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This study proposes an enhanced multi-agent swarm control algorithm (EN-MASCA) to solve the problem of efficient patrolling of drone swarms in complex durian orchard environments. It introduces a virtual navigator model to dynamically adjust the patrol path of the drone swarm and perform obstacle avoidance and path optimization in real time according to environmental changes. Different from traditional algorithms that only rely on fixed path planning, the virtual navigator model significantly improves the flexibility and stability of the drone swarm in complex environments. It also applies deep reinforcement learning algorithms to path planning and obstacle avoidance of drone swarms for the first time, improving the algorithm’s adaptability and optimization capabilities by learning dynamic information in complex environments. This innovation significantly improves the applicability of existing methods in complex terrain and dynamic obstacle environments. Finally, it incorporates the simulation characteristics of biological swarm behavior, and on this basis, comprehensively optimizes the flight path, obstacle avoidance and swarm stability of the drone swarm. By improving control strategies and parameter design, it improves the trajectory consistency and mission completion efficiency of the UAV swarm during flight. In the experimental part, this study verified in detail the advantages of the EN-MASCA algorithm in terms of flight trajectory, flight stability, cluster consistency and task completion efficiency by constructing a six-degree-of-freedom UAV motion simulation model and real environment simulation. It provides an efficient and intelligent solution for collaborative patrol operations of drones in durian orchards, which has important practical application value and promotion prospects.

This paper proposes a hybrid algorithm to complete path planning and dynamic obstacle avoidance in complicated maps for mobile robot. The hybrid algorithm (A*-DWA-B) combines the advantages of A* algorithm and Dynamic Window Approach (DWA). Firstly, methods of environmental modeling and collision detection are set. The improvement of A* algorithm lies in the establishment of a new calculation method for the evaluation function. After adding the risky cost, the parent node information is introduced into the calculation of the estimated cost, and the influence of the robot starting and braking modes is added to the calculation of the actual cost. Secondly, after removing superfluous nodes, the path obtained by the improved A* algorithm is divided into several linear segment paths. Then the endpoints of each line segment path are taken as the start node and target node of DWA for path planning. Adaptive initial attitude is set and two dynamic obstacle avoidance strategies are added for DWA. After integrating the paths planned by DWA, the B-spline smoothing method is used to optimize the integrated path, and finally obtained a smooth path. Compared with other similar algorithms, the proposed algorithm has advantages in path cost and turning angle. Experimental results show that the hybrid algorithm not only has strong ability of safe and smooth path planning, but also can avoid dynamic obstacles in time and effectively.

To improve the path optimization effect and search efficiency of ant colony optimization (ACO), an improved ant colony algorithm is proposed. A collar path is generated based on the known environmental information to avoid the blindness search at early planning. The effect of the ending point and the turning point is introduced to improve the heuristic information for high search efficiency. The adaptive adjustment of the pheromone intensity value is introduced to optimize the pheromone updating strategy. A variety of control strategies for updating the parameters are given to balance the convergence and global search ability. Then, the improved obstacle avoidance strategies are proposed for dynamic obstacles of different shapes and motion states, which overcome the shortcomings of existing obstacle avoidance strategies. Compared with other improved algorithms in different simulation environments, the results show that the algorithm in this paper is more effective and robust in complicated and large environments. On the other hand, the comparison with other obstacle avoidance strategies in a dynamic environment shows that the strategies designed in this paper have higher path quality after local obstacle avoidance, lower requirements for sensor performance, and higher safety.

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.

Cooperative motion control is a fundamental requirement for unmanned underwater vehicle (UUV) swarms operating in complex marine environments. Conventional swarm motion-control algorithms may suffer from limited convergence efficiency and redundant obstacle-avoidance maneuvers when the swarm is required to move toward multiple task-related regions. To address these issues, this study proposes a Vicsek-based distributed motion-control framework with static summoning points and threat-selective obstacle avoidance. First, static summoning points are introduced as predefined task-attraction locations, and a movement-cost-based assignment rule is used to divide the initially mixed swarm into task-oriented subclusters. Under a limited field-of-view constraint, a summoning factor is incorporated into the heading-update rule to balance local neighbor alignment and directional guidance toward the assigned summoning point. Then, an obstacle-avoidance strategy is developed by considering both the relative position of obstacles and the velocity direction of individuals. The detected obstacles are classified as current obstacles or potentially threatening obstacles, and avoidance maneuvers are triggered only when a current obstacle lies within the prescribed safety distance. Simulation results demonstrate that the proposed VSSPAO framework can improve convergence consistency, reduce convergence time, and decrease redundant obstacle-avoidance routes compared with the reference algorithms. The proposed method provides an interpretable and computationally simple distributed coordination mechanism for UUV swarm segmentation, task-oriented aggregation, and obstacle avoidance.

UAV formation navigation in complex environments such as narrow tunnels faces multiple challenges, including obstacle avoidance, formation maintenance, and communication constraints. This paper proposes a cooperative obstacle avoidance strategy for UAV formation based on adaptive event-triggered impulse control, achieving efficient navigation under limited resources. The strategy comprises four key modules: an adaptive event-triggering mechanism, optical flow-based obstacle detection, leader–follower formation structure, and dynamic communication topology management. The adaptive event-triggering mechanism dynamically adjusts triggering thresholds, ensuring control accuracy while reducing control update frequency; the enhanced optical flow perception model improves obstacle recognition ability through a sector-based approach, incorporating tunnel-specific avoidance strategies; the leader–follower formation structure employs dynamic weight allocation to balance obstacle avoidance needs with formation maintenance; and communication topology optimization enhances system robustness under limited communication conditions. Simulation experiments were conducted in an arc-shaped tunnel environment with 15 randomly distributed obstacles, and the results demonstrate that the proposed method significantly improves collision rates, formation errors, and communication overhead compared to traditional methods. Lyapunov stability analysis proves the convergence of the proposed control strategy. This research provides new theoretical and practical references for multi-UAV cooperative control in complex narrow environments.

In recent years, with the improvement of computers, automation, and communication technologies, autonomous driving has developed rapidly and has become a research hotspot in transportation. In order to optimize the existing autonomous driving scheme, this paper investigates the key technologies in 5G-based Telematics autonomous driving, mainly including the millimeter wave communication method and automatic obstacle avoidance strategy design, and tests and analyzes them through simulation experiments. In the simulation experiment, the synchronization rate of rear vehicle 1 of lane is 97.56%, that of rear vehicle is 98.43%, and that of rear vehicle is 97.82%, with an average synchronization rate of 97.94%. The synchronization rates of rear vehicle , and of lane 2 are 98.27%, 97.84%, and 96.89%, respectively, with an average synchronization rate of 97.67%. For the local observation latency in Telematics, the 5G Big Data-based scheme reduces 10.22% on average compared to the F-DDQL scheme and 9.76% on average compared to the IF-DDQL scheme. Regarding system latency, the 5G Big Data-based scheme reduces 8.67% and 9.21% on average compared to the other two schemes, respectively. The 5G Big Data-based Telematics autopilot can significantly improve the synchronization rate of vehicles and effectively reduce network latency. The research on the key technologies of 5G big data-based connected vehicle autonomous driving in this paper can overcome the shortcomings of traditional autonomous driving technology with unstable networking and help reduce the reliance on high-precision sensors, thus further improving autonomous driving performance.

To address the shortcomings of traditional bionic algorithms in path planning, such as inefficient search processes, extended planning distances and times, and suboptimal dynamic obstacle avoidance, this paper introduces a fusion algorithm called NRBO-DWA. This algorithm is specifically applied to plan the path for a tube-changing robot in a knitting workshop. The process begins with spatial modeling based on the actual parameters of the workshop, followed by the development of a comprehensive, objective function for the robot in line with the relevant constraints. The NRBO algorithm is then integrated with the DWA algorithm to boost its dynamic obstacle avoidance capabilities, while a path correction mechanism is introduced to minimize unnecessary detours. Finally, a comparative experiment is designed to evaluate the algorithm against the GA, PSO, and SSA algorithms. Simulation results demonstrate that in a dynamically complex 3D environment, the NRBO-DWA algorithm outperforms in terms of higher 3D search efficiency, shorter total path length, and faster planning times.

Focusing on the collision avoidance problem for Unmanned Surface Vehicles (USVs) in the scenario of multi-vessel encounters, a USV autonomous obstacle avoidance algorithm based on the improved velocity obstacle method is proposed. The algorithm is composed of two parts: a multi-vessel encounter collision detection model and a path re-planning algorithm. The multi-vessel encounter collision detection model draws on the idea of the velocity obstacle method through the integration of characteristics such as the USV dynamic model in the marine environment, the encountering vessel motion model, and the International Regulations for Preventing Collisions at Sea (COLREGS) to obtain the velocity obstacle region in the scenario of USV and multi-vessel encounters. On this basis, two constraint conditions for the motion state space of USV obstacle avoidance behavior and the velocity obstacle region are added to the dynamic window algorithm to complete a USV collision risk assessment and generate a collision avoidance strategy set. The path re-planning algorithm is based on the premise of the minimum resource cost and uses an improved particle swarm algorithm to obtain the optimal USV control strategy in the collision avoidance strategy set and complete USV path re-planning. Simulation results show that the algorithm can enable USVs to safely evade multiple short-range dynamic targets under COLREGS.

This paper investigates a novel hybrid gait obstacle-avoidance control strategy based on a perception system for the six wheel-legged robot (BIT-6NAZA) in uneven terrain. This robot has stronger payload transportation performance benefited from the flexibility of the 6-degree of freedom Stewart platform. It can guarantee the attitude level stability when passing through different shapes of obstacles. Firstly, the motion state matrix and gait unit of the BIT-6NAZA robot are considered. Moreover, the current local terrain is identified by the visual perception system. Then the wheel-legged hybrid gait types and parameters are selected according to the terrain detection. The gait topology matrix and gait planning matrix are generated for each leg controller to realize the wheel-legged hybrid obstacle-avoidance. In addition, a feedback controller combined with the posture sensor and foot-end force sensor is utilized to maintain the robot body. Finally, some demonstrations using the developed BIT-6NAZA robot are carried out. The performance illustrates the effectiveness and feasibility of the hybrid gait obstacle-avoidance control strategy.