Path Planning for Virtual Human Motion Using Improved A* Star Algorithm

Yao, Junfeng; Lin, Chen; Xie, Xiaolin; Wang, Andy JuAn; Hung, Chih‐Cheng

doi:10.1109/itng.2010.53

Cited by 166 publications

(93 citation statements)

References 3 publications

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“…For both experiments, we aimed at comparing and evaluating the solution quality obtained from these three methods in the perspectives of the target hit rate, the energy consumption, and the time step used by microrobot to deliver the drug to the target. To make a clearer analysis of the performance on the proposed algorithms, the A* search algorithm, a well-known path¯nding method, 39,40 is also included and can be used for comparison with the proposed method in these experiments.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Drug Delivery Based on Swarm Microrobots

Banharnsakun

Achalakul

Batra

2016

Int. J. Comp. Intel. Appl.

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Advances in the development of technology have led to microrobots applications in medical elds. Drug delivery is one of these applications in which microrobots deliver a pharmaceutical compound to targeted cells. Chemotherapy and its side e®ects can then be minimized by this method. Two major constraints, however, must be considered: the robot's onboard energy supply and the time needed for drug delivery. Furthermore, a microrobot must avoid biological restricted areas which we treat as obstacles in the path. The main objectives of this work were tō nd optimal paths to targeted cells and avoid collision with obstacles in the paths under a dynamic environment. In this study, we controlled motion of microrobots based on the concept of swarm intelligence. Arti¯cial Bee Colony (ABC), the Best-so-far ABC, and the Particle Swarm Optimization (PSO) methods were employed to implement the collision detection and the boundary distance detection modules. Forces that drove or resisted blood°ow as well as pressure in blood vessels were considered to approximate the e®ects of the environment on the microrobots. Numerical experiments were conducted using various obstacle environments. The results con¯rm that the proposed approaches were successful in avoiding obstacles and optimizing the energy consumption used to reach the target.

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Section: Experiments and Resultsmentioning

confidence: 99%

Drug Delivery Based on Swarm Microrobots

Banharnsakun

Achalakul

Batra

2016

Int. J. Comp. Intel. Appl.

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“…This algorithm uses a heuristic method to determine the optimal path between two points. Not only A* algorithm selects the next node based on the least cost from the initial vertex like Dijkstra algorithm (Dijkstra, 1959), but it also uses an estimation about the distance to the destination point (Yao et al, 2010). One of the most common estimations corresponding to the total length between origin and destination is Euclidean distance.…”

Section: Route Finding Using A* Algorithmmentioning

confidence: 99%

An Agent-Based Modelling for Ride Sharing Optimization Using A* Algorithm and Clustering Approach

Gorgoon

Davoodi

Motieyan

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Commission VI, WG VI/4 ABSTRACT:Today, city management is one of the great challenges facing the world. The growth of population, industries, and services is in urgent need of transportation on a large scale. Meanwhile, transportation has great importance in urban management. Therefore, it is necessary to solve the traffic problem with scientific methods and reduce the traffic load of cities. An interesting way to reduce urban travels is using 2,3, or 4 people from one car that it is known as "Ride Sharing". In this research, the NetLogo software is used to simulate travel sharing scenarios. The three considered parameters are the number of passengers, the acceptable travel sharing radius, and the acceptable waiting time. The proposed algorithm uses a clustering method to find the best candidates to share a ride. Several scenarios were performed to evaluate numerical results. The number of passengers was 100, and 500, the radius of the trip was 1,000 and 2,000 meters, and the waiting time was 10 and 20 minutes. So, 8 experiments were carried out. The least amount of travel sharing was observed in the first scenario (100 passengers, 1000 m travel sharing radius and 10 minutes waiting time), in which 2% of single trips dropped out. The most sharing trips were in the final scenario (500 passengers, 2000 meters radius and 20 minutes waiting time), which saw a decrease of 36.4% of single trips. So, it can be said that sharing a trip can reduce traffic in cities and consequently reduce urban costs and either air pollution or noise pollution.

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“…Figure 5 shows the active area covered by the deployed iBeacons and is accessible to the public. All our localization and navigation experiments (16,12)) and the bottom figures represent class 0 (from (0, 0) to (14,7)). are conducted in this area.…”

Section: A Datasetsmentioning

confidence: 99%

Path Planning in Support of Smart Mobility Applications Using Generative Adversarial Networks

Mohammadi

Al-Fuqaha

2018

2018 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and I

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This paper describes and evaluates the use of Generative Adversarial Networks (GANs) for path planning in support of smart mobility applications such as indoor and outdoor navigation applications, individualized wayfinding for people with disabilities (e.g., vision impairments, physical disabilities, etc.), path planning for evacuations, robotic navigations, and path planning for autonomous vehicles. We propose an architecture based on GANs to recommend accurate and reliable paths for navigation applications. The proposed system can use crowdsourced data to learn the trajectories and infer new ones. The system provides users with generated paths that help them navigate from their local environment to reach a desired location. As a use case, we experimented with the proposed method in support of a wayfinding application in an indoor environment. Our experiments assert that the generated paths are correct and reliable. The accuracy of the classification task for the generated paths is up to 99% and the quality of the generated paths has a mean opinion score of 89%.

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Path Planning for Virtual Human Motion Using Improved A* Star Algorithm

Cited by 166 publications

References 3 publications

Drug Delivery Based on Swarm Microrobots

Drug Delivery Based on Swarm Microrobots

An Agent-Based Modelling for Ride Sharing Optimization Using A* Algorithm and Clustering Approach

Path Planning in Support of Smart Mobility Applications Using Generative Adversarial Networks

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