In recent years, robot path planning has gained high attention. The traditional adaptive Monte Carlo localization (AMCL) has such problems as limitations in global localization, and incomplete path and time-consuming ...
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In recent years, robot path planning has gained high attention. The traditional adaptive Monte Carlo localization (AMCL) has such problems as limitations in global localization, and incomplete path and time-consuming problem in path planning due to too much calculation of meaningless nodes by the jump point search (jps) algorithm. In view of the above problems, this paper proposed a method for vision-based initial localization of automated guided vehicle (AGV) and path planning with (pruning optimization) PO-jps algorithm. The core contents include: vision-based AMCL localization module and improved jps algorithm based on pruning optimization. Firstly, Oriented FAST and Rotated BRIEF (ORB) features are extracted from the images collected by vision, and coordinates are localized with the features, coupled with the initial map by laser SLAM, to construct a bag-of-words (BoW) library of features. The key frame most similar to the current one is obtained by comparing the similarity between the current and historical frames in the BoW library. The Euler transformation between these two frames is calculated, to carry out pose estimation. This pose, as an initial value, is provided to the AMCL for particle iteration. Secondly, in the path planning stage, an improved jps algorithm based on pruning optimization is proposed, and a strategy that the repeated intermediate inflection points in the complemented path after pathfinding are deleted is designed. Therefore, while a complete path is obtained, the calculation workload and memory consumption for meaningless nodes during node extension are reduced successfully, and the efficiency of the pathfinding algorithm is raised. Finally, verification of the method proposed in this paper is completed through a large number of simulations and physical experiments, which saved 17.7% of the time compared to the original jps algorithm and 279.6% to the A* algorithm.
This paper focuses on the path planning improvement for mobile robots in cluttered environments. Due to the uncertainty of searching direction in traditional path planning algorithms, each node often searches for its ...
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This paper focuses on the path planning improvement for mobile robots in cluttered environments. Due to the uncertainty of searching direction in traditional path planning algorithms, each node often searches for its following path node in irrelevant directions, which increases the time cost and the number of invalid nodes. In this study, an artificial potential field guided jump point search algorithm is proposed to solve this low-efficiency problem. This method builds an APF and a direction map, which represent resultant force distribution and node directionality to the target node, respectively. Then, with consideration of APF influence and direction map guidance, an expansion direction priority for path planning is calculated, which guides and improves the search for subsequent jump points. To evaluate its performance and efficiency, the APF-jps algorithm is compared with the conventional jps, RRT, APF and 8-domains A* algorithms in simulation and mobile robot experiments. The experimental results indicate that the APF-jps algorithm not only plans the shortest available path with the least time cost, but also reaches the highest node utilization rate. Comparing with the conventional jps algorithm, which ranks second in overall performance, both the number of key nodes and the path planning time decrease by 45.0% and 53.8%, respectively, while the node utilization rate increases by 23.4%. Therefore, the APF-jps algorithm shows its advantages in path planning, mainly by reducing the system computational load, improving the real-time performance, and increasing the mobile robot endurance time.
In order to address issues in the jps pathfinding algorithm such as the search for unnecessary jump points, frequent jump point selections, and the lack of environmental information regarding the target location, this...
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In view of the discussion of robot collision in complex environment due to control or positioning error and robot size, this paper presents a jump point search algorithm with safe distance(SD-jps) for path planning. B...
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ISBN:
(纸本)9781728116976
In view of the discussion of robot collision in complex environment due to control or positioning error and robot size, this paper presents a jump point search algorithm with safe distance(SD-jps) for path planning. Based on the fast jump point search method, a new definition of the jump point and a node domain matrix are proposed to improve the jps algorithm. The SD-jps algorithm can select the any size of the node domain matrix to obtain the safe distance between the robot and the obstacle, and improve the flexibility of the robot movement. In addition, the different effects of SD-jps and A* algorithm on the number of search nodes, planed routes and planning time are also studied. Simulation results show that the improved jps algorithm can obtain different safety distances and plan the optimal path with shorter time.
Due to the increasing demand for unmanned in the hotel industry in recent years, how to efficiently use hotel service robots to further improve the efficiency of the hotel industry has become a hot research issue. To ...
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Due to the increasing demand for unmanned in the hotel industry in recent years, how to efficiently use hotel service robots to further improve the efficiency of the hotel industry has become a hot research issue. To solve the problems of lengthy path-finding time and poor route security in conventional service robots in complex environments, the current study provides an improved A* path-finding algorithm for application in the hotel environment. Firstly, the conventional A* algorithm is combined with bidirectional search and Jump Point Search (JSP) algorithm, which makes the search more effective. Secondly, the traditional A* algorithm is combined with the security weight square matrix to make the path trajectory safer. A cubic spline interpolation is chosen to smoothen the transitions at the corners planned by the improved A* algorithm. Simulation experiments were done on grid maps with 10*10, 20*20 and 50*50 sizes. Compared with the conventional A* algorithm, the search time were decreased by 67%, 77% and 95% respectively. The number of search nodes was decreased by 80%, 76% and 95%, respectively. Meanwhile the distance between the robot and the obstacles was increased. The results indicate that the improved A* algorithm suggested in the present research can ensure the path trajectory safer while keeping the path search efficiency higher.
Aiming at the problem that it is difficult to obtain the optimal safe path when the special operations team performs missions in unfamiliar indoor environments, a tactical path planning approach incorporating the impr...
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ISBN:
(纸本)9798350349122;9798350349115
Aiming at the problem that it is difficult to obtain the optimal safe path when the special operations team performs missions in unfamiliar indoor environments, a tactical path planning approach incorporating the improved jps algorithm and the A* algorithm is proposed. Firstly, the algorithm performance is improved by improving the screening rules of the jump points of the jps algorithm and deleting the redundant intermediate jump points;secondly, the special warfare threat model is introduced into the heuristic function to avoid the enemy threat in the process of tactical path planning;in addition, considering the characteristics of the influence of environmental complexity on the jps algorithm, the threshold function of the jump points is designed, and the A* algorithm and the improved jps algorithm are fused to solve the problem of reduced efficiency caused by searching for too many jump points in a dense environment of the jps algorithm in the obstacles. Finally, experimental results show that the proposed algorithm reduces the exposed path length by 69.95% in the hazardous area during path planning, while the path planning time is reduced by 8.44%.
The Jump Point Search (jps) algorithm ignores the possibility of any-angle walking, so the paths found by the jps algorithm under the discrete grid map still have a gap with the real paths. To address the above proble...
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The Jump Point Search (jps) algorithm ignores the possibility of any-angle walking, so the paths found by the jps algorithm under the discrete grid map still have a gap with the real paths. To address the above problems, this paper improves the path optimization strategy of the jps algorithm by combining the viewable angle of the Angle-Propagation Theta* (AP Theta*) algorithm, and it proposes the AP-jps algorithm based on an any-angle pathfinding strategy. First, based on the jps algorithm, this paper proposes a vision triangle judgment method to optimize the generated path by selecting the successor search point. Secondly, the idea of the node viewable angle in the AP Theta* algorithm is introduced to modify the line of sight (LOS) reachability detection between two nodes. Finally, the paths are optimized using a seventh-order polynomial based on minimum snap, so that the AP-jps algorithm generates paths that better match the actual robot motion. The feasibility and effectiveness of this method are proved by simulation experiments and comparison with other algorithms. The results show that the path planning algorithm in this paper obtains paths with good smoothness in environments with different obstacle densities and different map sizes. In the algorithm comparison experiments, it can be seen that the AP-jps algorithm reduces the path by 1.61-4.68% and the total turning angle of the path by 58.71-84.67% compared with the jps algorithm. The AP-jps algorithm reduces the computing time by 98.59-99.22% compared with the AP-Theta* algorithm.
The Jump Point Search (jps) algorithm is adopted for local path planning of the driverless car under urban environment, and it is a fast search method applied in path planning. Firstly, a vector Geographic Information...
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The Jump Point Search (jps) algorithm is adopted for local path planning of the driverless car under urban environment, and it is a fast search method applied in path planning. Firstly, a vector Geographic Information System (GIS) map, including Global Positioning System (GPS) position, direction, and lane information, is built for global path planning. Secondly, the GIS map database is utilized in global path planning for the driverless car. Then, the jps algorithm is adopted to avoid the front obstacle, and to find an optimal local path for the driverless car in the urban environment. Finally, 125 different simulation experiments in the urban environment demonstrate that jps can search out the optimal and safety path successfully, and meanwhile, it has a lower time complexity compared with the Vector Field Histogram (VFH), the Rapidly Exploring Random Tree (RRT), A*, and the Probabilistic Roadmaps (PRM) algorithms. Furthermore, jps is validated usefully in the structured urban environment.
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