An Efficient Multi-request Route Planning Framework Based on Grid Index and Heuristic Function

Author(s):  
Jiajia Li ◽  
Jiahui Hu ◽  
Vladislav Engel ◽  
Chuanyu Zong ◽  
Xiufeng Xia
2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Charis Ntakolia ◽  
Dimitris K. Iakovidis

AbstractRoute planning (RP) enables individuals to navigate in unfamiliar environments. Current RP methodologies generate routes that optimize criteria relevant to the traveling distance or time, whereas most of them do not consider personal preferences or needs. Also, most of the current smart wearable assistive navigation systems offer limited support to individuals with disabilities by providing obstacle avoidance instructions, but often neglecting their special requirements with respect to the route quality. Motivated by the mobility needs of such individuals, this study proposes a novel RP framework for assistive navigation that copes these open issues. The framework is based on a novel mixed 0–1 integer nonlinear programming model for solving the RP problem with constraints originating from the needs of individuals with disabilities; unlike previous models, it minimizes: (1) the collision risk with obstacles within a path by prioritizing the safer paths; (2) the walking time; (3) the number of turns by constructing smooth paths, and (4) the loss of cultural interest by penalizing multiple crossovers of the same paths, while satisfying user preferences, such as points of interest to visit and a desired tour duration. The proposed framework is applied for the development of a system module for safe navigation of visually impaired individuals (VIIs) in outdoor cultural spaces. The module is evaluated in a variety of navigation scenarios with different parameters. The results demonstrate the comparative advantage of our RP model over relevant state-of-the-art models, by generating safer and more convenient routes for the VIIs.


2010 ◽  
Vol 4 (9) ◽  
pp. 33-38
Author(s):  
Kashif Zafar ◽  
Abdul Rauf Baig ◽  
Ayesha Khan

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4150
Author(s):  
Barbara Siemiatkowska ◽  
Wojciech Stecz

This article presents a framework for planning a drone swarm mission in a hostile environment. Elements of the planning framework are discussed in detail, including methods of planning routes for drone swarms using mixed integer linear programming (MILP) and methods of detecting potentially dangerous objects using EO/IR camera images and synthetic aperture radar (SAR). Methods of detecting objects in the field are used in the mission planning process to re-plan the swarm’s flight paths. The route planning model is discussed using the example of drone formations managed by one UAV that communicates through another UAV with the ground control station (GCS). This article presents practical examples of using algorithms for detecting dangerous objects for re-planning of swarm routes. A novelty in the work is the development of these algorithms in such a way that they can be implemented on mobile computers used by UAVs and integrated with MILP tasks. The methods of detection and classification of objects in real time by UAVs equipped with SAR and EO/IR are presented. Different sensors require different methods to detect objects. In the case of infrared or optoelectronic sensors, a convolutional neural network is used. For SAR images, a rule-based system is applied. The experimental results confirm that the stream of images can be analyzed in real-time.


2009 ◽  
Vol 31 (6) ◽  
pp. 998-1012 ◽  
Author(s):  
Li TIAN ◽  
Peng ZOU ◽  
Ai-Ping LI ◽  
Yan JIA
Keyword(s):  

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