Aiming at the problem of low efficiency, large path turning angle and insufficient path smoothness of mobile robot path planning using traditional A* algorithm, an novel path planning method based on improved A* algor...
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ISBN:
(数字)9781728176871
ISBN:
(纸本)9781728176871
Aiming at the problem of low efficiency, large path turning angle and insufficient path smoothness of mobile robot path planning using traditional A* algorithm, an novel path planning method based on improved A* algorithm is proposed in this paper. The procedure of the proposed method is described as follows. Firstly, the grid method is used to model the working environment map of the mobile robot. Then, in order to improve the evaluation function of the A* algorithm, not only the node steering angle and the node direction angle are integrated into the actual cost function and heuristic function of the evaluation function, but also the exponential distance adjustment factor and parent node heuristic function information are added to the heuristic function. Finally, a rotation prediction method, to which the judgment of the minimum safety distance is added, is designed to optimize the two-way smoothing of the planned path. The simulation experiment results show that the path length planned by the improved A* algorithm is shorter, and the turning angle and number of turns are smaller. The algorithm designed in this paper can improve the search efficiency and adaptability of the A* algorithm, and increase the smoothness and safety of the path.
Many contemporary navigation applications rely on the well-known A* algorithm for pathfinding. We present a pathfinding technique utilizing the cross-entropy method and Bayesian networks that is capable of finding pat...
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Many contemporary navigation applications rely on the well-known A* algorithm for pathfinding. We present a pathfinding technique utilizing the cross-entropy method and Bayesian networks that is capable of finding paths for multiple agents while satisfying rigid, optimization, and uncertainty constraints.
Path Planning algorithms are indispensable in Autonomous Navigators. Though rover may not have prior information about the environment, path planning algorithms must be robust enough to navigate it to the target. A an...
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Path Planning algorithms are indispensable in Autonomous Navigators. Though rover may not have prior information about the environment, path planning algorithms must be robust enough to navigate it to the target. A and D are grid based Path Planning algorithms which enable to reach the target in shortest path. Cost computations are fixed in a algorithm while re- computations are done in D algorithm, whenever new obstacles are identified. A modified version of D algorithm is presented in this paper where cost re-computation is done based on certain criterion. By reducing the number of times cost re-computation occurs, Modified D aims to produce shortest path similar to D algorithm with less memory requirements and execution time. This paper also brings out the implementation of A, D and Modified D algorithms with NXT LEGO Mindstorms kit and their evaluation in real time scenario. Comparison of the above algorithms is elicited.
In game design, Artificial intelligence is an important and complex module, and path-finding algorithms are one of the most fundamental problems applied to video games. According to the feature of path -finding in Pic...
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ISBN:
(纸本)9781424472352
In game design, Artificial intelligence is an important and complex module, and path-finding algorithms are one of the most fundamental problems applied to video games. According to the feature of path -finding in Picture Matching game, this paper analyzed a* algorithm applied to the game in detail, and pointed out the composition of estimate function. Combined with practical application, the paper given its realization.
Aimed to the path optimization problem,this paper considers these two elements:the path cost and time-consuming to reduce the complexity of the planning issue and raise a algorithm search efficiency,so that a searched...
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Aimed to the path optimization problem,this paper considers these two elements:the path cost and time-consuming to reduce the complexity of the planning issue and raise a algorithm search efficiency,so that a searched path is more reasonable,and the practical is stronger.
With the development of information technology and the coming of the internet of things,traffic signal control has brought new opportunities and *** paper presents a method of traffic network signal control,which used...
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With the development of information technology and the coming of the internet of things,traffic signal control has brought new opportunities and *** paper presents a method of traffic network signal control,which used to optimize traffic signal timing,reduce the delay of vehicles within the *** method is divided into two levels:intersection control level and network control *** control level uses a dynamic programming based adaptive *** control level uses an optimization algorithm based on conflict decision tree,using the a algorithm,rolling optimization and feedback control strategies to enhance the real-time,reliability and robustness of the *** effectiveness of the system has evaluated through an actual traffics network simulation conducted with *** show that the proposed method is significantly better than the time-of-day method in travel time,average delay and other parameters.
In this paper, a path planning method for autonomous mobile robots in a known indoor environment is proposed. A traditional A* algorithm modified by a weighted cost function is proposed. The factors of distance and sa...
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ISBN:
(纸本)9781457707148
In this paper, a path planning method for autonomous mobile robots in a known indoor environment is proposed. A traditional A* algorithm modified by a weighted cost function is proposed. The factors of distance and safety are considered simultaneously in the cost function so that the path planning can let the mobile robot reach its goal safely and quickly. Some simulation results are presented to illustrate the proposed method has a good path planning for mobile robots. The proposed method has also been implemented and tested on a real mobile robot. The experiment results illustrate that the proposed method can let the autonomous mobile robot have a safe path-planning in a known environment.
A solution to compute the optimal path based on a single-line-single-directional(SLSD)road network model is *** the traditional road network model,in the SLSD conceptual model,being single-directional and single-lin...
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A solution to compute the optimal path based on a single-line-single-directional(SLSD)road network model is *** the traditional road network model,in the SLSD conceptual model,being single-directional and single-line style,a road is no longer a linkage of road nodes but abstracted as a network ***,a road node is abstracted as the linkage of two ordered single-directional *** model can describe turn restrictions,circular roads,and other real scenarios usually described using a *** a computing framework for optimal path finding(OPF)is *** is proved that classical Dijkstra and a algorithms can be directly used for OPF computing of any real-world road networks by transferring a super-graph to an SLSD ***,using Singapore road network data,the proposed conceptual model and its corresponding optimal path finding algorithms are validated using a two-step optimal path finding algorithm with a pre-computing strategy based on the SLSD road network.
This paper brings forward a new method of describing a public transportation networks and a new multi-objective public transportation guidance model. By this description, shortest path problems in bus networks are tra...
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This paper brings forward a new method of describing a public transportation networks and a new multi-objective public transportation guidance model. By this description, shortest path problems in bus networks are transferred into shortest path problems in normal networks. The paper extends the A* algorithm to shortest route problems in public transportation guidance systems. Finally, the developed algorithm is implemented with the bus network of Guangzhou City containing 471 routes and 1040 stops. The results indicate that the description, multi-objective model and adaptation of A* algorithm proposed in this paper are very efficient.
The multiple alignment of the sequences of DNA and proteins is applicable to various important fields in molecular biology. Although the approach based on Dynamic Programming is well-known for this problem, it require...
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The multiple alignment of the sequences of DNA and proteins is applicable to various important fields in molecular biology. Although the approach based on Dynamic Programming is well-known for this problem, it requires enormous time and space to obtain the optimal alignment. On the other hand, this problem corresponds to the shortest path problem and the A* algorithm, which can efficiently find the shortest path with an estimator, is usable. First, this paper directly applies the A* algorithm to multiple sequence alignment problem with more powerful estimator in more than two-dimensional case and discusses the extensions of this approach utilizing an upper bound of the shortest path length and of modification of network structure. The algorithm to provide the upper bound is also proposed in this paper. The basic part of these results was originally shown in Ikeda and Imai [11]. This part is similar to the branch-and-bound techniques implemented in MSA program in Gupta ct al. [6]. Our framework is based on the edge length transformation to reduce the problem to the shortest path problem, which is more suitable to generalizations to enumerating suboptimal alignments and parametric analysis as done in Shibuya and Imai [15-17]. By this enhanced A* algorithm, optimal multiple alignments of several long sequences can be computed in practice, which is shown by computational results. Second, this paper proposes a k-group alignment algorithm for multiple alignment as a practical method for much larger-size problem of, say multiple alignments of 50-100 sequences. A basic part of these results were originally presented in Imai and Ikeda [13]. In existing iterative improvement methods for multiple alignment, the so-called group-to-group two-dimensional dynamic programming has been used, and in this respect our proposal is to extend the ordinary two-group dynamic programming to a k-group alignment programming. This extension is conceptually straightforward, and here our contribu
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