Search Results (5)

To address the issue of unbalanced path allocation in multi-UUV cooperative operations under inhomogeneous ocean environments during full-coverage search missions, this paper proposes a heterogeneous UUV path planning method that integrates a hybrid waypoint deployment strategy with a dual-chromosome co-evolutionary genetic algorithm. First, heterogeneous UUVs are adaptively assigned to sub-regions according to the search value of the sea area, and a combination of Poisson sampling and Voronoi iterative refinement is adopted to complete the layout of detection points. Subsequently, connectivity-constrained K-means clustering is introduced to decompose the multi-traveling salesman problem (MTSP) into several independent TSP sub-problems. Finally, a dual-chromosome encoding scheme for task sequences and split points is designed, and a penalty matrix is incorporated into the fitness function to account for obstacle avoidance constraints, thereby establishing an integrated genetic-algorithm-based solution framework that incorporates both decomposition and obstacle avoidance. Simulation results demonstrate that the proposed method reduces the number of planned detection points by 12.4%, 12.8%, and 9.3% compared with baseline methods in circular, rectangular, and irregular sea areas, respectively, while the optimal path lengths are shortened by 5.8%, 4.5%, and 5.9%. Moreover, the cooperative mission time with four UUVs is reduced by 73.9%, 72.7%, and 71.2% relative to a single UUV, demonstrating an approximately linear speedup relative to the number of UUVs. Convergence analysis and extended experiments on 15 instances further confirm the algorithm’s solution stability and robustness under varying regional scales, shapes, and obstacle configurations. These results validate that the proposed approach not only reduces the number of deployment points and path cost, but also effectively balances obstacle avoidance and multi-robot load distribution. Full article

This study presents a simulation-based evaluation of a wall-following and mapping framework for autonomous underwater vehicles (AUVs) equipped with a single-line laser, targeting structured environments such as rectangular tanks and dam interiors. A hardware-in-the-loop (HIL) simulation platform is developed to integrate sensor emulation, vehicle dynamics, and image-based control while preserving the onboard data formats, update rates, and communication protocols of the AUV system. Using a single camera–laser pair, the framework estimates yaw angle and lateral wall distance from laser image geometry to support real-time wall-following and frontal obstacle avoidance. Wall mapping is performed by transforming laser image features into spatial coordinates and estimating the dimensions of geometric protrusions. The framework is evaluated on simulated walls with protruding features under two navigation conditions: ideal-motion and dynamic-control operation. Simulation results show stable wall-following performance, with lateral distance errors typically below 0.1 m. Under ideal-motion conditions, mapping errors range from 1% to 13%, while under dynamic-control navigation they increase to 10–35% due to attitude fluctuations and control-induced motion. Frontal obstacle avoidance maintains a minimum clearance of 1.04 m. The results demonstrate the feasibility of using a single-line laser and a unified image stream for both real-time wall-following control and post-mission geometric mapping within the defined simulation conditions. While the evaluation is limited to simulation and assumes idealized optical conditions without modeling hydrodynamic disturbances or optical degradation effects, the framework provides a system-level reference for laser-guided inspection strategies in confined underwater environments such as tanks, reservoirs, and dams. Full article

Trajectory planning for autonomous vehicles is essential for ensuring driving safety, passenger comfort, and operational efficiency. Collision avoidance constraints introduce significant computational complexity due to their inherent non-convex and nonlinear characteristics. Previous research has proposed the drivable corridor (DC) method, which transforms complex collision avoidance constraints into linear inequalities by constructing time-varying rectangular corridors within the spatiotemporal domains, thereby enhancing optimization efficiency. However, the DC construction process involves repetitive collision detection, leading to an increased computational burden. To address this limitation, this study proposes a novel approach that integrates grid-based obstacle representation with dynamic grid merging to accelerate collision detection and dynamically constructs the DC by adaptively adjusting the expansion strategies according to available spatial dimensions. The feasibility and effectiveness of the proposed method are validated through simulation-based evaluations conducted over 100 representative scenarios characterized by diverse and unstructured environmental configurations. The simulation results indicate that, with appropriately selected grid resolutions, the proposed approach achieves up to a 60% reduction in trajectory planning time compared to conventional DC-based planners while maintaining robust performance in complex environments. Full article

This paper proposes a multi-map path planning approach for rescue robots to address the challenges posed by complex obstacle information, high uncertainty, and the difficulties in mine disaster scenarios. Based on multiple possible environmental maps, each with associated subjective probabilities derived from prior knowledge and expert estimations, a mathematical model for multi-map path planning in mine disaster rescue scenarios is developed. An improved hybrid algorithm combining ant colony optimization (ACO) and genetic algorithm (GA) is then proposed to solve the established model. In the hybrid approach, the improved ACO is employed to overcome the limitations of traditional genetic algorithms, such as poor initial population quality, slow convergence, and suboptimal results. Additionally, a grid-based, rectangular-area, obstacle avoidance strategy is incorporated to precisely evaluate the obstacle avoidance path of each individual across different obstacle maps. Finally, the feasibility and effectiveness of the proposed hybrid algorithm are validated through simulations involving both single and multiple mine disaster maps. The results demonstrate the potential of the proposed approach for solving robot path optimization problems in complex multi-environment scenarios. Full article

The development of agricultural farming has evolved from traditional agricultural machinery due to its efficiency and autonomy. Intelligent agricultural machinery is capable of autonomous driving and remote control, but due to its limited perception of farmland and field obstacles, the assistance of unmanned aerial vehicles (UAVs) is required. Although existing intelligent systems have greater advantages than traditional agricultural machinery in improving the quality of operations and reducing labor costs, they also produce complex operational planning problems. Especially as agricultural products and fields become more diversified, it is necessary to develop an adaptive operation planning method that takes into account the efficiency and quality of work. However, the existing operation planning methods lack practicality and do not guarantee global optimization because traditional planners only consider the path commands and generate the path in the rectangular field without considering other factors. To overcome these drawbacks, this paper proposes a novel and practical collaborative path planning method for intelligent agricultural machinery based on unmanned aerial vehicles. First, we utilize UAVs for obstacle detection. With the field information and operation data preprocessed, automatic agricultural machinery could be assisted in avoiding obstacles in the field. Second, by considering both the historical state of the current operation and the statistics from previous operations, the real-time control of agricultural machinery is determined. Therefore, the K-means algorithm is used to extract key control parameters and discretize the state space of agricultural machinery. Finally, the dynamic operation plan is established based on the Markov chain. This plan can estimate the probability of agricultural machinery transitioning from one state to another by analyzing data, thereby dynamically determining real-time control strategies. The field test with an automatic tractor shows that the operation planner can achieve higher performance than the other two popular methods. Full article