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For the traditional JPS algorithm in mobile robot path planning, there are path inflection points as well as too many intermediate jump points, and the path planning time is long. This paper proposed an improved jump point search (JPS) algorithm. The I-JPS algorithm eliminated redundant nodes and increased the safe distance between the robot and obstacles by improving the cost function and introducing the fork product formula. It also introduced DWA for local path planning for temporary obstacle avoidance and path smoothing by robots, and increased the priority of obstacle avoidance between multiple robots by improving DWA. Finally, it introduced multi-robot collaborative path planning, where multiple robots could work together and complete complex tasks, and the robots could also share information, coordinate their actions, and solve problems through division of labour to improve the efficiency of task completion. Finally, the experimental simulation results show that the improved algorithm is greatly improved in all aspects compared to the pre-improved one. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In response to the imperative challenges associated with global path planning and dynamic obstacle avoidance for mobile robots navigating dynamic environments, we introduce a hybrid path planning methodology that synergistically combines an enhanced A* algorithm with an optimized dynamic window method. In the refined A* algorithm, an adaptive weight heuristic search function that comprehensively considers Manhattan distance and Euclidean distance is designed to improve the search efficiency; secondly, a redundant point elimination method is proposed to delete redundant paths node and perform path pruning, and then use Minimum Snap to smooth and optimize the pruned path. To address challenges associated with both random obstacle avoidance and dynamic obstacle avoidance, the fusion algorithm delineated in this study incorporates the global path nodes, derived through enhancements to the A* algorithm, as local target points while also employing the optimized dynamic window method for local path planning. The experimental results indicate that, on average, the improved A* algorithm can decrease the path length by 17.2% and reduce the number of search nodes by 62.3% compared to the conventional A* algorithm. After integrating and optimizing the dynamic window method, it can realize random obstacle avoidance and dynamic Avoidance. [ABSTRACT FROM AUTHOR]

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Due to the problems of traditional material transfer equipment such as single working mode and inflexible adjustment of transfer path, intelligent material transfer system has gradually become a research hotspot in the field of logistics transmission. A path planning algorithm based on A* and dynamic window method was proposed for the modular material transmission platform, in order to improve its obstacle avoidance ability during the transfer process by flexibly adjusting the material transfer path. In the global path planning, the smoothness and static obstacle avoidance of the transmission path were realized by improving the A* weight function and integrating the Bezier curve and matrix interference theory. And by introducing the dynamic window method and extracting the global path key points as transition points for local path guidance of the transmission target, dynamic obstacle avoidance was realized when the path falling into local optimization was avoided. The research results showed that the path planning algorithm based on A* and dynamic window method could reduce the total global path length by 4.6% and the total path turning angle by 42.3%, while the dynamic obstacles could be effectively avoided in the local planning, which verified the rationality of the path planning algorithm.

Copyright of Chinese Journal of Intelligent Science & Technology is the property of Beijing Xintong Media Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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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. [ABSTRACT FROM AUTHOR]

Copyright of Ordnance Industry Automation is the property of Editorial Board for Ordnance Industry Automation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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. [ABSTRACT FROM AUTHOR]

Aiming at the efficiency of path planning of mobile robots in complex environments (including static and dynamic environments), a hybrid algorithm combining an improved A* algorithm and an improved dynamic window method is proposed. Aiming at the problem of insufficient security of the traditional A* algorithm, the obstacle avoidance strategy is adopted to optimize the selection of nodes to increase the safety of the path. Aiming at the problem of many turning points, the recursive dichotomy optimization strategy is adopted to remove redundant nodes and reduce the number of turns. Aiming at the problem of insufficient path smoothness in a static environment, the dynamic inscribed circle smoothing strategy is used to optimize the polyline angle to a radian angle to increase the smoothness of the path. In the traditional dynamic window method, when there are obstacles near the target point, the planning effect is not good and it is easy to fall into the local optimum in the concave groove obstacle. The distance deviation and trajectory deviation are introduced into the original evaluation function. Finally, the proposed improved A* algorithm and hybrid algorithm are simulated and compared with other algorithms in static and dynamic environments respectively. The results show that, compared with the traditional hybrid algorithm, the proposal reduces the path length and running time in the temporary obstacle environment by 13.2% and 65.8%, respectively, and reduces the path length and running time in the mobile obstacle environment by 13.9% and 44.9%, respectively. The proposed algorithm improves the efficiency of path planning in complex environments. [ABSTRACT FROM AUTHOR]

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.

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.

Aiming at the autonomous valet parking path planning problems for the wire-four-wheel steering vehicles, an autonomous valet path planning method with adaptive ability to dynamic obstacles was proposed. Firstly, the grid map which might describe the environmental information of parking lot was used to transform the autonomous valet parking path planning problem in continuous state space into that in discrete state space, and the isotropic circular structure elements were used to expand the grid map to keep a safe distance between the planned autonomous valet parking path and the obstacles. Then, the D* Lite algorithm was used to plan the global autonomous valet parking path with adaptive ability to dynamic obstacles, as well the dynamic window method and the arc-line combination method were used to plan the local autonomous valet parking path, which met the kinematic and mechanical constraints of the wire-four-wheel steering vehicles and might guide the vehicles to enter the oblique parking space safely. Finally, the feasibility of the proposed method was verified by simulation, and the results show that the proposed method may plan a feasible path to guide the vehicles into the target oblique parking space safely in the presence of dynamic obstacles. [ABSTRACT FROM AUTHOR]

In order to meet the performance requirements of global optimality and path smoothness in robot path planning, a new fusion algorithm of jump-A* algorithm and dynamic window approach is proposed. First, A* algorithm is optimized by using the jump point search method and a new distance evaluation function defined by Manhattan and Euclidean distance to obtain global path information. Then take the dynamic window approach as the core by integrating the global path information to safely plan a global optimal path with high smoothness. The experimental results show that the new fusion algorithm proposed in this paper can not only effectively solve the problem of non-continuous curvature and excessive turning angle at the turning points of the path planned by jump-A* algorithm, but also improve the smoothness of the path and the global optimality. This research is beneficial to the motion control of robots and has certain reference for robot navigation.

Aiming at the problem of autonomous valet parking path planning in the presence of dynamic obstacle, a novel path planning method for autonomous valet parking system is proposed based on D * algorithm and dynamic window method. First, the raster scanned algorithm is used to quickly and accurately construct the static environment map of the autonomous valet parking scene, and the Dykstra algorithm is used to update the local static environment map affected by dynamic obstacle in real time. Then, the D* algorithm is used to transform the static environment map of the autonomous valet parking scene into a static path field in order to obtain the original global autonomous valet parking path. Meanwhile, the global autonomous valet parking path affected by dynamic obstacle is re-planned with the help of the static path field. Finally, the dynamic window method and arc-line combination method are used to plan the optimal local autonomous valet parking path, and the geometric boundary collision detection method and raster space coverage enumeration method are integrated to quickly eliminate the infeasible local autonomous valet parking path. The feasibility and effectiveness of the proposed method for autonomous valet parking system are verified in VC + + 6. 0 environment. The results show that the proposed method can safely guide the vehicle into the target vertical parking space in the autonomous valet parking scene with dynamic obstacle. [ABSTRACT FROM AUTHOR]

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.

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Signal Processing is the property of Journal of Signal Processing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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. [ABSTRACT FROM AUTHOR]

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.

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.

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)