Pose estimation is a particularly important link in the task of robotic bin-picking. Its purpose is to obtain the 6D pose (3D position and 3D posture) of the target object. In real bin-picking scenarios, noise, overlap, and occlusion affect accuracy of pose estimation and lead to failure in robot grasping. In this paper, a new point-pair feature (PPF) descriptor is proposed, in which curvature information of point-pairs is introduced to strengthen feature description, and improves the point cloud matching rate. The proposed method also introduces an effective point cloud preprocessing, which extracts candidate targets in complex scenarios, and, thus, improves the overall computational efficiency. By combining with the curvature distribution, a weighted voting scheme is presented to further improve the accuracy of pose estimation. The experimental results performed on public data set and real scenarios show that the accuracy of the proposed method is much higher than that of the existing PPF method, and it is more efficient than the PPF method. The proposed method can be used for robotic bin-picking in real industrial scenarios.

Hand-eye calibration is a critical step for camera-robot systems. Addressing the problems of low accuracy in hand-eye calibration, the paper introduces a method to obtain the transformation matrix based on data filtering and parameters optimization. In constructing the calibration equation system, certain equations exhibit larger errors due to factors such as the angles, positions, and noise when the camera captures the calibration board. Additionally, following the acquisition of the initial calibration solution, the traditional methods of optimizing calibration parameters frequently based on two-dimensional. However, the calibration results need to ensure the accuracy of the visual-guided robotic arm in 3D space, which leads to a discrepancy between the optimization objective and the actual requirements. Based on this, the proposed method can be divided into two steps. Firstly, the calibration equations are filtered using Random Sampling Consistency (RANSAC) to reduce the calibration data collection error. Subsequently, the spatial distance error serves as a constraint to iteratively optimize the calibration parameters, and the error is used as an evaluation metric for calibration quality, thereby enhancing calibration accuracy further. Finally, simulation and real experiments were conducted to validate the precision and robustness, and the calibration results were applied in robotic arm pick-and-release operation to evaluate the practical application effectiveness. Experimental results show that the proposed method has higher accuracy and stronger robustness compared with other traditional methods.

Research on helicopter dispatching has received considerable attention, particularly in relation to post-disaster rescue operations. The survival chances of individuals trapped in emergency situations decrease as time passes, making timely helicopter dispatch crucial for successful rescue missions. Therefore, this study investigates a collaborative helicopter rescue routing problem with time window and limited survival time constraints, solving it using an improved iterative greedy (IIG) algorithm. In the proposed algorithm, a heuristic initialization strategy is designed to generate an efficient and feasible initial solution. Then, a feasible-first destruction-construction strategy is applied to enhance the algorithm’s exploration ability. Next, a problem-specific local search strategy is developed to improve the algorithm’s local search effectiveness. In addition, the simulated annealing (SA) method is integrated as an acceptance criterion to avoid the algorithm from getting trapped in local optima. Finally, to evaluate the efficacy of the proposed IIG, 56 instances were generated based on Solomon instances and used for simulation tests. A comparative analysis was conducted against six efficient algorithms from the existing studies. The experimental results demonstrate that the proposed algorithm performs well in solving the post-disaster rescue helicopter routing problem.

Following natural disasters, emergency rescue route planning becomes substantially more challenging due to damaged transportation networks, urgent rescue demands, and the operational complexity of heterogeneous rescue resources. Efficient rescue decision-making under constraints including time windows, regional accessibility, and casualty survival risk is therefore of considerable practical significance. However, existing studies often suffer from inadequate modeling of casualty survival dynamics, excessive reliance on manually designed operator control strategies, and limited search robustness in highly constrained environments. To address these challenges, this paper proposes a reinforcement learning-assisted iterated greedy algorithm for heterogeneous rescue and route planning, termed Dueling Double Deep Q-Network Iterated Greedy (D3QNIG). First, a disaster rescue routing optimization model integrating casualty evacuation and relief material distribution is developed, where an injury-adaptive survival-risk mechanism is incorporated alongside capacity, time-window, andaccessibility constraints. Second, a reinforcement learning-assisted iterated greedy framework is designed to adaptively control operator selection and destruction intensity during the destruction, reconstruction, and local search stages. Third, a state representation approach based on set attention pooling is constructed for variable-length and heterogeneous routing solutions, together with a hierarchical Dueling Double DQN decision architecture to enhance policy-learning stability and search effectiveness. Finally, a two-stage reward–acceptance decoupling strategy is introduced to alleviate the credit assignment issue and improve training stability. Experimental results demonstrate that the proposed D3QNIG achieves superior solution quality, faster convergence, and stronger robustness than benchmark methods, indicating its effectiveness for heterogeneous rescue route optimization in complex disaster scenarios.