Sequential Classification of Palm Gestures Based on A* Algorithm and MLP Neural Network for Quadrocopter Control

Wodziński, Marek; Krzyżanowska, Aleksandra

doi:10.1515/mms-2017-0021

Cited by 7 publications

(4 citation statements)

References 19 publications

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“…In the domain of parallel algorithms, there have been notable efforts to address tree traversal and related problems. Wodziński and Krzyżanowska [6] introduced a parallel BFS algorithm that utilizes a multi-threaded approach to achieve faster exploration of tree structures. Additionally, Uenoet al [7] presented a parallel DFS variant employing task parallelism and synchronization techniques for graph traversal, demonstrating improved scalability on multi-core architectures.…”

Section: Related Workmentioning

confidence: 99%

Parallelizing Depth-First Search for Pathway Finding: A Comprehensive Investigation

Sangamesvarappa,

Vidyaathulasiraman

2023

RIA

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Search algorithms are integral to numerous applications in computer science. With the prevalence of multi-core processors in contemporary computing devices, the parallelization of search algorithms has surfaced as a viable strategy for achieving significant performance enhancements. This paper offers a detailed examination of the performance improvements garnered through the parallelization of search procedures, with a particular emphasis on the Depth-First Search (DFS) algorithm as it pertains to pathway discovery in binary trees. The primary aim of this study was to contrast the performance of the conventional sequential DFS approach with a novel parallel strategy designed to exploit the computational capabilities of multi-core processors. By capitalizing on the resources available in modern desktop and laptop computers, it was intended to markedly diminish the processing time necessary for examining all possible pathways in both symmetrical and asymmetrical binary trees. A meticulous experimental evaluation was conducted using a varied assortment of binary trees, spanning perfectly balanced to highly skewed structures, to ensure a thorough assessment of the effectiveness of both strategies. The primary metric employed for performance evaluation was the total processing time, a crucial consideration for time-critical applications. The experimental results confirmed the superiority of the parallelized method over the conventional sequential DFS approach. The parallel technique demonstrated significantly lower processing times for pathway discovery in all binary tree scenarios tested. These performance enhancements were particularly noticeable in larger and more complex trees, underscoring the potential of parallelization for managing computationally demanding tasks.

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Section: Related Workmentioning

confidence: 99%

Parallelizing Depth-First Search for Pathway Finding: A Comprehensive Investigation

Sangamesvarappa,

Vidyaathulasiraman

2023

RIA

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“…Path planning generally consists of global path planning and local path planning [ 31 ]. Global path planning is to select a whole path when the map is known, which mainly including ant colony optimization (ACO) [ 32 ] and A-star algorithm [ 33 ]. Global path planning relies on known static maps and it may not work in the dynamic environment.…”

Section: Related Workmentioning

confidence: 99%

Learning Reward Function with Matching Network for Mapless Navigation

Zhang

Zhu

Zou

et al. 2020

Sensors

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Deep reinforcement learning (DRL) has been successfully applied in mapless navigation. An important issue in DRL is to design a reward function for evaluating actions of agents. However, designing a robust and suitable reward function greatly depends on the designer’s experience and intuition. To address this concern, we consider employing reward shaping from trajectories on similar navigation tasks without human supervision, and propose a general reward function based on matching network (MN). The MN-based reward function is able to gain the experience by pre-training through trajectories on different navigation tasks and accelerate the training speed of DRL in new tasks. The proposed reward function keeps the optimal strategy of DRL unchanged. The simulation results on two static maps show that the DRL converge with less iterations via the learned reward function than the state-of-the-art mapless navigation methods. The proposed method performs well in dynamic maps with partially moving obstacles. Even when test maps are different from training maps, the proposed strategy is able to complete the navigation tasks without additional training.

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“…The A* algorithm was first proposed in 1968 by Hart et al [14], it is a heuristic search algorithm proposed by combining the advantages of the bestfirst search algorithm and Dijkstra's algorithm [37], [42]. The A* algorithm uses heuristic information to guide the search direction, which has been widely used to find the optimal solution in a short time, and has strong expansibility and adaptability to different scenarios.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Center Constraint Weighted A* Algorithm for Path Planning of Petrochemical Inspection Robot

Lai

Chambers

2021

J Intell Robot Syst

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In many practical applications of robot path planning, finding the shortest path is critical, while the response time is often overlooked but important. To address the problems of search node divergence and long calculation time in the A* routing algorithm in the large scenario, this paper presents a novel center constraint weighted A* algorithm (CCWA*). The heuristic function is modified to give different dynamic weights to nodes in different positions, and the node weights are changed in the specified direction during the expansion process, thereby reducing the number of search nodes. An adaptive threshold is further added to the heuristic function to enhance the adaptiveness of the algorithm. To verify the effectiveness of the CCWA* algorithm, simulations are performed on 2-dimensional grid maps of different sizes. The results show that the proposed algorithm speeds up the search process and reduces the planning time in the process of path planning in a multi-obstacle environment compared with the conventional A* algorithm and weighted A* algorithm.

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Sequential Classification of Palm Gestures Based on A* Algorithm and MLP Neural Network for Quadrocopter Control

Cited by 7 publications

References 19 publications

Parallelizing Depth-First Search for Pathway Finding: A Comprehensive Investigation

Parallelizing Depth-First Search for Pathway Finding: A Comprehensive Investigation

Learning Reward Function with Matching Network for Mapless Navigation

Enhanced Center Constraint Weighted A* Algorithm for Path Planning of Petrochemical Inspection Robot

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