scholarly journals Using Genetic Algorithms for Navigation Planning in Dynamic Environments

2012 ◽  
Vol 2012 ◽  
pp. 1-16 ◽  
Author(s):  
Ferhat Uçan ◽  
D. Turgay Altılar
Keyword(s):  
Fuel Consumption ◽  
Dynamic Environment ◽  
Dynamic Environments ◽  
Flight Path ◽  
Main Concern ◽  
Planning Problem ◽  
Process Unit ◽  
Flight Planning ◽  
Air Vehicle ◽  
Navigation Planning

Navigation planning can be considered as a combination of searching and executing the most convenient flight path from an initial waypoint to a destination waypoint. Generally the aim is to follow the flight path, which provides minimum fuel consumption for the air vehicle. For dynamic environments, constraints change dynamically during flight. This is a special case of dynamic path planning. As the main concern of this paper is flight planning, the conditions and objectives that are most probable to be used in navigation problem are considered. In this paper, the genetic algorithm solution of the dynamic flight planning problem is explained. The evolutionary dynamic navigation planning algorithm is developed for compensating the existing deficiencies of the other approaches. The existing fully dynamic algorithms process unit changes to topology one modification at a time, but when there are several such operations occurring in the environment simultaneously, the algorithms are quite inefficient. The proposed algorithm may respond to the concurrent constraint updates in a shorter time for dynamic environment. The most secure navigation of the air vehicle is planned and executed so that the fuel consumption is minimum.

Flight path optimization with application to in-flight replanning to changing destinations

2018 ◽  
Vol 90 (8) ◽  
pp. 1192-1202 ◽  
Author(s):  
Luitpold Babel
Keyword(s):  
Flight Path ◽  
Path Optimization ◽  
Minimum Length ◽  
Content Type ◽  
Flight Planning ◽  
Air Vehicle ◽  
Computationally Intensive ◽  
Shortest Path Algorithms ◽  
High Flexibility ◽  
Flight Path Optimization

Purpose The purpose of this paper is to present a new approach for finding a minimum-length trajectory for an autonomous unmanned air vehicle or a long-range missile from a release point with specified release conditions to a destination with specified approach conditions. The trajectory has to avoid obstacles and no-fly zones and must take into account the kinematic constraints of the air vehicle. Design/methodology/approach A discrete routing model is proposed that represents the airspace by a sophisticated network. The problem is then solved by applying standard shortest-path algorithms. Findings In contrast to the most widely used grids, the generated networks allow arbitrary flight directions and turn angles, as well as maneuvers of different strengths, thus fully exploiting the flight capabilities of the aircraft. Moreover, the networks are resolution-independent and provide high flexibility by the option to adapt density. Practical implications As an application, a concept for in-flight replanning of flight paths to changing destinations is proposed. All computationally intensive tasks are performed in a pre-flight planning prior to the launch of the mission. The in-flight planning is based entirely on precalculated data, which are stored in the onboard computer of the air vehicle. In particular, no path finding algorithms with high or unpredictable running time and uncertain outcome have to be applied during flight. Originality/value The paper presents a new network-based algorithm for flight path optimization that overcomes weaknesses of grid-based approaches and allows high-quality solutions. The method can be applied for quick in-flight replanning of flight paths.

Navigation of the S.S.T.

1967 ◽  
Vol 20 (2) ◽  
pp. 176-187
Author(s):  
J. Villiers
Keyword(s):  
Fuel Consumption ◽  
Traffic Control ◽  
Air Traffic Control ◽  
General Rule ◽  
Air Traffic ◽  
Flight Path ◽  
Point Of View ◽  
Optimum Trajectory ◽  
Flight System ◽  
Actual Position

As a general rule the navigational function is aimed at determining the position of the aircraft in order to resolve three types of problem:(1) To subject the aircraft's flight path to an optimum trajectory calculated before departure or progressively adapted in course of flight to the circumstances encountered.(2) To choose at each point of the selected flight path the flight system best adapted to the safety and economy of the flight.(3) Taking into account the presence of other aircraft in the airspace, to know and make known the actual position and the information allowing provision to be made for future positions, so as to permit effective air traffic control.Departures of the actual from the chosen flight path penalize the flight by a lowering of economy (in flying time or fuel consumption). It does not seem, however, that the problems raised from this point of view by S.S.T. are by nature or in difficulty any different from those which affect conventional aircraft. Taking into account the present-day precision of navigational aids there is every reason to believe that departures of the actual flight path from the optimum flight path will introduce a penalization which it is possible to ignore when compared with the penalization due to the inaccuracy of the knowledge of the elements (winds, temperatures, pressures) which have, in fact, served to determine this optimum flight path.

Effects of Descent Flight-Path Angle on Fuel Consumption of Commercial Aircraft

2019 ◽  
Vol 56 (1) ◽  
pp. 313-323 ◽  
Author(s):  
Enis T. Turgut ◽  
Oznur Usanmaz ◽  
Mustafa Cavcar ◽  
Tuncay Dogeroglu ◽  
Kadir Armutlu
Keyword(s):  
Fuel Consumption ◽  
Flight Path ◽  
Commercial Aircraft ◽  
Flight Path Angle

Multi-Robot SLAM in Dynamic Environments with Parallel Maps

2021 ◽  
pp. 2150011
Author(s):  
Sajad Badalkhani ◽  
Ramazan Havangi ◽  
Mohsen Farshad
Keyword(s):  
Large Scale ◽  
Dynamic Environment ◽  
Dynamic Environments ◽  
Extensive Literature ◽  
Real World Data ◽  
Data Set ◽  
Cooperative Approach ◽  
Localization And Mapping ◽  
Multi Robot

There is an extensive literature regarding multi-robot simultaneous localization and mapping (MRSLAM). In most part of the research, the environment is assumed to be static, while the dynamic parts of the environment degrade the estimation quality of SLAM algorithms and lead to inherently fragile systems. To enhance the performance and robustness of the SLAM in dynamic environments (SLAMIDE), a novel cooperative approach named parallel-map (p-map) SLAM is introduced in this paper. The objective of the proposed method is to deal with the dynamics of the environment, by detecting dynamic parts and preventing the inclusion of them in SLAM estimations. In this approach, each robot builds a limited map in its own vicinity, while the global map is built through a hybrid centralized MRSLAM. The restricted size of the local maps, bounds computational complexity and resources needed to handle a large scale dynamic environment. Using a probabilistic index, the proposed method differentiates between stationary and moving landmarks, based on their relative positions with other parts of the environment. Stationary landmarks are then used to refine a consistent map. The proposed method is evaluated with different levels of dynamism and for each level, the performance is measured in terms of accuracy, robustness, and hardware resources needed to be implemented. The method is also evaluated with a publicly available real-world data-set. Experimental validation along with simulations indicate that the proposed method is able to perform consistent SLAM in a dynamic environment, suggesting its feasibility for MRSLAM applications.

A survey on discrete space and continuous space facility layout problems

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Irappa Basappa Hunagund ◽  
V. Madhusudanan Pillai ◽  
Kempaiah U.N.
Keyword(s):  
Literature Review ◽  
Facility Layout ◽  
Dynamic Environment ◽  
Dynamic Environments ◽  
Structure Design ◽  
Discrete Space ◽  
Content Type ◽  
Continuous Space ◽  
Complete Problems ◽  
Flexible Bay Structure

Purpose The purpose of this paper is to review, evaluate and classify the academic research that has been published in facility layout problems (FLPs) and to analyse how researches and practices on FLPs are. Design/methodology/approach The review is based on 166 papers published from 1953 to 2021 in international peer-reviewed journals. The literature review on FLPs is presented under broader headings of discrete space and continuous space FLPs. The important formulations of FLPs under static and dynamic environments represented in the discrete and continuous space are presented. The articles reported in the literature on various representations of facilities for the continuous space Unequal Area Facility Layout Problems (UA-FLPs) are summarized. Discussed and commented on adaptive and robust approaches for dynamic environment FLPs. Highlighted the application of meta-heuristic solution methods for FLPs of a larger size. Findings It is found that most of the earlier research adopted the discrete space for the formulation of FLPs. This type of space representation for FLPs mostly assumes an equal area for all facilities. UA-FLPs represented in discrete space yield irregular shape facilities. It is also observed that the recent works consider the UA-FLPs in continuous space. The solution of continuous space UA-FLPs is more accurate and realistic. Some of the recent works on UA-FLPs consider the flexible bay structure (FBS) due to its advantages over the other representations. FBS helps the proper design of aisle structure in the detailed layout plan. Further, the recent articles reported in the literature consider the dynamic environment for both equal and unequal area FLPs to cope with the changing market environment. It is also found that FLPs are Non-deterministic Polynomial-complete problems, and hence, they set the challenges to researchers to develop efficient meta-heuristic methods to solve the bigger size FLPs in a reasonable time. Research limitations/implications Due to the extremely large number of papers on FLPs, a few papers may have inadvertently been missed. The facility layout design research domain is extremely vast which covers other areas such as cellular layouts, pick and drop points and aisle structure design. This research review on FLPs did not consider the papers published on cellular layouts, pick and drop points and aisle structure design. Despite the possibility of not being all-inclusive, the authors firmly believe that most of the papers published on FLPs are covered and the general picture presented on various approaches and parameters of FLPs in this paper are precise and trustworthy. Originality/value To the best of the authors’ knowledge, this paper reviews and classifies the literature on FLPs for the first time under the broader headings of discrete space and continuous space representations. Many important formulations of FLPs under static and dynamic environments represented in the discrete and continuous space are presented. This paper also provides the observations from the literature review and identifies the prospective future directions.

A Study of Airline Fuel Planning Optimization Using R-Ga

2020 ◽  
Vol 46 ◽  
pp. 189-197
Author(s):  
Ekene Gabriel Okafor ◽  
Osaretin Kole Uhuegho ◽  
Christopher Manshop ◽  
Paul Olugbeji Jemitola ◽  
Osichinaka Chiedu Ubadike
Keyword(s):  
Regression Analysis ◽  
Fuel Consumption ◽  
Large Time ◽  
Inequality Constraints ◽  
Flight Time ◽  
Planning Problem ◽  
Study Objective ◽  
Planning Optimization ◽  
Equality And Inequality Constraints ◽  
Human Effort

In this study, airline planning optimization problem based on ferry strategy was considered. Cost was the study objective function subject to forty equality and inequality constraints. Regression analysis as well a genetic algorithm (GA) was used to solve the problem. The mathematical relationship between flight fuel consumption and flight time was established using regression analysis, while GA was used for the optimization. The established mathematical model was used to predict the fuel consumption for the twenty scheduled flight consider based on their respective flight time. The result was found to be satisfactory, as optimal fuel lift plan was achieved in approximately twenty seconds of program run time, as against the large time usually spend using human effort to solve the fuel planning problem. The optimized fuel lift plan was compared with the actual fuel lift plan executed by the airline for the twenty scheduled flight considered. The result revealed thirty percent savings using the optimized plan in comparison to the actual fuel lift plan executed by the airline.

scholarly journals Evolution of simple multicellular life cycles in dynamic environments

2019 ◽  
Vol 16 (154) ◽  
pp. 20190054 ◽  
Author(s):  
Yuriy Pichugin ◽  
Hye Jin Park ◽  
Arne Traulsen
Keyword(s):  
Natural Selection ◽  
Reproductive Strategies ◽  
Population Growth Rate ◽  
Dynamic Environment ◽  
Dynamic Environments ◽  
Life Cycles ◽  
Matrix Population Model ◽  
Bet Hedging ◽  
Reproduction Mode ◽  
Natural Response

The mode of reproduction is a critical characteristic of any species, as it has a strong effect on its evolution. As any other trait, the reproduction mode is subject to natural selection and may adapt to the environment. When the environment varies over time, different reproduction modes could be optimal at different times. The natural response to a dynamic environment seems to be bet hedging, where multiple reproductive strategies are stochastically executed. Here, we develop a framework for the evolution of simple multicellular life cycles in a dynamic environment. We use a matrix population model of undifferentiated multicellular groups undergoing fragmentation and ask which mode maximizes the population growth rate. Counterintuitively, we find that natural selection in dynamic environments generally tends to promote deterministic, not stochastic, reproduction modes.

Modified Newton's method applied to potential field based navigation for nonholonomic robots in dynamic environments

Robotica ◽  
2008 ◽  
Vol 26 (3) ◽  
pp. 285-294 ◽  
Author(s):  
Jing Ren ◽  
Kenneth A. McIsaac ◽  
Rajni V. Patel
Keyword(s):  
Newton’S Method ◽  
Potential Field ◽  
Newton's Method ◽  
Dynamic Environment ◽  
Dynamic Environments ◽  
Field Methods ◽  
Control Laws ◽  
Nonholonomic Robots ◽  
Modified Newton’S Method ◽  
Speed Up

SUMMARYThis paper is to investigate inherent oscillations problems of Potential Field Methods (PFMs) for nonholonomic robots in dynamic environments. In prior work, we proposed a modification of Newton's method to eliminate oscillations for omnidirectional robots in static environment. In this paper, we develop control laws for nonholonomic robots in dynamic environment using modifications of Newton's method. We have validated this technique in a multirobot search-and-forage task. We found that the use of the modifications of Newton's method, which applies anywhere C2 continuous navigation functions are defined, can greatly reduce oscillations and speed up robot's movement, when compared to the standard gradient approaches.

3D UAV trajectory planning using evolutionary algorithms: A comparison study

2015 ◽  
Vol 119 (1220) ◽  
pp. 1271-1285 ◽  
Author(s):  
M. Bagherian ◽  
A. Alos
Keyword(s):  
Trajectory Planning ◽  
Three Dimensional ◽  
Particle Swarm ◽  
Geographic Information ◽  
Flight Path ◽  
Planning Problem ◽  
The Earth ◽  
Swarm Algorithms ◽  
Starting Point ◽  
Wide Range

Abstract This paper focuses on the three dimensional flight path planning for an unmanned aerial vehicle (UAV) on a low altitude terrain following\terrain avoidance mission. The UAV trajectory planning problem is to compute an optimal or near-optimal trajectory for a UAV to do its mission objectives in a surviving penetration through the hostile enemy environment, considering the shape of the earth and the kinematics constraints of the UAV. Using the three dimensional terrain information, three dimensional flight path from a starting point to an end point, minimising a cost function and regarding the kinematics constraints of the UAV is calculated. The geographic information of the earth shape and enemy locations is generated using digital terrain model (DTM) and geographic information system (GIS), and is displayed in a 3D environment. Using 3D-maps containing the geographic data accompanied by DTM, and GIS, the problem is modelled by deriving the motion equations of the UAV. Two heuristic algorithms are proposed for this problem: genetic and particle swarm algorithms. Genetic and particle swarm algorithms are general purposes algorithms, because they can solve a wide range of problems, so they have to be adjusted to solve the trajectory planning problem. To test and compare the paths obtained from these algorithms, a software program is built using GIS tools and the programming languages C# and MATLAB.

scholarly journals Navigation in Unknown Dynamic Environments Based on Deep Reinforcement Learning

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3837 ◽  
Author(s):  
Junjie Zeng ◽  
Rusheng Ju ◽  
Long Qin ◽  
Yue Hu ◽  
Quanjun Yin ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Domain Knowledge ◽  
Moving Objects ◽  
Dynamic Environment ◽  
Dynamic Environments ◽  
Continuous Control ◽  
Complex Environments ◽  
Reward Function ◽  
Knowledge Based ◽  
Task Architecture

In this paper, we propose a novel Deep Reinforcement Learning (DRL) algorithm which can navigate non-holonomic robots with continuous control in an unknown dynamic environment with moving obstacles. We call the approach MK-A3C (Memory and Knowledge-based Asynchronous Advantage Actor-Critic) for short. As its first component, MK-A3C builds a GRU-based memory neural network to enhance the robot’s capability for temporal reasoning. Robots without it tend to suffer from a lack of rationality in face of incomplete and noisy estimations for complex environments. Additionally, robots with certain memory ability endowed by MK-A3C can avoid local minima traps by estimating the environmental model. Secondly, MK-A3C combines the domain knowledge-based reward function and the transfer learning-based training task architecture, which can solve the non-convergence policies problems caused by sparse reward. These improvements of MK-A3C can efficiently navigate robots in unknown dynamic environments, and satisfy kinetic constraints while handling moving objects. Simulation experiments show that compared with existing methods, MK-A3C can realize successful robotic navigation in unknown and challenging environments by outputting continuous acceleration commands.

Sign in / Sign up

Export Citation Format

Share Document