Blind search methods are widely used in the search phase of robotic peg-in-hole assembly. However, these methods are sensitive to the initial contact position and are time-consuming. Additionally, in scenarios with a limited search space, such as thin-walled holes, the hole search process is prone to failure due to loss of contact between the peg and the hole. To address this issue, this paper proposes a hole search strategy that uses visual detection of the contact state between the peg and the hole and peg position adjustment based on the detection results. First, a contact-state classification model under fixed single-view conditions is constructed, converting the disordered relative positional relationships between the peg and the hole into discrete contact states, laying a theoretical foundation for the vision-guided hole search process. Second, search trajectories are generated based on the contact states, and trajectory optimization is further performed through visual error analysis, thereby overcoming hole search failures caused by visual occlusion and camera mounting deviations. Finally, convergence analysis shows that the peg slides into the hole when the classification accuracy is greater than 50%. Robotic assembly experiments further demonstrate the effectiveness of the proposed contact-state classification model and search strategy in tasks such as thin-walled hole insertion and USB Type-A(USB-A) connector insertion.

Odor source localization (OSL) in complex industrial environments remains a significant challenge due to the coupled effects of building occlusion and turbulent advection, which often lead to pseudo-source stagnation. This paper proposes an adaptive collaborative Genghis Khan shark optimizer (ACGKSO), a unified search framework designed for robust multi-robot OSL. Specifically, an adaptive perturbation strategy is introduced to balance global exploration and local exploitation. This is achieved through a dynamic step size mechanism driven by two factors: an exponentially decaying convergence rate and a linearly amplified term based on swarm dispersion. Concurrently, to counter premature convergence in complex environments, we introduce a dual-modal experience sharing mechanism. This approach enhances collective learning by integrating both individual historical best solutions and neighborhood optima. Experimental results validate the effectiveness and robustness of the proposed approach compared with other swarm intelligence methods. In single-interference scenarios, ACGKSO achieves a localization success rate of 94%, significantly outperforming the baseline algorithms. In more complex multi-interference environments, it maintains an 83% success rate and reduces the average number of search steps by 21.1% relative to the standard GKSO. These results demonstrate that our framework significantly outperforms other SI methods in terms of accuracy and efficiency across different environmental conditions.

The 4-DOF (four degrees of freedom) parallel manipulator, namely 2PPaRR-2PPaRU, offers three translations and one rotation capabilities, boasting an extensive rotation range, and a regular workspace. Moreover, its workspace can infinitely extend in conjunction with the extension of the driving joints. Nonetheless, the symmetric structure relying on Pa joint design presents performance challenges in the initial configuration and complicates the analytical solution of the forward kinematics, thereby increasing the complexity of dynamics and control. To address these limitations, the structure of the moving platform is modified by coaxially connecting the three joints at the ends of the three limbs to the moving platform. This improvement is followed by analyses of mobility, motion/force transmissibility, and stiffness. The overall reachable workspace area, good transmission workspace ratio, and global stiffness index are then chosen as key objectives for dimensional optimization, thereby achieving satisfactory performance for the new 3T1R PM. Finally, the CAD model with the selected parameters is presented, together with the distributions of motion/force transmission index and virtual stiffness index within the reachable workspace. The results validate the effectiveness of the optimization design.

Pipeline robots represent intelligent systems engineered for confined and complex pipeline environments. They feature compact and flexible architectures, diverse locomotion modalities, and a high degree of functional integration. These systems have been widely applied in industrial equipment inspection, energy infrastructure maintenance, and the management of urban underground utilities. This review systematically analyzes the technological characteristics and recent advancements in pipeline robotics, with a focus on drive mechanisms and motion control strategies. It presents the evolution and comparative analysis of three primary drive architectures: passive-driven, self-driven, and compound-driven, with representative applications, advantages, and limitations. In addition, an integrated research framework is proposed that encompasses energy efficiency management, drive systems, adaptive mechanisms, perception, navigation, and intelligent adaptive control to address critical challenges in environmental adaptability, energy efficiency, and autonomous task execution. Finally, the review outlines emerging trends and unresolved technical bottlenecks, providing theoretical insights and practical guidance to support ongoing innovation and real-world deployment of pipeline robotic systems.

Current geometric-based path-planning methods for vertical parking in automated parking systems face issues such as stringent initial position requirements, insufficient clearance from garage boundaries, and discontinuous curvature. To address these problems, this paper proposes a parking path-planning method that integrates the Reeds–Shepp curve with an improved particle swarm optimization (PSO) algorithm. The parking process is decomposed into forward and backward segments, each generated using the Reeds–Shepp curve to enhance flexibility. The shift point, defined as the transition between the two segments, serves as the decision variable in the PSO framework. The fitness function jointly considers path length and deviation between the target pose and garage entry. A quadratic Bézier curve and a nonlinear adaptation mechanism are incorporated into the PSO to determine the optimal shift point, mitigating the risk of proximity to garage boundaries. The initial path is then generated using the Reeds–Shepp curve based on the initial pose, shift point, and target pose. After discretization, the path points are optimized via a nonlinear optimization formulation to eliminate curvature discontinuities at segment junctions. Experimental results demonstrate that the proposed method generates feasible, continuous-curvature parking paths across a variety of initial poses, outperforming conventional approaches in terms of flexibility and clearance.

It has always been expected that robots can actively manipulate complex environments to fulfill human requirements. This process typically necessitates that the robot be equipped with the ability for embodied exploration and manipulation. To achieve this goal, in this paper, we propose to incorporate multi-source knowledge to enhance the ability of robotic embodied exploration and manipulation. Specifically, to eliminate the inherent biases in decision-making of large language models (LLMs), we introduce a multi-source knowledge fusion module to generate more reasonable exploration sequences. Notably, grasping detection plays a critical role in the process of robot manipulation. To achieve a better balance between the accuracy and efficiency of the grasping detection network, we design a two-branch feature fusion module with residual blocks to improve network performance. Conditioned on the aforementioned innovations, the robot is capable of actively exploring and manipulating in constrained environments to meet human requirements. Extensive experiments are conducted in both simulation and real-world environments. The results demonstrate the effectiveness and efficiency of our proposed framework.

Multi-robot coverage path planning (MCPP) is a typical problem in multi-robot applications. Its goal is to decompose and assign coverage tasks, enabling mobile robots to collaboratively perform complete coverage of the map within a short time (referred to as coverage time). The coverage time of MCPP is generally determined by the path lengths of the robots and the number of turns on the paths. Most existing studies focus on only one of the two metrics, and few address the simultaneous optimization of both aspects. In this letter, we propose the Divide Areas based on area Growing via Wavefront Propagation (DAWG) algorithm to solve this problem through a staged co-optimization approach. Specifically, DAWG combines area growing and wavefront propagation to divide the map into equally sized subareas, ensuring equal path lengths. Meanwhile, DAWG incorporates a greedy strategy that reduces computation time by 81% while maintaining turn counts comparable to TMSTC*, a state-of-the-art (SOTA) method that minimizes path turns. Experiments on dozens of large-scale map instances demonstrate that DAWG achieves superior performance in both computation time and path quality compared to existing methods.

In grid-based robotic path planning, traditional A* algorithms are often constrained by severe path redundancy, low search efficiency, and poor adaptability to complex environments with varying obstacle densities. To overcome these limitations, this article proposes the Hierarchical Adaptive Neighborhood Search A* (HANSA*) algorithm, which integrates a dual adaptive search mechanism with local environmental complexity evaluation. This mechanism enables dynamic switching between a 16-neighborhood pattern for rapid global exploration and a directional 5-neighborhood pattern for localized refinement based on real-time planning progress and local obstacle density. Phase-specific heuristic functions are introduced to balance search efficiency, path quality, and trajectory smoothness across different planning stages. Furthermore, a two-stage path optimization strategy is employed to reduce path redundancy, remove unnecessary waypoints, and alleviate sharp corners. Experimental results in dense and complex environments demonstrate that the HANSA* algorithm outperforms conventional approaches, exhibiting improved adaptability and robustness under varying obstacle-density distributions while generating paths with lower redundancy and fewer sharp corners. The results also show that HANSA* can replan paths after newly detected obstacles are incorporated into the updated occupancy grid map. These findings confirm the effectiveness of HANSA* for grid-based global path planning of mobile robots in complex environments.

This article presents a novel framework for Multi-Robot Task Allocation (MRTA) in smart warehouses by using metaheuristic approaches, namely Multi-Objective Variable Neighborhood Search with Adaptive Intensification and Diversification (MOVNS-AINS) and Non-dominated Sorting Genetic Algorithm II (NSGA-II), to optimize task distribution. The proposed approach addresses the challenge of coordinating multiple mobile robots in warehouse logistics, where tasks such as item transportation, storage, and delivery must be continuously assigned under spatial and temporal constraints. Traditional heuristic methods focus on single-objective optimization. Besides, they often struggle to maintain solution quality when the number of tasks and agents increases or when the environment changes dynamically. In contrast, we propose an MRTA optimization framework to address task allocation in structured warehouse environments with heterogeneous robots and dynamic constraints with a multi-objective strategy considering travel distance, workload distribution, and execution time. The proposed framework is validated in a simulated warehouse environment using NVIDIA Omniverse Isaac Sim, demonstrating scalability and adaptability to dynamic task assignments in industrial logistics and manufacturing environments.