Robotic Indoor Path Planning Using Dijkstra's Algorithm with Multi-Layer Dictionaries

Fadzli, Syed Abdullah; Abdulkadir, Sani Iyal; Makhtar, Mokhairi; Jamal, Azrul Amri

doi:10.1109/icissec.2015.7371031

Cited by 39 publications

(17 citation statements)

References 4 publications

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“…FA is known to work well in solving engineering optimization problems with avoidance of local minima situations. 2,3 To compare the suitability of FA in generating the required turning angle (TA) based on the above mentioned objective function, it has been compared against Dijkstra's algorithm (DA), 48 A* algorithm (AA), 49 particle swarm optimization (PSO) 50 and ant colony optimization (ACO). 51 Table 1 represents a comparison of FA against other metaheuristic approaches in generation of the required TA.…”

Section: Global Fitness Functionmentioning

confidence: 99%

“…However, it is simultaneously important to validate the proposed controller against other existing navigational models. To do the same, the proposed FA based approach has been compared against Dijkstra's algorithm, 48 A* algorithm, 49 particle swarm optimization 50 and ant colony optimization. 51 Here, the simulations scenes represented in the current work are considered as graphs and each algorithm has been run separately on the same.…”

Section: Validation Of Fa-hsv Controller Against Other Existing Navigational Modelmentioning

confidence: 99%

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Humanoid navigation: A firefly based approach

Kumar

Parhi

Sethy

2021

Computer Animation & Virtual

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Humanoid navigation has been the center of attraction among robotic researchers since many years by virtue of their increasing use in industrial automation and smart manufacturing. In the current research, a firefly based computer vision integrated navigational analysis has been performed on humanoid robots for smooth movement by negotiation with obstacles present in complicated terrains. Here, the logic of the firefly algorithm has been used to design the controller by careful consideration of the navigational parameters. A computer vision based method is integrated along with the developed controller to resolve some conflicting situations that may arise by encountering large sized obstacle or detection of an obstacle exactly in front of the robot where the robot becomes confused regarding the direction of turn. The developed navigational model has been tested in simulation environments by considering NAO robot as the humanoid platform. The simulation results are also verified through an experimental platform developed under research laboratory conditions. The data obtained from both the platforms are compared in terms of navigational parameters and are found to be in close proximity to each other.Finally, the developed navigational model is also authenticated against other existing models for validating better efficiency.

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Section: Global Fitness Functionmentioning

confidence: 99%

Section: Validation Of Fa-hsv Controller Against Other Existing Navigational Modelmentioning

confidence: 99%

Humanoid navigation: A firefly based approach

Kumar

Parhi

Sethy

2021

Computer Animation & Virtual

View full text Add to dashboard Cite

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“…According to [58], Dijkstra's algorithm is typically used in the indoor environment because it is easy to implement for various environments. Dijkstra's algorithm is a special form of dynamic programming and a breadth-first-search algorithm for finding the shortest paths from a single source vertex to all other vertices [59]. The Fig.…”

Section: Path Planning Using Dijkstra's Algorithmmentioning

confidence: 99%

Wi-Fi indoor positioning and navigation: a cloudlet-based cloud computing approach

Khanh

Nguyen

Pham

et al. 2020

Hum. Cent. Comput. Inf. Sci.

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Increasing demands for location-based services require accurate wireless indoor location information. Location-based services include indoor navigation for people or robots, personnel, asset tracking, guiding blind people, factory automation, workplace safety, locating patients in a hospital, and location-based advertising [1]. Additionally, such services are becoming essential in various other fields such as mobile commerce, parcel or vehicle tracking, discovering the nearest shops or restaurants, and social

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“…Path planning is one of the key technologies in navigation system, which usually includes the global as well as local planners [ 6 , 7 ]. The global planner searches the global map to obtain a feasible path by means of planning algorithms, such as Dijkstra, A-star (A*), PRM, Rapidly Exploring Random Tree (RRT) algorithms, and so on [ 8 , 9 , 10 , 11 , 12 ]. The Dijkstra is a classic algorithm for finding the shortest path between two points due to its optimal capability for global planning.…”

Section: Introductionmentioning

confidence: 99%

Deep Reinforcement Learning for Indoor Mobile Robot Path Planning

Gao

Guo

et al. 2020

Sensors

110

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This paper proposes a novel incremental training mode to address the problem of Deep Reinforcement Learning (DRL) based path planning for a mobile robot. Firstly, we evaluate the related graphic search algorithms and Reinforcement Learning (RL) algorithms in a lightweight 2D environment. Then, we design the algorithm based on DRL, including observation states, reward function, network structure as well as parameters optimization, in a 2D environment to circumvent the time-consuming works for a 3D environment. We transfer the designed algorithm to a simple 3D environment for retraining to obtain the converged network parameters, including the weights and biases of deep neural network (DNN), etc. Using these parameters as initial values, we continue to train the model in a complex 3D environment. To improve the generalization of the model in different scenes, we propose to combine the DRL algorithm Twin Delayed Deep Deterministic policy gradients (TD3) with the traditional global path planning algorithm Probabilistic Roadmap (PRM) as a novel path planner (PRM+TD3). Experimental results show that the incremental training mode can notably improve the development efficiency. Moreover, the PRM+TD3 path planner can effectively improve the generalization of the model.

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Robotic Indoor Path Planning Using Dijkstra's Algorithm with Multi-Layer Dictionaries

Cited by 39 publications

References 4 publications

Humanoid navigation: A firefly based approach

Humanoid navigation: A firefly based approach

Wi-Fi indoor positioning and navigation: a cloudlet-based cloud computing approach

Deep Reinforcement Learning for Indoor Mobile Robot Path Planning

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