Multi-robot coverage path planning (MCPP) is a key technology in the field of precision agriculture, providing fundamental support for autonomous agricultural vehicles and unmanned aerial vehicles (UAVs) to perform tasks such as crop monitoring, pesticide spraying, and seeding. Although existing MCPP methods can generate feasible coverage paths, they usually do not consider the nonholonomic kinematic constraints on robots during the planning stage but instead rely on post-processing methods to smooth the paths. This makes it difficult to evaluate the true costs of paths and easily causes an imbalance in task allocation, thereby prolonging coverage time. Therefore, this article studies the coverage path planning (CPP) problem of multiple curvature-constrained Dubins robots in agricultural scenarios. Firstly, inspired by the Generalized Traveling Salesman Problem, we propose an exact mathematical model to solve the optimal coverage paths. This model is more concise than existing ones as it does not use additional endpoint continuity constraints. Based on this, we further propose a game-based Dubins multi-robot coverage path planning algorithm, which achieves load balancing through task negotiation among robots and reduces the maximum coverage time. Experiments conducted in various agricultural scenarios show that the proposed algorithm significantly improves the system load balancing and effectively reduces the overall coverage time. Specifically, compared with existing algorithms, the algorithm in this article reduces the coverage time by an average of $10.8\%$ and improves the load balance degree by approximately $47.4\%$. The feasibility tests also indicate that the generated paths are suitable for fixed-wing UAVs.

This paper presents a sensorless human-intention-based control framework for a differential-drive power-assist mobile robot for indoor cooperative transportation. The proposed method estimates interaction-consistent motion cues in the motion domain using wheel-encoder measurements and motor-side actuation information, without relying on dedicated force or torque sensors. An interaction observer is first used to extract an acceleration-like interaction cue from the discrepancy between the commanded robot motion and the measured robot response. This signal is then processed by a human-intent analysis module, in which encoder-derived motion features and statistical class modeling are used to distinguish representative human interaction patterns from disturbance-related motion variations. The resulting interaction-related signal is separated into estimated human-induced and disturbance-induced acceleration components. The human-induced component is converted into a compliant velocity command through a virtual impedance model, whereas the disturbance-induced component is used to construct a disturbance-compensation term in the power-assist controller. Experimental results obtained in indoor cooperative transport scenarios suggest that the proposed framework provides effective assistive control behavior while reducing sensitivity to environmental disturbances.

Reliable path planning is a significant requirement for humanoid robots to move around in an environment filled with obstacles. In this paper, a novel method for humanoid robot navigation is proposed based on the Adaptive Dragonfly Algorithm (aDA), which is an improvement over the traditional Dragonfly Algorithm. The aDA method provides a better trade-off between exploration and exploitation of the search space, thereby avoiding local optima and increasing diversity in the search process. The aDA approach has been implemented and evaluated in a range of Webots-based simulation and experimental environments. Its performance is assessed using both single and multiple humanoid robots to analyze navigation capabilities under static as well as dynamic conditions. In multi-robot scenarios, conflict situations arise since each robot can act as a moving obstacle for the others, to overcome such situation, the dining philosopher controller is integrated with the proposed technique. The results of the aDA method are compared with traditional heuristics and metaheuristics for different scenarios of obstacle densities. The results obtained from the virtual and real environments show a variation within 5%, which proves that the aDA method is a robust and intelligent solution for humanoid robot navigation in a real-time environment. Furthermore, the proposed controller is benchmarked against an existing navigation method, demonstrating its improved performance and effectiveness.

Mobile robots operating on soft, low-bearing terrain often suffer from sinkage, slip, steering difficulties, and particle intrusion into exposed transmission components. This study proposes an enclosed dual-screw robot chassis for soft-terrain locomotion. The chassis employs two independently driven screw wheels with built-in motors and sealed transmission components. A simplified propulsion-force analysis and a kinematic model were developed to describe the forward, backward, rotational, and differential-steering motions of the chassis. On this basis, a finite state machine (FSM)-based control strategy was designed to coordinate the two screw wheels, enable smooth transitions between motion modes, and provide a soft-stop function. A physical prototype was evaluated in loose sand in terms of speed regulation, travel speed, slip ratio, sinkage, and sealing performance. The screw-wheel rotational speed showed a strong linear relationship with the chassis travel speed, with a coefficient of determination of 0.9974. Within the tested speed range, the maximum slip ratio was approximately 3.6%, and the maximum sinkage was approximately 79 mm. After 30 min of continuous operation, no obvious sand intrusion was observed inside the enclosed screw-wheel cavity. These results provide preliminary evidence for the feasibility of the proposed chassis for soft-terrain locomotion, while further validation under broader terrain conditions is still required.

To address the difficulty of deploying complex grasping point detection methods in fabric unfolding and folding tasks for smart homes and automated production lines, this paper presents an RGB-based robotic grasping system for fabric corner recognition, targeting small- to medium-sized solid-color fabrics. For thin and deformable fabrics, ordinary depth cameras often provide unstable local depth measurements at lifted or folded corners, making precise corner height estimation difficult, while introducing depth sensing also increases hardware complexity and computational cost. Therefore, this work adopts an RGB-only perception setting and builds on a line segment detector (LSD)-enhanced line perception pipeline and a Line Segment Classification, Deletion, and Completion Tool (LS-CDCT) to recover task-relevant line structures and corner cues from 2D images. Based on the recovered line segment chains, a geometric fabric corner detection algorithm is developed for robust recognition of visible fabric corners. The detected corner points are further integrated into a single-arm robotic system with a corner-driven grasping strategy for automatic fabric corner grasping. Experiments were conducted on 160 fabric images containing 489 visible fabric corners under different postures, backgrounds, lighting conditions, image acquisition settings, and image sizes. The proposed method achieves a corner recognition rate of 92.84%. In 100 recognition-and-grasping trials, the overall corner-grasping success rate reaches 92.00%, and the grasping success rate conditioned on correct corner recognition reaches 100.00%. The results demonstrate the effectiveness of the proposed line perception and structural recovery framework for lightweight robotic fabric handling.

Industrial inspection often requires systems to pass through confined entrances and traverse loosely constrained paths before reaching target locations. Borescopes and long-reach continuum manipulators are susceptible to insertion-induced buckling when deployed without continuous lateral support. This work presents a modular extra-long continuum manipulator designed for such hybrid industrial environments. The system combines a self-recoverable passive body with an independently actuated dual-balloon stabilisation unit, allowing it to traverse regions without continuous lateral confinement and subsequently establish a stabilised lift-up configuration at the target location. Its modular architecture supports transport and field maintenance, while the low-cost disposable design reduces decontamination requirements in harsh or contaminated environments. A solver-free, closed-form framework combines ground, tip, wall-contact, and wrapping resistances to estimate insertion-induced buckling margins for deployment planning. Prototypes (24 mm diameter, 8.2 m length) were validated experimentally and deployed in a nuclear decommissioning hot cell at Nuclear Restoration Services (NRS) Dounreay, UK, carrying a borescope camera and a 70 g radiation sensor to support inspection for subsequent decommissioning decisions.

Greenhouse crop monitoring systems predominantly rely on sensor-based measurements of environmental conditions, such as temperature and humidity, while largely neglecting plant- and fruit-specific information. For delicate crops such as strawberries, this lack of plant-level perception limits the assessment of ripeness, spatial distribution, and localized growth conditions, which are essential for informed crop management. Enabling frequent, plant-specific visual and spatial monitoring therefore provides a necessary foundation for downstream tasks such as harvest planning and automated picking. To address this gap, this study presents a data collector robot (DCR) designed for plant-specific monitoring through a decoupled perception–action workflow. The proposed DCR integrates a red-green-blue-depth (RGB-D) camera, a robotic arm, and a rail-guided mobile platform to perform repeatable, multi-view image acquisition in greenhouse environments. Collected data are transmitted to a cloud server, where strawberry detection and localization are performed using a you-only-look-once (YOLO)-based model, and the processed results are stored for remote access and downstream use. This architecture enables accurate plant-level perception while avoiding the need for high-performance onboard computing. Experimental evaluations assess localization repeatability, robustness of cloud-based processing under variable network conditions, and continuous multi-plant operation. Results demonstrate a localization root-mean-square (RMS) of 0.39 mm, stable cloud-assisted inference with no dropped detections under simulated network impairments, and reliable performance across 100 complete data-collection cycles. A comparative analysis further shows that cloud-based processing offers a cost-effective alternative to local graphical processing unit (GPU)-based inference for scalable deployment. Overall, the proposed DCR provides a lightweight and modular solution for plant-level greenhouse monitoring and establishes a practical foundation for future autonomous strawberry harvesting systems.

This work presented a biomechanically informed model and analysis of a 5-degree-of-freedom shoulder exoskeleton, constructed on a spatial parallel mechanism with a universal-prismatic-spherical joint configuration. The primary aim of the shoulder exoskeleton mechanism was to ensure the human shoulder’s natural movements. The mechanism incorporated a proposed anatomical mapping strategy to align the directions of the mechanism linkage movements with the physiological lines of action of the muscles, thereby expected to acquire effective support in aligning the shoulder exoskeleton with the physiological muscle lines of action. To facilitate spatial functionality, forward kinematics were developed based on Newton-Raphson for estimating the shoulder platform based on the actuators’ inputs. The forward kinematics were validated through a closed-form inverse kinematics that provided the actuator parameters, demonstrating geometric consistency with the anticipated configurations. Additionally, the work included the velocity and acceleration analyses, utilizing the Jacobian-based rigid body kinematics to maintain motion continuity. The model was assessed using anthropometric parameters representative of the upper extremity dimensions and was evaluated across three reference poses: neutral, flexion, and abduction. Simulation outcomes revealed a similar spatial motion of the mechanism with that of the shoulder movement range, showing close biomechanical alignment based on the actuator response. Moreover, static force analysis confirmed the efficient distribution of loads among the actuator legs while bearing both limb weight and substantial promise for applications in assistive robotics technology and rehabilitation in the context of shoulder exoskeleton development.