Author
Mehrnaz Hooshmand
Bio: Mehrnaz Hooshmand is an academic researcher from University of Tabriz. The author has contributed to research in topics: A* search algorithm & Isovist. The author has co-authored 1 publications.
Papers
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TL;DR: In this paper, a modified A* algorithm is used to generate multi-weighted graphs through pairs of POIs to propose the most suitable tour and facilitate traversal for a tourist who does not get involved with riding vehicles.
9 citations
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TL;DR: In this paper , a parameter adaptation-based ant colony optimization algorithm based on particle swarm optimization (PSO) algorithm with the global optimization ability, fuzzy system with the fuzzy reasoning ability and 3-Opt algorithm with local search ability, namely PF3SACO is proposed to improve the optimization ability and convergence, avoid to fall into local optimum.
103 citations
TL;DR: In this article , a detailed review of data mining techniques for structural health monitoring (SHM) applications is presented, where a brief background, models, functions, and classification of DM techniques are presented.
46 citations
TL;DR: In this article , the authors proposed a collision avoidance algorithm based on the dynamic window method and the knowledge of local collision avoidance theory to study the local path planning of USVs, and simulation experiments are carried out in different situations and environments containing unknown obstacles.
Abstract: 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.
1 citations
TL;DR: In this article , an APSO algorithm combining A* and PSO was proposed to calculate the optimal path for mobile robot path planning, where a redundant point removal strategy was adopted to preliminarily optimize the path and obtain the set of key nodes.
Abstract: Aiming at the problems of the A* algorithm in mobile robot path planning, such as multiple nodes, low path accuracy, long running time and difficult path initialization of particle swarm optimization, an APSO algorithm combining A* and PSO was proposed to calculate the optimal path. First, a redundant point removal strategy is adopted to preliminarily optimize the path planned by the A* algorithm and obtain the set of key nodes. Second, a stochastic inertia weight is proposed to improve the search ability of PSO. Third, a stochastic opposition-based learning strategy is proposed to further improve the search ability of PSO. Fourth, the global path is obtained by using the improved PSO to optimize the set of key nodes. Fifth, a motion time objective function that is more in line with the actual motion requirements of the mobile robot is used to evaluate the algorithm. The simulation results of path planning show that the path planned by APSO not only reduces the running time of the mobile robot by 17.35%, 14.84%, 15.31%, 15.21%, 18.97%, 15.70% compared with the A* algorithm in the six environment maps but also outperforms other path planning algorithms to varying degrees. Therefore, the proposed APSO is more in line with the actual movement of the mobile robot.
1 citations
TL;DR: In this paper , a modified flow direction optimization algorithm (MFDA) and firefly algorithm (FA) are hybridized and implemented on wheeled robots to encounter multi-target trajectory optimization with smooth navigation by negotiating obstacles present within the workspace.
Abstract: The autonomous robot has been the attraction point among robotic researchers since the last decade by virtue of increasing demand of automation in defence and intelligent industries. In the current research, a modified flow direction optimization algorithm (MFDA) and firefly algorithm (FA) are hybridized and implemented on wheeled robots to encounter multi-target trajectory optimization with smooth navigation by negotiating obstacles present within the workspace. Here, a hybrid algorithm is adopted for designing the controller with consideration of navigational parameters. A Petri-Net controller is also aided with the developed controller to resolve any conflict during navigation. The developed controller has been investigated on WEBOTS and MATLAB simulation environments coupled with real-time experiments by considering Khepera-II robot as wheeled robot. Single robot- multi-target, multiple robot single target and multiple robots-multiple target problems are tackled during the investigation. The outcomes of simulation are verified through real-time experimental outcomes by comparing results. Further, the proposed algorithm is tested for its suitability, precision, and stability. Finally, the developed controller is tested against existing techniques for authentication of proposed technique, and significant improvements of an average 34.2% is observed in trajectory optimization and 70.6% in time consumption.