optimization algorithm, we obtain the robot's firefighting decision results, thereby guiding the robot to carry out firefighting operations systematically. Finally, through numerical experiments, it is verified that the proposed ASUNet model is superior and effective, as the model can perceive important information in a fire scene and extract it well. The use of improved genetic optimization can further accelerate algorithm convergence. To our knowledge, this study is the first to use drone‐based monocular vision guidance for firefighting decision‐making, providing significant engineering value.

ABSTRACTIn robotics, navigating a humanoid robot through a cluttered environment is challenging. The present study aims to enhance the footstep and determine optimal paths regarding the robot's route length. The objective function for navigation of multiple humanoid robots is presented to optimize the route length and travel time. A hybrid technique using a probabilistic roadmap (PRM) and firefly algorithm (FA) is presented for humanoid robot navigation in a cluttered environment with static and dynamic obstacles. Sensory information, such as barrier range in the left, right, and front directions, is fed into the PRM framework that allows the humanoid robot to walk steadily with an initial steering angle. It finds the shortest path using the Bellman–Ford algorithm. The FA technique is used for efficient guidance and footstep modification in a cluttered environment to find a smooth and optimized path. To avoid static obstacles, the suggested hybrid technique provides optimum steering angles and ensures the minimum route length by taking the output of PRM as its input. A 3D simulator and a real‐world environment have been used for simulation and experiment in a cluttered environment utilizing the developed model and standalone methods. The humanoid robot achieves the target in all scenarios, but the FA‐tuned PRM technique is advantageous to this purpose, as shown by the convergence curve, route length, and travel duration. Multiple humanoid robot navigation has an additional self‐collision issue, which is eliminated by employing a dining philosopher controller as the base technique. In addition, the proposed controller is evaluated in contrast to the existing technique. The developed strategy ensures effectiveness and efficacy depending on these findings.

Abstract Over the last few decades, the agricultural industry has made significant advances in autonomous systems, such as wheeled robots, with the primary objective of improving efficiency while reducing the impact on the environment. In this context, determining a path for the robot that optimizes coverage while taking into account topography, robot characteristics, and operational requirements, is critical. In this paper, we present H‐CCPP, a novel hybrid method that combines the comprehensive coverage benefits of our previous approach O‐CCPP with the computational efficiency of the Fields2Cover algorithm. Besides optimizing coverage area, overlaps, and overall travel time, it significantly improves the computation process, and enhances the flexibility of trajectory generation. H‐CCPP also considers terrain inclination to address soil erosion and energy consumption. In an effort to support this innovative approach, we have also created and made available a public data set that includes both 2D and 3D representations of 30 agricultural fields. This resource not only allows us to illustrate the effectiveness of our approach but also provides invaluable data for future research in complete coverage path planning (CCPP) for modern agriculture.