To improve the efficiency of mobile robot movement, this paper investigates the fusion of the A* algorithm with the Dynamic Window Approach (dwa) algorithm (IA-dwa) to quickly search for globally optimal collision-fre...
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To improve the efficiency of mobile robot movement, this paper investigates the fusion of the A* algorithm with the Dynamic Window Approach (dwa) algorithm (IA-dwa) to quickly search for globally optimal collision-free paths and avoid unknown obstacles in time. First, the data from the odometer and the inertial measurement unit (IMU) are fused using the extended Kalman filter (EKF) to reduce the error caused by wheel slippage on the mobile robot's positioning and improve the mobile robot's positioning accuracy. Second, the prediction function, weight coefficients, search neighborhood, and path smoothing processing of the A* algorithm are optimally designed to incorporate the critical point information in the global path into the dwa calculation framework. Then, the length of time and convergence speed of path planning are compared and simulated in raster maps of different complexity. In terms of path planning time, the algorithm reduces by 23.3% compared to A*-dwa;in terms of path length, the algorithm reduces by 1.8% compared to A*-dwa, and the optimization iterations converge faster. Finally, the reliability of the improved algorithm is verified by conducting autonomous navigation experiments using a ROS (Robot Operating System) mobile robot as an experimental platform.
Purpose The purpose of this study is to solve the problems of poor stability and high energy consumption of the dynamic window algorithm (dwa) for the mobile robots, a novel enhanced dynamic window algorithm is propos...
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Purpose The purpose of this study is to solve the problems of poor stability and high energy consumption of the dynamic window algorithm (dwa) for the mobile robots, a novel enhanced dynamic window algorithm is proposed in this paper. Design/methodology/approach The novel algorithm takes the distance function as the weight of the target-oriented coefficient, and a new evaluation function is presented to optimize the azimuth angle. Findings The jitter of the mobile robot caused by the drastic change of angular velocity is reduced when the robot is closer to the target point. The simulation results show that the proposed algorithm effectively optimizes the stability of the mobile robot during operation with lower angular velocity dispersion and less energy consumption, but with a slightly higher running time than dwa. Originality/value A novel enhanced dynamic window algorithm is proposed and verified. According to the experimental result, the proposed algorithm can reduce the energy consumption of the robot and improves the efficiency of the robot.
Based on the actual outdoor road environment, this paper processes the road area in the semantic label map to obtain discrete guide points. B-spline curve fitting is employed to obtain road guide lines, which are then...
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Based on the actual outdoor road environment, this paper processes the road area in the semantic label map to obtain discrete guide points. B-spline curve fitting is employed to obtain road guide lines, which are then introduced into the Dynamic Window Approach (dwa) through an evaluation function, realizing the improvement of dwa-based local path planning for epidemic prevention robots in outdoor road environments. After analyzing the outdoor road environment scene, the characteristics of outdoor roads can be obtained. However, these characteristics cannot be directly applied to the path planning of epidemic prevention robots. It is necessary to process the semantic label map to obtain road guide lines to guide the improvement of dwa algorithm path planning for epidemic prevention robots. The guide lines can provide basic travel directions for robots, improving the efficiency of the improved dwa algorithm for local path planning of epidemic prevention robots.
In the field of unmanned vessels, path planning in confined and complex environments has become a crucial research focus. Existing methods face issues such as insufficient obstacle avoidance and low planning efficienc...
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In the field of unmanned vessels, path planning in confined and complex environments has become a crucial research focus. Existing methods face issues such as insufficient obstacle avoidance and low planning efficiency. To address these challenges, this paper proposes a hybrid approach combining an improved A* algorithm with an optimized Dynamic Window Approach (dwa). The enhanced A* algorithm adjusts the weight of the heuristic function by introducing a tuning factor (alpha), which directly influences the obstacle density. Additionally, a 5neighborhood search combined with the Floyd algorithm is employed to boost search efficiency and improve path smoothness. The modified dwa algorithm incorporates a path smoothing coefficient and a local target selection strategy, enhancing the safety and stability of local planning. MATLAB simulations demonstrate that the proposed hybrid algorithm generates smooth and safe paths, successfully avoids dynamic obstacles, and shows promising effectiveness and feasibility in unmanned vessel path planning.
To improve the intelligence level and the navigation efficiency of electric crawler tractors in facility greenhouses, this paper proposes a path planning algorithm based on the fusion of the improved A* algorithm and ...
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To improve the intelligence level and the navigation efficiency of electric crawler tractors in facility greenhouses, this paper proposes a path planning algorithm based on the fusion of the improved A* algorithm and the dwa algorithm. The weight coefficients are integrated into the heuristic function of the A* algorithm, the key point selection strategy is improved, and the second-order Bessel curves are used to smooth the path trajectories. Besides, the dwa algorithm is integrated, and the key point of global paths planned by the improved A* algorithm is taken as an interpolation point. This addresses the issue that the traditional A* algorithm needs to search many nodes and has a low computational efficiency, with many path turning points and unsmooth paths. The results of simulation experiments proved that the improved A* algorithm is less time-consuming and obtains more smoother path than the Dijkstra, RRT, and traditional A* algorithms. Meanwhile, tests in a facility greenhouse show that the electric crawler tractor can realize autonomous navigation and obstacle avoidance, with a maximum lateral deviation of 11.20 cm and a maximum heading deviation of 13 degrees, which can meet the requirements of actual operation in facility greenhouses.
The path planning of Unmanned Surface Vehicles(USV) represents a pivotal technology for autonomous navigation. However, prevailing USV path planning methodologies frequently overlook the constraints imposed by the Int...
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The path planning of Unmanned Surface Vehicles(USV) represents a pivotal technology for autonomous navigation. However, prevailing USV path planning methodologies frequently overlook the constraints imposed by the International Regulations for Preventing Collisions at Sea (COLREGs), resulting in certain deficiencies in the corresponding planned paths. To address this issue, a hybrid path planning algorithm integrating 20D-A* and improved dynamic window approach(Idwa) is proposed. The proposed algorithm enables USVs to comply with COLREGs requirements for collision avoidance when encountering other vessels. The 20D-A* algorithm performs an initial path planning based on the starting and ending points. When the USV encounters other vessels while navigating along the planned path, Idwa autonomously executes collision avoidance actions in compliance with COLREGs. This involves locally adjusting the planned path and resuming the original path once the avoidance actions have been completed. The experimental results demonstrate that the proposed method demonstrates high efficiency and obstacle avoidance performance in complex environments. It effectively addresses the impact of static obstacles and navigating vessels on the USV while satisfying the collision avoidance requirements specified by COLREGs. This research offers a novel solution for USV path planning in real-world navigation scenarios, showcasing promising application prospects.
This paper focussed on the development of a dynamic and efficient obstacle avoidance path planning algorithm based on ORCA-dwa algorithm, which combines the Optimal Reciprocal Collision Avoidance (ORCA) algorithm and ...
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This paper focussed on the development of a dynamic and efficient obstacle avoidance path planning algorithm based on ORCA-dwa algorithm, which combines the Optimal Reciprocal Collision Avoidance (ORCA) algorithm and an improved Dynamic Window Approach (dwa), for improving the quality and efficiency of globally planned paths and ensuring obstacle avoidance between robots for local path planning. This combined ORCA-dwa approach effectively combines the speed of dwa planning with the preferred speed of the ORCA algorithm, it not only solves the problem of the ORCA algorithm's difficulty in determining the preferred speed, but also does not deviate from its optimal trajectory while avoiding obstacles. Additionally, an improved dynamic windowing method is proposed to enhance the adaptability to the environment. As a result, the mobile robot can not only use the dwa algorithm to achieve global path optimisation during navigation, but also achieve obstacle avoidance with the shortest time and path while following the robot's own constraints and considering the robot's radius. Simulation results prove that the method can greatly reduce the length and time of path planning and show that this new algorithm can make the robot's speed smoother.
In the field of AGV, a path planning algorithm is always a heated area. However, traditional path planning algorithms have many disadvantages. To solve these problems, this paper proposes a fusion algorithm that combi...
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In the field of AGV, a path planning algorithm is always a heated area. However, traditional path planning algorithms have many disadvantages. To solve these problems, this paper proposes a fusion algorithm that combines the kinematical constraint A* algorithm and the following dynamic window approach algorithm. The kinematical constraint A* algorithm can plan the global path. Firstly, the node optimization can reduce the number of child nodes. Secondly, improving the heuristic function can increase efficiency of path planning. Thirdly, the secondary redundancy can reduce the number of redundant nodes. Finally, the B spline curve can make the global path conform to the dynamic characteristics of AGV. The following dwa algorithm can be dynamic path planning and allow the AGV to avoidance moving obstacle. The optimization heuristic function of the local path is closer to the global optimal path. The simulation results show that, compared with the fusion algorithm of traditional A* algorithm and traditional dwa algorithm, the fusion algorithm reduces the length of path by 3.6%, time of path by 6.7% and the number of turns of final path by 25%.
With the escalating demand for automation in chemical laboratories, multi-robot systems are assuming an increasingly prominent role in chemical laboratories, particularly in the task of transporting reagents and exper...
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With the escalating demand for automation in chemical laboratories, multi-robot systems are assuming an increasingly prominent role in chemical laboratories, particularly in the task of transporting reagents and experimental materials. In this paper, we propose a multi-robot path planning approach based on the combination of the A* algorithm and the dynamic window algorithm (dwa) for optimizing the efficiency of reagent transportation in chemical laboratories. In environments like chemical laboratories, dynamic obstacles (such as people and equipment) and transportation tasks that demand precise control render traditional path planning algorithms challenging. To address these issues, in this paper, we incorporate the cost information from the current point to the goal point into the evaluation function of the traditional A* algorithm to enhance the search efficiency and add the safety distance to extract the critical points of the paths, which are utilized as the temporary goal points of the dwa algorithm. In the dwa algorithm, a stop-and-wait mechanism and a replanning strategy are added, and a direction factor is included in the evaluation function to guarantee that the robots can adjust their paths promptly in the presence of dynamic obstacles or interference from other robots to evade potential conflicts or traps, thereby reaching the goal point smoothly. Additionally, regarding the multi-robot path conflict problem, this paper adopts a dynamic prioritization method, which dynamically adjusts the motion priority among robots in accordance with real-time environmental changes, reducing the occurrence of path conflicts. The experimental results highlight that this approach effectively tackles the path planning challenge in multi-robot collaborative transportation tasks within chemical laboratories, significantly enhancing transportation efficiency and ensuring the safe operation of the robots.
A path planning method for unmanned surface vessels (USV) in dynamic environment is proposed to address the impact of dynamic environments on path planning results and the lack of dynamic obstacle avoidance capabiliti...
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A path planning method for unmanned surface vessels (USV) in dynamic environment is proposed to address the impact of dynamic environments on path planning results and the lack of dynamic obstacle avoidance capabilities. First, the considering ocean current rapidly exploring random tree (RRT*) (COC-RRT*) algorithm was proposed for global path planning. The RRT* algorithm has been enhanced with the integration of the virtual field sampling algorithm and ocean current constraint algorithm. The COC-RRT* algorithm optimizes the global planning path by adjusting the path between the parent nodes and child nodes. Second, according to the limitations of the International Regulations for Preventing Collisions at Sea (COLREGs), the improved dynamic window approach (dwa) is applied for local path planning. To enhance the ability of avoid dynamic obstacles, the dist function in the dwa algorithm has been improved. Simulation experiments were conducted in three scenarios to validate the proposed algorithm. The experimental results demonstrate that, in comparison with other algorithms, the proposed algorithm effectively avoids dynamic obstacles and mitigates the influence of the space-varying ocean current environment on the path-planning outcome. Additionally, the proposed algorithm exhibits high efficiency and robustness. The results verified the effectiveness of the proposed algorithm in dynamic environments.
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