scholarly journals Research on aircraft route planning optimization problem with multi-constraints and dual-targets

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Qianyu Zhang ◽  
Xianfeng Ding ◽  
Jingyu Zhou ◽  
Yi Nie
Keyword(s):  
Path Length ◽  
Model Simulation ◽  
Optimal Path ◽  
Route Planning ◽  
Solution Time ◽  
Planning Model ◽  
Data Set ◽  
Positioning Errors ◽  
Core Technology ◽  
Planning Algorithm

Abstract As the core technology in the field of aircraft, the route planning has attracted much attention. However, due to the complexity of the structure and performance constraints of the aircraft, the route planning algorithm does not have well universality, so it cannot be used in a complex environment. In the paper, a multi-constraints and dual-targets aircraft route planning model was established for the real-time requirements of space flight, the dynamic changes of flight environment with time, the accuracy requirements of positioning errors in the safety area, and the minimum turning radius constraints. Based on the directed graph and dynamic programming ideas, the model simulation and model validation were carried out with the data of F question in the “16th Graduate Mathematical Modeling Contest”. The results showed that the optimal path length obtained in data set 1 was 124.61 km, the number of corrections was 11 times, the solution time was 2.3768 seconds, the optimal path length obtained in data set 2 was 110.00 km, and the number of corrections was 12 times. The solution time was 0.0168 seconds. Multi-constraints and dual-targets aircraft route planning model can plan the flight path of the aircraft intuitively and timely, which confirmed the effectiveness of the model.

PARP: A Parallel Traffic Condition Driven Route Planning Model on Dynamic Road Networks

2021 ◽  
Vol 12 (6) ◽  
pp. 1-24
Author(s):  
Tianlun Dai ◽  
Bohan Li ◽  
Ziqiang Yu ◽  
Xiangrong Tong ◽  
Meng Chen ◽  
...  
Keyword(s):  
Response Time ◽  
Road Networks ◽  
Route Planning ◽  
Location Based Services ◽  
Route Optimization ◽  
Planning Model ◽  
Traffic Condition ◽  
Traffic Conditions ◽  
Skeleton Graph ◽  
Planning Algorithm

The problem of route planning on road network is essential to many Location-Based Services (LBSs). Road networks are dynamic in the sense that the weights of the edges in the corresponding graph constantly change over time, representing evolving traffic conditions. Thus, a practical route planning strategy is required to supply the continuous route optimization considering the historic, current, and future traffic condition. However, few existing works comprehensively take into account these various traffic conditions during the route planning. Moreover, the LBSs usually suffer from extensive concurrent route planning requests in rush hours, which imposes a pressing need to handle numerous queries in parallel for reducing the response time of each query. However, this issue is also not involved by most existing solutions. We therefore investigate a parallel traffic condition driven route planning model on a cluster of processors. To embed the future traffic condition into the route planning, we employ a GCN model to periodically predict the travel costs of roads within a specified time period, which facilitates the robustness of the route planning model against the varying traffic condition. To reduce the response time, a Dual-Level Path (DLP) index is proposed to support a parallel route planning algorithm with the filter-and-refine principle. The bottom level of DLP partitions the entire graph into different subgraphs, and the top level is a skeleton graph that consists of all border vertices in all subgraphs. The filter step identifies a global directional path for a given query based on the skeleton graph. In the refine step, the overall route planning for this query is decomposed into multiple sub-optimizations in the subgraphs passed through by the directional path. Since the subgraphs are independently maintained by different processors, the sub-optimizations of extensive queries can be operated in parallel. Finally, extensive evaluations are conducted to confirm the effectiveness and superiority of the proposal.

Multi-Helicopter Search and Rescue Route Planning Based on Strategy Optimization Algorithm

2018 ◽  
Vol 33 (01) ◽  
pp. 1950002 ◽  
Author(s):  
Quan Shao ◽  
Chenchen Xu ◽  
Yan Zhu
Keyword(s):  
Ant Colony Algorithm ◽  
Route Planning ◽  
Behavioral Model ◽  
Search And Rescue ◽  
Planning Model ◽  
Cooperative Search ◽  
Rescue Operation ◽  
Algorithm Efficiency ◽  
Planning Algorithm ◽  
Efficient Route

This paper attempts to develop an efficient route planning algorithm to guide the operations of the multi-helicopter search and rescue in emergency. Route planning model of multi-helicopter cooperative search and rescue activity was established first, based on preference ordering of search and rescue objectives, as well as behavioral model of rescue helicopter and on-board detector. Given the route planning model, a multi-helicopter search and rescue route planning general algorithm was developed. The operation mechanism of ant colony algorithm was improved by introducing cooperative modes and the pheromone updating mechanism into existing methods. Furthermore, two cooperative search and rescue modes were studied: one is Overall Cooperative Search and Rescue Mode (OCSARM), in which many ants search and rescue the same region all together; the other is Blocking Cooperative Search and Rescue Mode (BCSARM), which partitions the region into small blocks and appoints helicopter with corresponding performance capabilities. Simulated experiments were developed to test the operability of proposed multi-helicopter search and rescue route planning algorithm. The comparison with existing algorithm shows that the algorithm proposed in this paper reduces computational complexity and evidently enhances algorithm efficiency. Results also indicate that this algorithm not only has the capability of comparing efficiency of two search and rescue modes in different mission requirements but also helps select search and rescue modes before rescue operation.

scholarly journals Path Planning Algorithm for Mobile Robot Based on Improved Ant Colony Algorithm

2021 ◽  
Vol 2083 (4) ◽  
pp. 042033
Author(s):  
Yanyun Li ◽  
Fenggang Liu
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Path Length ◽  
Ant Colony Algorithm ◽  
Planning Process ◽  
Optimal Path ◽  
Ant Colony ◽  
Position Information ◽  
Environment Model ◽  
Planning Algorithm

Abstract Due to the influence of full traversal environment, the path length obtained by existing methods is too long. In order to improve the performance of path planning and obtain the optimal path, a full traversal path planning method for omnidirectional mobile robots based on ant colony algorithm is proposed. On the basis of the topology modeling schematic diagram, according to the position information of the mobile robot in the original coordinate system, a new environment model is established by using the Angle transformation. Considering the existing problems of ant colony algorithm, the decline coefficient is introduced into the heuristic function to update the local pheromone, and the volatility coefficient of the pheromone is adjusted by setting the iteration threshold. Finally, through the design of path planning process, the planning of omnidirectional mobile robot’s full traversal path is realized. Experimental results show that the proposed method can not only shorten the full traversal path length, but also shorten the time of path planning to obtain the optimal path, thus improving the performance of full traversal path planning of omnidirectional mobile robot.

scholarly journals The Gradational Route Planning for Aircraft Stealth Penetration Based on Genetic Algorithm and Sparse A-Star Algorithm

2018 ◽  
Vol 151 ◽  
pp. 04001 ◽  
Author(s):  
Li Maoquan ◽  
Zhang Yunfei ◽  
Li Shihao
Keyword(s):  
Genetic Algorithm ◽  
Detection Probability ◽  
Optimal Path ◽  
Route Planning ◽  
Goal Function ◽  
Optimal Route ◽  
Optimal Path Planning ◽  
Planning Algorithm ◽  
Global Optimal ◽  
The Cost

It is established for a gradational route planning algorithm which includes two layers. The first layer makes use of genetic algorithm to obtain the global optimal path by its global optimal characteristics. The second layer makes use of A* algorithm to obtain the local optimal path by its dynamic characteristic. When flying along the global optimal path, locating the new threat and confirming its performance, the aircraft can plan the local optimal path timely by A* algorithm. It is constructed for the cost function with two goals of the range and the average detection probability, which is used as the goal function for optimal path planning. Two paths that obtained from two optimal methods are merged to construct the optimal route comprehensively considering the threats and range. The simulation result shows that the cost of new optimal route is lower than the original optimal path obtained only by the genetic algorithm.It revealed that our algorithm could obtain an optimal path when a new radar threas occured.

Recent advances on urban taxi sharing systems with the path arrival probability

2020 ◽  
Vol 13 ◽  
Author(s):  
Dui Hongyan ◽  
Zhang Chi
Keyword(s):  
Failure Probability ◽  
Traffic Congestion ◽  
Optimal Strategy ◽  
Time Windows ◽  
Optimal Path ◽  
Route Planning ◽  
Driving Time ◽  
Load Rate ◽  
Travel Efficiency ◽  
Arrival Probability

Background : Taxi sharing is an emerging transportation arrangement that helps improve the passengers’ travel efficiency and reduce costs. This study proposes an urban taxi sharing system. Methods: Considering each side congestion of the transport network, their corresponding reliability and failure probability are analyzed. Under the constraints of the number of passengers and their own time windows, the analysis is performed on passengers whose optimal path is inclusive. Results: According to the optimal strategy, the different passengers can be arranged into the same taxi to realize the taxi sharing. Then the shared taxi route can be optimized. Conclusion: Due to the reasonable vehicle route planning and passenger combination, these can effectively alleviate the traffic congestion, save the driving time, reduce the taxi no-load rate, and save the driving distance. At last, a numerical example is used to demonstrate the proposed method.

scholarly journals A 2D Optimal Path Planning Algorithm for Autonomous Underwater Vehicle Driving in Unknown Underwater Canyons

2021 ◽  
Vol 9 (3) ◽  
pp. 252
Author(s):  
Yushan Sun ◽  
Xiaokun Luo ◽  
Xiangrui Ran ◽  
Guocheng Zhang
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Autonomous Underwater Vehicles ◽  
Optimal Path ◽  
Small Scale ◽  
Target Point ◽  
Safe Driving ◽  
Policy Gradient ◽  
Planning Algorithm ◽  
Path Planning Algorithm

This research aims to solve the safe navigation problem of autonomous underwater vehicles (AUVs) in deep ocean, which is a complex and changeable environment with various mountains. When an AUV reaches the deep sea navigation, it encounters many underwater canyons, and the hard valley walls threaten its safety seriously. To solve the problem on the safe driving of AUV in underwater canyons and address the potential of AUV autonomous obstacle avoidance in uncertain environments, an improved AUV path planning algorithm based on the deep deterministic policy gradient (DDPG) algorithm is proposed in this work. This method refers to an end-to-end path planning algorithm that optimizes the strategy directly. It takes sensor information as input and driving speed and yaw angle as outputs. The path planning algorithm can reach the predetermined target point while avoiding large-scale static obstacles, such as valley walls in the simulated underwater canyon environment, as well as sudden small-scale dynamic obstacles, such as marine life and other vehicles. In addition, this research aims at the multi-objective structure of the obstacle avoidance of path planning, modularized reward function design, and combined artificial potential field method to set continuous rewards. This research also proposes a new algorithm called deep SumTree-deterministic policy gradient algorithm (SumTree-DDPG), which improves the random storage and extraction strategy of DDPG algorithm experience samples. According to the importance of the experience samples, the samples are classified and stored in combination with the SumTree structure, high-quality samples are extracted continuously, and SumTree-DDPG algorithm finally improves the speed of the convergence model. Finally, this research uses Python language to write an underwater canyon simulation environment and builds a deep reinforcement learning simulation platform on a high-performance computer to conduct simulation learning training for AUV. Data simulation verified that the proposed path planning method can guide the under-actuated underwater robot to navigate to the target without colliding with any obstacles. In comparison with the DDPG algorithm, the stability, training’s total reward, and robustness of the improved Sumtree-DDPG algorithm planner in this study are better.

A Perception-Based Fuzzy Route Planing Algorithm for Autonomous Unmanned Ground Vehicles

2018 ◽  
Vol 06 (04) ◽  
pp. 251-266
Author(s):  
Phillip J. Durst ◽  
Christopher T. Goodin ◽  
Cindy L. Bethel ◽  
Derek T. Anderson ◽  
Daniel W. Carruth ◽  
...  
Keyword(s):  
Path Planning ◽  
Urban Area ◽  
Route Planning ◽  
Unmanned Ground Vehicles ◽  
Membership Functions ◽  
Ground Vehicles ◽  
Ground Vehicle ◽  
Sensor Error ◽  
Planning Algorithm ◽  
Error Costs

Path planning plays an integral role in mission planning for ground vehicle operations in urban areas. Determining the optimum path through an urban area is a well-understood problem for traditional ground vehicles; however, in the case of autonomous unmanned ground vehicles (UGVs), additional factors must be considered. For an autonomous UGV, perception algorithms rather than platform mobility will be the limiting factor in operational capabilities. For this study, perception was incorporated into the path planning process by associating sensor error costs with traveling through nodes within an urban road network. Three common perception sensors were used for this study: GPS, LIDAR, and IMU. Multiple set aggregation operators were used to blend the sensor error costs into a single cost, and the effects of choice of aggregation operator on the chosen path were observed. To provide a robust path planning ability, a fuzzy route planning algorithm was developed using membership functions and fuzzy rules to allow for qualitative route planning in the case of generalized UGV performance. The fuzzy membership functions were then applied to several paths through the urban area to determine what sensors were optimized in each path to provide a measure of the UGV’s performance capabilities. The research presented in this paper shows the impacts that sensing/perception has on ground vehicle route planning by demonstrating a fuzzy route planning algorithm constructed by using a robust rule set that quantifies these impacts.

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