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Unmanned Aerial Vehicle (UAV) path planning has increasingly become the key research point for civilian drones to expand their use and enhance their work efficiency. Focusing on offline derivative algorithms, represented by Rapidly-exploring Random Trees (RRT), are widely utilized due to their high computational efficiency. However, deploying these offline algorithms in complex and changing disaster environments presents its own drawbacks, such as slow convergence speed, poor real-time performance, and uneven generation paths. In this paper, the Artificial Potential Field -Improved Rapidly-exploring Random Trees (APF-IRRT*) path-planning algorithm is proposed, which is applicable to disaster relief UAV cruises. The RRT* algorithm is adapted with adaptive step size and adaptive search range coupled with the APF algorithm for final path-cutting optimization. This algorithm guarantees computational efficiency while giving the target directivity of the extended nodes. Furthermore, this algorithm achieves remarkable progress in solving problems of slow convergence speed and unsmooth path in the UAV path planning and achieves good performance in both offline static and online dynamic environment path planning.

In recent years, dual-arm robots have been favored in various industries due to their excellent coordinated operability. One of the focused areas of study on dual-arm robots is obstacle avoidance, namely path planning. Among the existing path planning methods, the artificial potential field (APF) algorithm is widely applied in obstacle avoidance for its simplicity, practicability, and good real-time performance over other planning methods. However, APF is firstly proposed to solve the obstacle avoidance problem of mobile robot in plane, and thus has some limitations such as being prone to fall into local minimum, not being applicable when dynamic obstacles are encountered. Therefore, an obstacle avoidance strategy for a dual-arm robot based on speed field with improved artificial potential field algorithm is proposed. In our method, the APF algorithm is used to establish the attraction and repulsion functions of the robotic manipulator, and then the concepts of attraction and repulsion speed are introduced. The attraction and repulsion functions are converted into the attraction and repulsion speed functions, which mapped to the joint space. By using the Jacobian matrix and its inverse to establish the differential velocity function of joint motion, as well as comparing it with the set collision distance threshold between two robotic manipulators of robot, the collision avoidance can be solved. Meanwhile, after introducing a new repulsion function and adding virtual constraint points to eliminate existing limitations, APF is also improved. The correctness and effectiveness of the proposed method in the self-collision avoidance problem of a dual-arm robot are validated in MATLAB and Adams simulation environment.

Aiming at the problem of difficult obstacle avoidance for unmanned ground vehicles (UGVs) in complex dynamic environments, an improved A*-APF algorithm (BA*-MAPF algorithm) is proposed in this paper. Addressing the A* algorithm’s challenges of lengthy paths, excess nodes, and lack of smoothness, the BA*-MAPF algorithm integrates a bidirectional search strategy, applies interpolation to remove redundant nodes, and uses cubic B-spline curves for path smoothing. To rectify the traditional APF algorithm’s issues with local optimization and ineffective dynamic obstacle avoidance, the BA*-MAPF algorithm revises the gravitational field function by incorporating a distance factor, and fine-tunes the repulsive field function to vary with distance. This adjustment ensures a reduction in gravitational force as distance increases and moderates the repulsive force near obstacles, facilitating more effective local path planning and dynamic obstacle navigation. Through our experimental analysis, the BA*-MAPF algorithm has been validated to significantly outperform existing methods in achieving optimal path planning and dynamic obstacle avoidance, thereby markedly boosting path planning efficiency in varied scenarios.

In view of the complex marine environment of navigation, especially in the case of multiple static and dynamic obstacles, the traditional obstacle avoidance algorithms applied to unmanned surface vehicles (USV) are prone to fall into the trap of local optimization. Therefore, this paper proposes an improved artificial potential field (APF) algorithm, which uses 5G communication technology to communicate between the USV and the control center. The algorithm introduces the USV discrimination mechanism to avoid the USV falling into local optimization when the USV encounter different obstacles in different scenarios. Considering the various scenarios between the USV and other dynamic obstacles such as vessels in the process of performing tasks, the algorithm introduces the concept of dynamic artificial potential field. For the multiple obstacles encountered in the process of USV sailing, based on the International Regulations for Preventing Collisions at Sea (COLREGS), the USV determines whether the next step will fall into local optimization through the discrimination mechanism. The local potential field of the USV will dynamically adjust, and the reverse virtual gravitational potential field will be added to prevent it from falling into the local optimization and avoid collisions. The objective function and cost function are designed at the same time, so that the USV can smoothly switch between the global path and the local obstacle avoidance. The simulation results show that the improved APF algorithm proposed in this paper can successfully avoid various obstacles in the complex marine environment, and take navigation time and economic cost into account.

Considering that the actual operating environment of UAV is complex and easily disturbed by the space environment of urban buildings, the RoutE Planning Algorithm of Resilience Enhancement (REPARE) for UAV 3D route planning based on the A* algorithm and artificial potential fields algorithm is carried out in a targeted manner. First of all, in order to ensure the safety of the UAV design, we focus on the capabilities of the UAV body and build a risk identification, assessment, and modeling method such that the mission control parameters of the UAV can be determined. Then, the three-dimensional route planning algorithm based on the artificial potential fields algorithm is used to ensure the safe operation of the UAV online and in real time. At the same time, by adjusting the discriminant coefficient of potential risks in real time to deal with time-varying random disturbance encountered by the UAV, the resilience of the UAV 3D flight route planning can be improved. Finally, the effectiveness of the algorithm is verified by the simulation. The simulation results show that the REPARE algorithm can effectively solve the traditional route planning algorithm’s insufficiency in anti-disturbance. It is safer than a traditional A* route planning algorithm, and its running time is shorter than that of the traditional artificial potential field route planning algorithm. It solves the problems of local optimization, enhances the UAV’s ability to tolerate general uncertain disturbances, and eventually improves resilience of the system.

The rural revitalization strategy requires the establishment of an express delivery industry service system with high-quality service, advanced technology, universal urban and rural benefits, green energy saving, safety and efficiency. In order to improve the efficiency of rural logistics terminal distribution, this paper first proposes a crowdsourcing service model of logistics terminal distribution for the current situation of rural logistics distribution. Then, the LRP model of hybrid mode of rural logistics terminal distribution to home self-pickup is constructed, and an improved potential field ant colony algorithm is proposed by combining the ant colony algorithm and artificial potential field algorithm to realize the optimization of logistics terminal distribution efficiency assisted by artificial intelligence. Finally, an arithmetic example analysis is carried out and structural equation modeling is used to further analyze the influencing factors of improving rural logistics terminal distribution efficiency, so as to propose a path suggestion. The error between the optimal solution and the worst solution obtained by the potential field ant colony algorithm is small, and the error is 0 in 61.54% of the cases in the small-scale problem, which is better than the unimproved ant colony algorithm, which can effectively reduce the distribution cost and optimize the distribution path, and it is suitable for solving the optimization of rural logistics terminal distribution. In addition, the informationization level and eco-efficiency of logistics terminal distribution have a positive effect on service quality, industry development and organizational efficiency have a positive effect on economic efficiency, and service quality has a significant positive effect on economic efficiency.

During the path planning of robots in the indoor unstructured complex environment, there are often problems such as unreachable target points, deflection in the planning process, and failure to avoid dynamic obstacles in time. To solve these problems, an improved hybrid indoor path planning algorithm was proposed, wherein the improved global path planning algorithm was effectually integrated with improved local path planning algorithm. Firstly, the heuristic factor of traditional A-Star algorithm was optimized, search range and nodes were reduced, and then the path generated by traditional A-Star algorithm for path planning was smoothed using the angle bisector tangent point method. Secondly, combining path and environment information, local path planning was undertaken by utilizing the improved artificial potential field algorithm, and the unreachable target points problem was addressed by adjusting the repulsive field parameters. Additionally, dynamic potential field function was constructed to make it have the ability to resolve dynamic obstacles. Finally, in the part of actual environment verification, a comparison was made in this paper to assess the performance of the traditional hybrid algorithm against the improved algorithm in terms of path planning. The consequences showed that, by the hybrid algorithm proposed in this paper, the path planning length was reduced by 10.3%, the running time was decreased by 12.5%, and 34 redundant nodes were eliminated. The consequences indicated that the hybrid algorithm can effectively address the indoor unstructured and complex path planning problems.

The Artificial Potential Field (APF) algorithm has been widely used for collision avoidance on unmanned ships. However, traditional APF methods have several defects that need to be addressed. To ensure safe navigation with good seamanship and full compliance with the Convention on the International Regulations for Preventing Collisions at Sea, 1972 (COLREGS), this study proposes a dynamic collision avoidance method based on the APF algorithm. The proposed method incorporates a ship domain priority judgment encounter situation, allowing the algorithm to perform collision avoidance operations in accordance with actual operational requirements. To address path interference and unreachable target issues, a new attractive potential field function is introduced, dividing the attractive potential field of the target point into multiple segments simultaneously. Additionally, the repulsive force on the own ship is reduced when close to the target point. The results show that the proposed method effectively resolves path oscillation problems by integrating the potential field based on traditional APF with partial ideas from the Dynamic Window Approach (DWA). In comparison with traditional APF algorithms, the overall smoothing degree was improved by 71.8%, verifying the effectiveness and superiority of the proposed algorithm.

Unmanned surface vehicles (USVs) nowadays have been widely used in ocean observation missions, helping researchers to monitor climate change, collect environmental data, and observe marine ecosystem processes. However, path planning for USVs often faces several inherent difficulties during ocean observation missions: high dependence on environmental information, long convergence time, and low-quality generated paths. To solve these problems, this article proposes a novel artificial potential field-heuristic reward-averaging deep Q-network (APF-RADQN) framework-based path planning algorithm, aiming at finding optimal paths for USVs. First, the USV path planning is modeled as a Markov decision process (MDP). Second, a comprehensive reward function incorporating artificial potential field (APF) inspiration is designed to guide the USV to reach the target region. Subsequently, an optimized deep neural network with a reward-averaging strategy is constructed to effectively enhance the learning and convergence speed of the algorithm, thus further improving the global search capability and interface performance of USV path planning. In addition, the Bezier curve is applied to make the generated path more feasible. Finally, the effectiveness of the proposed algorithm is verified by comparing it with the DQN, A*, and APF algorithms in simulation experiments. Simulation results demonstrate that the APF-RADQN improves the interface ability and path quality, significantly enhancing the USV navigation safety and ocean observation mission operation efficiency.

This paper proposes a novel path planning algorithm called Reference Linguistic Fuzzy Algorithm (RLFA); moreover, the parametric analysis procedure of the RLFA is described in this paper. It finds the optimal path by calculating the decision value required by path planning. However, most of the research ignores the issue of equal decision scores. The proposed algorithm has been compared to the original distance metrics, the fuzzy synthetic evaluation method, the modified three-way TOPSIS method, and the Generalized TODIM method. Compared with the existing algorithms, the algorithm proposed in this paper improves the reliability of path planning decision making and eliminates the defects of the traditional algorithms, which rely on parameter settings. It combines subjective and objective dual evaluation perspectives to correct the possible bias in the single subjective perspective. This paper is supported by experimental validation, and the results show that the proposed algorithm can achieve the optimal solution under all conditions.