MISSION-ADAPTABLE ROUTE PLANNING IN UNCERTAIN AND ADVERSARIAL ENVIRONMENT

2006 ◽  
Vol 15 (05) ◽  
pp. 803-821 ◽  
Author(s):  
PING YAN ◽  
MINGYUE DING ◽  
CHANGWEN ZHENG
Keyword(s):  
Real Time ◽  
A Priori ◽  
Route Planning ◽  
Optimal Solution ◽  
Dijkstra Algorithm ◽  
Solution Quality ◽  
Model Complex ◽  
Local Methods ◽  
Domain Specific Knowledge ◽  
Planning Algorithm

In this paper, the route-planning problems of Unmanned Aerial Vehicle (UAV) in uncertain and adversarial environment are addressed, including not only single-mission route planning in known a priori environment, but also the route replanning in partially known and mission-changeable environments. A mission-adaptable hybrid route-planning algorithm based on flight roadmap is proposed, which combines existing global and local methods (Dijkstra algorithm, SAS and D*) into a two-level framework. The environment information and constraints for UAV are integrated into the procedure of building flight roadmap and searching for routes. The route-planning algorithm utilizes domain-specific knowledge and operates in real time with near-optimal solution quality, which is important to uncertain and adversarial environment. Other planners do not provide all of the functionality, namely real-time planning and replanning, near-optimal solution quality, and the ability to model complex 3D constraints.

scholarly journals Accident Management System Based on Vehicular Network for an Intelligent Transportation System in Urban Environments

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Yusor Rafid Bahar Al-Mayouf ◽  
Omar Adil Mahdi ◽  
Namar A. Taha ◽  
Nor Fadzilah Abdullah ◽  
Suleman Khan ◽  
...  
Keyword(s):  
Real Time ◽  
Traffic Congestion ◽  
Urban Areas ◽  
Traffic Management ◽  
Management System ◽  
Vehicular Ad Hoc Networks ◽  
Road Traffic ◽  
Route Planning ◽  
Accident Management ◽  
Planning Algorithm

As cities across the world grow and the mobility of populations increases, there has also been a corresponding increase in the number of vehicles on roads. The result of this has been a proliferation of challenges for authorities with regard to road traffic management. A consequence of this has been congestion of traffic, more accidents, and pollution. Accidents are a still major cause of death, despite the development of sophisticated systems for traffic management and other technologies linked with vehicles. Hence, it is necessary that a common system for accident management is developed. For instance, traffic congestion in most urban areas can be alleviated by the real-time planning of routes. However, the designing of an efficient route planning algorithm to attain a globally optimal vehicle control is still a challenge that needs to be solved, especially when the unique preferences of drivers are considered. The aim of this paper is to establish an accident management system that makes use of vehicular ad hoc networks coupled with systems that employ cellular technology in public transport. This system ensures the possibility of real-time communication among vehicles, ambulances, hospitals, roadside units, and central servers. In addition, the accident management system is able to lessen the amount of time required to alert an ambulance that it is required at an accident scene by using a multihop optimal forwarding algorithm. Moreover, an optimal route planning algorithm (ORPA) is proposed in this system to improve the aggregate spatial use of a road network, at the same time bringing down the travel cost of operating a vehicle. This can reduce the incidence of vehicles being stuck on congested roads. Simulations are performed to evaluate ORPA, and the results are compared with existing algorithms. The evaluation results provided evidence that ORPA outperformed others in terms of average ambulance speed and travelling time. Finally, our system makes it easier for ambulance to quickly make their way through traffic congestion so that the chance of saving lives is increased.

Real-time Unmanned Aerial Vehicle Cruise Route Optimization for Road Segment Surveillance using Decomposition Algorithm

Robotica ◽  
2020 ◽  
pp. 1-16
Author(s):  
Xiaofeng Liu ◽  
Jian Ma ◽  
Dashan Chen ◽  
Li-Ye Zhang
Keyword(s):  
Real Time ◽  
Unmanned Aerial Vehicle ◽  
Route Planning ◽  
Traffic Monitoring ◽  
Route Optimization ◽  
Road Segment ◽  
Solution Quality ◽  
Planning Approach ◽  
Aerial Vehicle ◽  
Road Segments

SUMMARY Unmanned aerial vehicle (UAV) was introduced for nondeterministic traffic monitoring, and a real-time UAV cruise route planning approach was proposed for road segment surveillance. First, critical road segments are defined so as to identify the visiting and unvisited road segments. Then, a UAV cruise route optimization model is established. Next, a decomposition-based multi-objective evolutionary algorithm (DMEA) is proposed. Furthermore, a case study with two scenarios and algorithm sensitivity analysis are conducted. The analysis result shows that DMEA outperforms other two commonly used algorithms in terms of calculation time and solution quality. Finally, conclusions and recommendations on UAV-based traffic monitoring are presented.

scholarly journals Three-Dimensional Unmanned Aerial Vehicle Route Planning Using Hybrid Differential Evolution

2020 ◽  
Vol 24 (7) ◽  
pp. 820-828
Author(s):  
Hao Zhang ◽  
Lihua Dou ◽  
Chunxiao Cai ◽  
Bin Xin ◽  
◽  
...  
Keyword(s):  
Differential Evolution ◽  
Search Algorithm ◽  
Dynamic Properties ◽  
Three Dimensional ◽  
Route Planning ◽  
Performance Comparison ◽  
Solution Quality ◽  
De Algorithm ◽  
Planning Algorithm ◽  
Aerial Vehicle

Unmanned aerial vehicles (UAVs) have been investigated proactively owing to their promising applications. A route planner is key to UAV autonomous task execution. Herein, a hybrid differential evolution (HDE) algorithm is proposed to generate a high-quality and feasible route for fixed-wing UAVs in complex three-dimensional environments. A multiobjective function is designed, and both the route length and risk are optimized. Multiple constraints based on actual situations are considered, including UAV mobility, terrain, forbidden flying areas, and interference area constraints. Inspired by the wolf pack search algorithm, the proposed HDE algorithm combines differential evolution (DE) with an approaching strategy to improve the search capability. Moreover, considering the dynamic properties of fixed-wing UAVs, the quadratic B-spline curve is used for route smoothing. The HDE algorithm is compared with a state-of-the-art UAV route planning algorithm, i.e., the modified wolf pack search algorithm, and the traditional DE algorithm. Several numerical experiments are performed, and the performance comparison of algorithms shows that the HDE algorithm demonstrates better performances in terms of solution quality and constraint-handling ability in complex three-dimensional environments.

scholarly journals An Airborne Target Localization Algorithm Based on UKF and Optimal Maneuver Strategy

2018 ◽  
Vol 151 ◽  
pp. 05002
Author(s):  
Chunxiao Zhang ◽  
Xiaofeng Shi
Keyword(s):  
Route Planning ◽  
Optimal Solution ◽  
Monte Carlo Analysis ◽  
Single Step ◽  
Target Localization ◽  
Localization Algorithm ◽  
Localization Accuracy ◽  
Planning Algorithm ◽  
Estimation Covariance ◽  
Airborne Target

The airborne bearing-only passive target localization performance is relevant with the specific filter and maneuver model. This paper presents a combination of Unscented Kalman Filtering (UKF) with control inputs and optimal route planning algorithm to improve the performance. Taking the minimum trace of unbiased UKF estimation covariance matrix as criteria, single-step heading traverse method is applied to reach every optimal solution. A single flight path simulation and Monte Carlo analysis validate that the optimal strategy can improve both the convergence and localization accuracy.

scholarly journals Fast Solver for Implicit Continuous Set Model Predictive Control of Electric Drives

2021 ◽  
Author(s):  
Riccardo Torchio ◽  
Andrea Favato ◽  
Paolo Gherardo Carlet ◽  
Francesco Toso ◽  
Ruggero Carli ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Real Time ◽  
Predictive Control ◽  
A Priori ◽  
Optimal Solution ◽  
Experimental Tests ◽  
Current Control ◽  
Computational Time ◽  
Case Scenario ◽  
Worst Case

<div>This paper proposes an effective solver for implicit Continuous Set Model Predictive Control for the current control loop of synchronous motor drives with input constraints, allowing for reaching the maximum voltage feasible set. The related quadratic programming problem requires an iterative solver to find the optimal solution. The real-time certification of the algorithm is of paramount importance to move the technology toward industrial-scale applications by the proposed solver. The total number of operations can be computed in the worst-case scenario, thus the maximum computational time is known a priori. The solver is deeply illustrated, showing its feasibility for real-time applications in the microseconds range by means of experimental tests. Promising results are obtained with respect to well known general purpose solvers.</div>

scholarly journals Application of Angle Related Cost Function Optimization for Dynamic Path Planning Algorithm

Algorithms ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 127 ◽  
Author(s):  
Mingbin Zeng ◽  
Xu Yang ◽  
Mengxing Wang ◽  
Bangjiang Xu
Keyword(s):  
Path Planning ◽  
Real Time ◽  
Intelligent Transportation Systems ◽  
Optimal Solution ◽  
Transportation Systems ◽  
Function Optimization ◽  
Dynamic Path Planning ◽  
Planning Algorithm ◽  
Dynamic Path ◽  
Path Planning Algorithm

In recent years, Intelligent Transportation Systems (ITS) have developed a lot. More and more sensors and communication technologies (e.g., cloud computing) are being integrated into cars, which opens up a new design space for vehicular-based applications. In this paper, we present the Spatial Optimized Dynamic Path Planning algorithm. Our contributions are, firstly, to enhance the effective of loading mechanism for road maps by dividing the connected sub-net, and building a spatial index; and secondly, to enhance the effect of the dynamic path planning by optimizing the search direction. We use the real road network and real-time traffic flow data of Karamay city to simulate the effect of our algorithm. Experiments show that our Spatial Optimized Dynamic Path Planning algorithm can significantly reduce the time complexity, and is better suited for use as a real-time navigation system. The algorithm can achieve superior real-time performance and obtain the optimal solution in dynamic path planning.

Finding Near-Optimal Locations for Variable Message Signs for Real-Time Network Traffic Management

2003 ◽  
Vol 1856 (1) ◽  
pp. 34-53 ◽  
Author(s):  
Nathan Huynh ◽  
Yi-Chang Chiu ◽  
Hani S. Mahmassani
Keyword(s):  
Real Time ◽  
Network Traffic ◽  
Traffic Management ◽  
Dynamic Traffic Assignment ◽  
A Priori ◽  
Optimal Solution ◽  
Solution Procedure ◽  
Variable Message Signs ◽  
Message Signs ◽  
Short Time

This study addressed the problem of finding the best locations for portable variable message signs to divert traffic to alternative paths when an incident occurs so that impact on the network is minimized. The study proposed and evaluated a solution procedure for finding such locations in the context of real-time network traffic management. In this context, it was essential that the procedure find the solution to the formulated mathematical program in a relatively short time. The procedure relied on a heuristic to guide the search and a simulation-based dynamic traffic assignment program to evaluate the solution. The proposed heuristic combined principles of greedy and drop heuristics. To evaluate the proposed solution procedure, four sets of experiments were conducted on the Fort Worth, Texas, network. The results from the proposed solution procedure are compared with those obtained by other methods—( a) an a priori solution to a stochastic programming formulation, and ( b) the optimal solution with an exact (but slow to execute) procedure. It is found that the solutions obtained from the proposed solution procedure consistently outperform the a priori solutions and that they are consistently within 15% of the optimal solutions.

scholarly journals Fast Solver for Implicit Continuous Set Model Predictive Control of Electric Drives

2021 ◽  
Author(s):  
Riccardo Torchio ◽  
Andrea Favato ◽  
Paolo Gherardo Carlet ◽  
Francesco Toso ◽  
Ruggero Carli ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Real Time ◽  
Predictive Control ◽  
A Priori ◽  
Optimal Solution ◽  
Experimental Tests ◽  
Current Control ◽  
Computational Time ◽  
Case Scenario ◽  
Worst Case

<div>This paper proposes an effective solver for implicit Continuous Set Model Predictive Control for the current control loop of synchronous motor drives with input constraints, allowing for reaching the maximum voltage feasible set. The related quadratic programming problem requires an iterative solver to find the optimal solution. The real-time certification of the algorithm is of paramount importance to move the technology toward industrial-scale applications by the proposed solver. The total number of operations can be computed in the worst-case scenario, thus the maximum computational time is known a priori. The solver is deeply illustrated, showing its feasibility for real-time applications in the microseconds range by means of experimental tests. Promising results are obtained with respect to well known general purpose solvers.</div>

scholarly journals Indoor Path-Planning Algorithm for UAV-Based Contact Inspection

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 642
Author(s):  
Luis Miguel González de Santos ◽  
Ernesto Frías Nores ◽  
Joaquín Martínez Sánchez ◽  
Higinio González Jorge
Keyword(s):  
Path Planning ◽  
Real Time ◽  
Execution Time ◽  
Point Cloud ◽  
Indoor Environments ◽  
Calculation Algorithm ◽  
Time Restrictions ◽  
Planning Algorithm ◽  
Inspection Tasks ◽  
Path Planning Algorithm

Nowadays, unmanned aerial vehicles (UAVs) are extensively used for multiple purposes, such as infrastructure inspections or surveillance. This paper presents a real-time path planning algorithm in indoor environments designed to perform contact inspection tasks using UAVs. The only input used by this algorithm is the point cloud of the building where the UAV is going to navigate. The algorithm is divided into two main parts. The first one is the pre-processing algorithm that processes the point cloud, segmenting it into rooms and discretizing each room. The second part is the path planning algorithm that has to be executed in real time. In this way, all the computational load is in the first step, which is pre-processed, making the path calculation algorithm faster. The method has been tested in different buildings, measuring the execution time for different paths calculations. As can be seen in the results section, the developed algorithm is able to calculate a new path in 8–9 milliseconds. The developed algorithm fulfils the execution time restrictions, and it has proven to be reliable for route calculation.

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