<i>A Real-Time Route Planning Method Based On Morphological Segmentation for Plant Protection UAVs</i>

2019 ◽  
Author(s):  
Zhilun Yang ◽  
Yu Xiao ◽  
Zepeng Yang ◽  
Yalei Wu ◽  
Lijun Qi
Keyword(s):  
Real Time ◽  
Plant Protection ◽  
Route Planning ◽  
Planning Method ◽  
Morphological Segmentation

A Real-Time Route Planning Method Based on DeepLabV3+ for Plant Protection UAVs

2021 ◽  
Author(s):  
Junjie Wan ◽  
Lijun Qi ◽  
Hao Zhang ◽  
Zhong�ao Lu ◽  
Jiarui Zhou
Keyword(s):  
Real Time ◽  
Plant Protection ◽  
Route Planning ◽  
Planning Method

A real-time intelligent route planning method in geomagnetic navigation

2012 ◽  
Vol 2 (2) ◽  
pp. 125 ◽  
Author(s):  
Yuan Sun ◽  
Jinsheng Zhang ◽  
Shicheng Wang ◽  
Wei Jiao
Keyword(s):  
Real Time ◽  
Route Planning ◽  
Planning Method

scholarly journals Research on Ship Meteorological Route Based on A-Star Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Ge Chen ◽  
Tao Wu ◽  
Zheng Zhou
Keyword(s):  
Real Time ◽  
Optimal Path ◽  
Route Planning ◽  
Planning Method ◽  
Target Point ◽  
Convex Shape ◽  
Weather Information ◽  
Starting Point ◽  
Motion Characteristics ◽  
Initial Route

Ship meteorological navigation is based on hydrometeorological data of a certain time scale, considering the ship’s motion characteristics and its own characteristics. First, we provide the best route for the ship and then use real-time local weather information to correct the route during the ship’s navigation. It can also be expressed as follows: it is based on the hydrological and meteorological conditions of the ship during its voyage and the seakeeping characteristics of the ship itself, and the route planning method is used to select the best route for the ship. The best route is a balance between economy and safety, that is, based on ensuring the safety of ship navigation, the route that meets the shortest navigation time, the least fuel consumption, or the least navigation risk is obtained. Weather navigation includes the optimization of the initial route before sailing and the correction of the route after sailing. As there may be errors in hydrometeorological forecasts, especially in the accuracy and real-time performance of medium and long-term forecasts, the optimal initial route may not achieve the best results. Therefore, after the ship sails, it is necessary to adjust and correct the preferred initial route based on the meteorological information detected by the sensors or the continuously updated hydrometeorological forecast data to ensure the best effect of meteorological navigation. This paper proposes a weather route planning method based on the improved A-star algorithm. The convex shape of the concave obstacle and the expansion of the obstacle are carried out; according to the position of the target point relative to the starting point, the search direction of the A-star algorithm at each node is restricted, and an improved A-star algorithm is proposed. The simulation of global weather route planning shows that the improved A-star algorithm can not only find the optimal path but also effectively reduce the number of nodes that the algorithm needs to search during operation. Compared with the classic algorithm, the improved algorithm reduces the number of node searches by 29.25%.

scholarly journals Toward a Comfortable Driving Experience for a Self-Driving Shuttle Bus

Electronics ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 943 ◽  
Author(s):  
Il Bae ◽  
Jaeyoung Moon ◽  
Jeongseok Seo
Keyword(s):  
Real Time ◽  
High Performance ◽  
Autonomous Driving ◽  
Planning Method ◽  
Dynamics Simulation ◽  
Driving Experience ◽  
Optimal Velocity ◽  
Planning Approach ◽  
Velocity Planning ◽  
Time Optimal

The convergence of mechanical, electrical, and advanced ICT technologies, driven by artificial intelligence and 5G vehicle-to-everything (5G-V2X) connectivity, will help to develop high-performance autonomous driving vehicles and services that are usable and convenient for self-driving passengers. Despite widespread research on self-driving, user acceptance remains an essential part of successful market penetration; this forms the motivation behind studies on human factors associated with autonomous shuttle services. We address this by providing a comfortable driving experience while not compromising safety. We focus on the accelerations and jerks of vehicles to reduce the risk of motion sickness and to improve the driving experience for passengers. Furthermore, this study proposes a time-optimal velocity planning method for guaranteeing comfort criteria when an explicit reference path is given. The overall controller and planning method were verified using real-time, software-in-the-loop (SIL) environments for a real-time vehicle dynamics simulation; the performance was then compared with a typical planning approach. The proposed optimized planning shows a relatively better performance and enables a comfortable passenger experience in a self-driving shuttle bus according to the recommended criteria.

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