Coverage path planning (CPP) serves as the basis for a variety of practical robotic applications. The use of multiple robots can significantly improve coverage efficiency and enhance system robustness. However, most multi-robot CPP (MCPP) approaches face challenges such as unbalanced task allocation and reduced efficiency caused by redundant invalid paths. To address these issues, this paper proposes a heuristic MCPP method that incorporates elastic balance (EB) mechanism and a dynamic programming (DP) mechanism. The EB mechanism models the path-splitting problem of a loop as an elastic adjustment process. By identifying the state of elastic equilibrium, it determines the optimal segmentation, thereby effectively balancing the workload among robots. The DP mechanism models the problem of minimizing round-trip charging as a dynamic programming problem and searches the entire solution space to minimize the cost of non-working paths. Comparative experiments demonstrate that the proposed method achieves the minimum coverage time in simulation environments and real-world scenarios.
Patients confined to bed for extended periods are highly susceptible to pressure injury, which can lead to severe complications and prolonged hospitalization. The redistribution of pressure through continuous repositioning of the patient’s body is essential to mitigate this risk and ensure that excessive pressure is not sustained on any specific body part. In a previous study, the authors proposed a robotic bed capable of pressure redistribution; however, it lacked the patient side-lying positioning capability recommended for preventing pressure injury. In this study, we propose an improved robotic bed designed to prevent pressure injury. The developed prototype consists of 10 independently driven segments and incorporates 70 pressure sensors, with each segment having a lifting capacity of up to 60 kg. The segments are powered by linear electric actuators, and the driving mechanism combines a scissor mechanism and a pin-in-slot mechanism, enabling not only linear motion but also rotational motion to facilitate pressure redistribution in the supine position and to reposition the patient into a side-lying position. Kinematic analysis of the proposed mechanism was performed, and experiments with the developed prototype achieved a linear displacement of 89.5 mm and a rotation angle of 19.2°, thereby experimentally verifying its ability to achieve pressure redistribution and side-lying positioning. If its clinical efficacy is demonstrated through future clinical trials, the proposed robotic bed may provide a technological basis for future pressure-injury prevention strategies, subject to clinical validation.
Simultaneous localisation and mapping (SLAM) forms the foundation of autonomous perception and navigation in mobile robots. In geometrically degraded scenarios such as tunnels, corridors and open areas, traditional laser SLAM is prone to reduced accuracy or tracking failure due to insufficient geometric constraints. To mitigate this issue, this paper introduces LiDAR return intensity information into the LiDAR-inertial odometry framework, establishing joint constraints between geometric residuals and intensity photometric residuals. First, reliable image patches are selected based on intensity confidence and geometric complementarity; subsequently, a reliability-driven adaptive residual fusion strategy is employed within an Iterative Extended Kalman Filter (IEKF). The adaptive weights are estimated jointly from geometric degradation indicators, photometric confidence, and a robust reliability term, rather than being determined directly by the magnitude of the residuals. Consequently, large residuals arising from outliers or model mismatches are suppressed by a robust kernel and are not misclassified as highly reliable observations. Experiments on public datasets demonstrate that this method improves trajectory accuracy and estimation stability in typical geometric degradation scenarios.
This paper presents a feasibility-oriented study on the implementation of a coordinated dual-robot workflow using two legacy KUKA industrial robots (KR16 and KR5) for CNC-style low-load machining tasks. The workflow covers G-code generation from a CAD model, translation into KRL via a custom converter, and dual-robot coordination through a centralized RS-232 communication interface. The KR16 executes Cartesian tool motions, whereas the KR5 provides the rotary B-axis, emulating a 4-axis CNC arrangement on legacy KR-C2 controllers. Experimental trials were conducted on floral foam, and shape fidelity was evaluated using a 2D image-based DiffMap similarity between toolpath-derived reference renderings generated from the corresponding G-code and photographs of the machined parts, yielding 75.00% for the Don Bosco bust and 79.13% for the decorative owl. The results support the practical feasibility of coordinated dual-robot toolpath execution under low-load conditions, but they should not be interpreted as CNC-level machining accuracy or metrology-grade dimensional validation. Repeatability assessment, 3D metrology, and higher-load machining validation remain future work.
Human–robot interaction is an important research content in the robot field. In order to achieve the compliance and cooperation of human–robot interaction process, variable parameter interaction control and contact collision response of human-redundant robot are studied. Firstly, the kinematics model of redundant robot is established, and an inverse kinematics algorithm based on the augmented Jacobian matrix is proposed with the optimization objective of avoiding joint limit. Then, the admittance control model of human–robot interaction is established, and variable admittance control based on force and velocity is proposed. The contact intention of human is judged by the vector product relationship between external force and velocity. The control parameters are adjusted to better ensure the friendliness of human–robot cooperation. When the external force is applied to robot, based on human–robot interaction control law, robot motion is controlled, and the robot can move in the direction of the force, so the goal of human–robot compliance interaction is realized. When the external force is suddenly withdrawn, by adding the stiffness coefficient, the robot moves along the direction of the force and then can return to the position where the previous force is applied, achieving the safety response to robot's contact collision.
