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In order to address the shortcomings of the traditional bidirectional RRT* algorithm, such as its high degree of randomness, low search efficiency, and the many inflection points in the planned path, we institute improvements in the following directions. Firstly, to address the problem of the high degree of randomness in the process of random tree expansion, the expansion direction of the random tree growing at the starting point is constrained by the improved artificial potential field method; thus, the random tree grows towards the target point. Secondly, the random tree sampling point grown at the target point is biased to the random number sampling point grown at the starting point. Finally, the path planned by the improved bidirectional RRT* algorithm is optimized by extracting key points. Simulation experiments show that compared with the traditional A*, the traditional RRT, and the traditional bidirectional RRT*, the improved bidirectional RRT* algorithm has a shorter path length, higher path-planning efficiency, and fewer inflection points. The optimized path is segmented using the dynamic window method according to the key points. The path planned by the fusion algorithm in a complex environment is smoother and allows for excellent avoidance of temporary obstacles.

Designed to meet the demands of AGV global optimal path planning and dynamic obstacle avoidance, this paper proposes a combination of an improved A* algorithm and dynamic window method fusion algorithm. Firstly, the heuristic function is dynamically weighted to reduce the search scope and improve the planning efficiency; secondly, a path-optimization method is introduced to eliminate redundant nodes and redundant turning points in the path; thirdly, combined with the improved A* algorithm and dynamic window method, the local dynamic obstacle avoidance in the global optimal path is realized. Finally, the effectiveness of the proposed method is verified by simulation experiments. According to the results of simulation analysis, the path-planning time of the improved A* algorithm is 26.3% shorter than the traditional A* algorithm, the search scope is 57.9% less, the path length is 7.2% shorter, the number of path nodes is 85.7% less, and the number of turning points is 71.4% less. The fusion algorithm can evade moving obstacles and unknown static obstacles in different map environments in real time along the global optimal path.

In the context of Industry 4.0, addressing the challenge of dynamic obstacle avoidance for Automated Guided Vehicles (AGVs) in complex industrial environments, this paper proposes an algorithm that integrates an enhanced social force model (SFM) and an improved dynamic window approach (DWA), leveraging SparkLink communication technology to enhance data transmission speed and reliability. The introduction of SparkLink technology significantly improves the environmental perception capabilities of AGVs, optimizing their dynamic obstacle-avoidance performance. Experimental results demonstrate that this method effectively increases the efficiency of AGVs in dynamic obstacle avoidance, offering significant practical value.

In order to solve the problems of the basic mayfly optimization algorithm (MOA) in the field of robot path planning, such as slow convergence speed, poor accuracy, insufficient stability, and only applicable to static environment, a fusion algorithm combining improved mayfly optimization algorithm and dynamic window approach is proposed in this paper. Firstly, an improved mayfly optimization algorithm based on Q-learning (IMOA-QL) is proposed to solve robot global path planning problem. Q-learning is taken as the core of the improved mayfly optimization algorithm. For the basic MOA, the inertia weight and positive attraction coefficients are set to fixed values, which are not reasonable and will make the global search ability unbalanced, fall into local optimization easily, and also limit the iteration speed. In this paper, the parameters are adaptively adjusted based on Q-learning, and the appropriate parameters are selected according to the fitness of each mayfly. Meanwhile, the memory mechanism is introduced to speed up the convergence speed and implement the global path planning. Then, the global path nodes are extracted as the sub-target points, and the improved dynamic window approach is used to carry out the local path planning, which effectively improves the dynamic real-time avoidance ability. In order to verify the effectiveness of the proposed IMOA-QL algorithm in this paper, 20 random simulation experiments are carried out in the 100 × 100 static map environment and compared with the basic mayfly optimization algorithm (MOA) and the mayfly optimization algorithm based on linear adaptive inertia weight (MOA-LAIW). The results show that the average path length of the proposed IMOA-QL algorithm is reduced by 4.48% and 2.17% compared with MOA and MOA-LAIW in simple environment, and the average path length of the proposed IMOA-QL algorithm is reduced by 6.58% and 3.24% compared with MOA and MOA-LAIW in complex environment. In 20 experiments, the average variance of the proposed IMOA-QL algorithm in this paper is reduced by 74.15% and 57.67% compared with MOA and MOA-LAIW in simple environment, and the average variance of the proposed IMOA-QL algorithm is reduced by 51.22% and 38.67% in complex environment compared with MOA and MOA-LAIW. The simulation results show that the proposed IMOA-QL algorithm has significantly improved the accuracy and speed of solution. Moreover, dynamic obstacles are added in the static environment to carry out the simulation test of the fusion dynamic path planning algorithm. The results show that a fusion algorithm combining improved mayfly optimization algorithm and dynamic window approach in this paper can better complete the path planning task well in the complex dynamic environment.

This paper introduces an Improved Bidirectional Jump Point Search (I-BJPS) algorithm to address the challenges of the traditional Jump Point Search (JPS) in mobile robot path planning. These challenges include excessive node expansions, frequent path inflexion points, slower search times, and a high number of jump points in complex environments with large areas and dense obstacles. Firstly, we improve the heuristic functions in both forward and reverse directions to minimize expansion nodes and search time. We also introduce a node optimization strategy to reduce non-essential nodes so that the path length is optimized. Secondly, we employ a second-order Bezier Curve to smooth turning points, making generated paths more suitable for mobile robot motion requirements. Then, we integrate the Dynamic Window Approach (DWA) to improve path planning safety. Finally, the simulation results demonstrate that the I-BJPS algorithm significantly outperforms both the original unidirectional JPS algorithm and the bidirectional JPS algorithm in terms of search time, the number of path inflexion points, and overall path length, the advantages of the I-BJPS algorithm are particularly pronounced in complex environments. Experimental results from real-world scenarios indicate that the proposed algorithm can efficiently and rapidly generate an optimal path that is safe, collision-free, and well-suited to the robot’s locomotion requirements.

Aiming at the problems of “local minimum” and “unreachable target” existing in the traditional artificial potential field method in path planning, an improved artificial potential field method was proposed after analyzing the fundamental causes of the above problems. The method solved the problem of local minimum by modifying the direction and influence range of the gravitational field, increasing the virtual target and evaluation function, and the problem of unreachable targets is solved by increasing gravity. In view of the change of motion state of robot fish in amphibious environments, the improved artificial potential field method was fused with a dynamic window algorithm, and a dynamic window evaluation function of the optimal path was designed on the basis of establishing the dynamic equations of land and underwater. Then, the simulation experiment was designed under the environment of Matlab2019a. Firstly, the improved and traditional artificial potential field methods were compared. The results showed that the improved artificial potential field method could solve the above two problems well, shorten the operation time and path length, and have high efficiency. Secondly, the influence of different motion modes on path planning is verified, and the result also reflects that the amphibious robot can avoid obstacles flexibly and reach the target point accurately according to its own motion ability. This paper provides a new way of path planning for the amphibious robot.

In order to ensure the safe navigation of USVs (unmanned surface vessels) and real-time collision avoidance, this study conducts global and local path planning for USVs in a variable dynamic environment, while local path planning is proposed under the consideration of USV motion characteristics and COLREGs (International Convention on Regulations for Collision Avoidance at Sea) requirements. First, the basis of collision avoidance decisions based on the dynamic window method is introduced. Second, the knowledge of local collision avoidance theory is used to study the local path planning of USV, and finally, simulation experiments are carried out in different situations and environments containing unknown obstacles. The local path planning experiments with unknown obstacles can prove that the local path planning algorithm proposed in this study has good results and can ensure that the USV makes collision avoidance decisions based on COLREGs when it meets with a ship.

A hard issue in the field of microrobots is path planning in complicated situations with dense obstacle distribution. Although the Dynamic Window Approach (DWA) is a good obstacle avoidance planning algorithm, it struggles to adapt to complex situations and has a low success rate when planning in densely populated obstacle locations. This paper suggests a multi-module enhanced DWA (MEDWA) obstacle avoidance planning algorithm to address the aforementioned issues. An obstacle-dense area judgment approach is initially presented by combining Mahalanobis distance, Frobenius norm, and covariance matrix on the basis of a multi-obstacle coverage model. Second, MEDWA is a hybrid of enhanced DWA (EDWA) algorithms in non-dense areas with a class of two-dimensional analytic vector field methods developed in dense areas. The vector field methods are used instead of the DWA algorithms with poor planning performance in dense areas, which greatly improves the passing ability of microrobots over dense obstacles. The core of EDWA is to extend the new navigation function by modifying the original evaluation function and dynamically adjusting the weights of the trajectory evaluation function in different modules using the improved immune algorithm (IIA), thus improving the adaptability of the algorithm to different scenarios and achieving trajectory optimization. Finally, two scenarios with different obstacle-dense area locations were constructed to test the proposed method 1000 times, and the performance of the algorithm was verified in terms of step number, trajectory length, heading angle deviation, and path deviation. The findings indicate that the method has a smaller planning deviation and that the length of the trajectory and the number of steps can both be reduced by about 15%. This improves the ability of the microrobot to pass through obstacle-dense areas while successfully preventing the phenomenon of microrobots going around or even colliding with obstacles outside of dense areas.

Path planning is a hot topic at present. Considering the global and local path planning of mobile robot is one of the challenging research topics. The objective of this paper is to create a rasterized environment that optimizes the planning of multiple paths and solves barrier avoidance issues. Combining the A* algorithm with the dynamic window method, a robo-assisted random barrier avoidance method is used to resolve the issues caused by collisions and path failures. Improving the A* algorithm requires analyzing and optimizing its evaluation function to increase search efficiency. The redundant point removal strategy is then presented. The dynamic window method is utilized for local planning between each pair of adjacent nodes. This method guarantees that random obstacles are avoided in real-time based on the globally optimal path. The experiment demonstrates that the enhanced A* algorithm reduces the average path length and computation time when compared to the traditional A* algorithm. After fusing the dynamic window method, the local path is corrected using the global path, and the resolution for random barrier avoidance is visualized.

This paper addresses the limitations of the A* algorithm in underground roadway path planning, such as proximity to roadway boundaries, intersection with obstacle corners, trajectory smoothness, and timely obstacle avoidance (e.g., fallen rocks, miners, and moving equipment). To overcome these challenges, we propose an improved path planning algorithm integrating an enhanced A* method with an improved Dynamic Window Approach (DWA). First, a diagonal collision detection mechanism is implemented within the A* algorithm to effectively avoid crossing obstacle corners, thus enhancing path safety. Secondly, roadway width is incorporated into the heuristic function to guide paths toward the roadway center, improving stability and feasibility. Subsequently, based on multiple global path characteristics—including path length, average curvature, fluctuation degree, and direction change rate—an adaptive B-spline curve smoothing method generates smoother paths tailored to the robot’s kinematic requirements. Furthermore, the global path is segmented into local reference points for DWA, ensuring seamless integration of global and local path planning. To prevent local optimization traps during obstacle avoidance, a distance-based cost function is introduced into DWA’s evaluation criteria, maintaining alignment with the global path. Experimental results demonstrate that the proposed method significantly reduces node expansions by 43.79%, computation time by 16.28%, and path inflection points by 80.70%. The resultant path is smoother, centered within roadways, and capable of effectively avoiding dynamic and static obstacles, thereby ensuring the safety and efficiency of underground robotic transport operations.