PATH PLANNING OF UNMANNED AERIAL VEHICLE USING DUBINS GEOMETRY WITH AN OBSTACLE

Motivation: Unmanned Aerial Vehicles (UAV) is an aircraft that is controlled without the use of human beings crew. One of main problem of Unmanned Aerial Vehicle’s (UAV) flight is guide. UAV need a guide who can direct the movement of aircraft to arrive at the destination, so it takes planning trajectory (path planning) appropriate to the aircraft can be controlled in accordance with the objectives and can pass the desired trajectory. Given two points on a 2-dimensional plane, the two points are coordinates the initial and final coordinates to be taken by UAV. And between the two points is given a obstacle in the form circle. Planning algorithm trajectory using Dubins geometry. Results obtained from this paper are path planning to produce path between two points with the shortest distance and shortest time. The first track is the track without a obstacle and the second path is the path to the obstacle. Differences in distance on the second track is provided

Navigasi dan Kendali pada Pesawat Udara Nir Awak (Puna) untuk Menghindari Halangan

Pesawat udara nir awak(PUNA) adalah pesawat udara multifungsi yang dikendalikan tanpa menggunakan awak manusia. PUNA dapat bergerak sampai ke tempat tujuan jika diterapkan sebuah navigasi dan kendali. Masalah yang muncul pada pernerbangan PUNA diantaranya masalah jalur tempuh dan halangan pada lintasan. Navigasi penerbangan adalah proses mengarahkan posisi pesawat dari satu titik ke titik yang lain dengan selamat dan lancar untuk menghindari rintangan penerbangan. Navigasi yang digunakan adalah dengan merancang Algoritma perencanaan lintasan menggunakan geometri Dubins. Agar PUNA tetap pada lintasan yang dibangun maka diperlukan suau kendali optimal. Kendali yang digunakan adalah Prin- sip Minimum Pontryagin(PMP) yang berguna untuk meminimumkan atau memaksimumkan fungsi tujuan. Kasus yang diteliti dalam paper ini, yaitu PUNA bergerak mengikuti lintasan yang dibangun dengan metode geometri dubins. Hasil yang diperoleh dalam paper ini adalah mendapatkan suatu lintasan optimal untuk menghindari halangan berupa lingkaran

PESAWAT UDARA NIR AWAK (PUNA) DENGAN METODE ENSEMBLE KALMAN FILTER

Pesawat Udara Nir Awak (PUNA) adalah pesawat udara yang dilengkapi dengan kamera, sensor, radar, dan peralatan lainnya yang dikendalikan tanpa menggunakan awak manusia. PUNA dapat dimanfaatkan untuk peran pengintaian, pengumpulan data intelejen, kepentingan sipil dan memiliki kemampuan untuk menjalankan fungsi yang sama dengan satelit. PUNA membutuhkan sistem navigasi dan estimasi yang mampu mengarahkan pesawat bergerak mengikuti lintasan yang ada dengan posisi yang tepat dan sampai ke tujuan. Oleh karena itu dibutuhkan suatu metode navigasi dan estimasi posisi PUNA agar mudah dideteksi keberadaannya. Pada paper ini dilakukan uji performansi metode navigasi dan estimasi posisi pada PUNA yaitu menggunakan metode Dubins dan Ensemble Kalman Filter (EnKF). Hasil simulasi menunjukkan bahwa metode Dubins didapatkan lintasan yang optimal yaitu pada lintasan tanpa halangan dan lintasan dengan halangan. Sedangkan hasil simulasi pada metode EnKF pada kondisi nilai ???? tetap jauh lebih baik jika seluruh parameter bisa diukur dengan membangkitkan sebanyak 300 buah ensemble. Pada kondisi nilai ???? tidak tetap hasil estimasi jauh lebih baik jika seluruh parameter bisa dikur dengan membangkitkan sebanyak 200 buah ensemble

Real-Time Obstacle and Collision Avoidance System for Fixed-Wing Unmanned Aerial Systems

The motivation for the research presented in this dissertation is to provide a two-fold solution to the problem of non-cooperative reactive mid-air threat avoidance for fixed-wing unmanned aerial systems. The first phase is an offline UAS trajectory planning designed for an altitude-specific mission. The second phase leans on the results produced during the first phase to provide intelligent, real-time, reactive mid-air threat avoidance logic. That real-time operating logic provides a given fixed-wing UAS with local threat awareness so it can get a feel for the danger represented by a potential threat before using results produced during the first phase to require aircraft rerouting. The first original contribution of this research is the Advanced Mapping and Waypoint Generator (AMWG), a piece of software which processes publicly available elevation data in order to only retain the information necessary for a given altitude-specific flight mission. The AMWG is what makes systematic offline trajectory possible. The AMWG first creates altitude groups in order to discard elevations points which are not relevant to a specific mission because of the altitude flown at. Those groups referred to as altitude layers can in turn be reused if the original layer becomes unsafe for the altitude range in use, and the other layers are used for altitude re-scheduling in order to update the current altitude layer to a safer layer. Each layer is bounded by a lower and higher altitude, within which terrain contours are considered constant according to a conservative approach involving the principle of natural erosion. The AMWG then proceeds to obstacle contours extraction using threshold and edge detection vision algorithms. A simplification of those obstacle contours and their corresponding free space zones counterparts is performed using a fixed -tolerance Douglas-Peucker algorithm. This simplification allows free space zones to be described by vectors instead of point clouds, which enables UAS point location. The resulting geometry is then processed through a vertical trapezoidal decomposition where for each vertex defining a contour a vertical line is drawn, and the results of this decomposition is a set of trapezoidal cells. The cells corresponding to obstacle contours are then removed from the original trapezoidal decomposition in order to solely retain the obstacle-free trapezoidal cells. After decomposition, cells sharing part of a common edge are considered from a graph theory perspective so it becomes possible to list all acyclic paths between two cells by applying a depth first search (DFS) algorithm. The final product of the AWMG is a network of connected free space trapezoidal cells with embedded connectivity information referred to as the Synthetic Terrain Avoidance (STA network). The walls of the trapezoidal cells are then extruded as the AWMG essentially approximates a three-dimensional world by considering it as a stratification of two-dimensional layers, but the real-time phase needs 3D support. Using the graph conceptual view and the depth first search algorithm, all the connected cell sequences joining the departure to the arrival cell can be listed, a capability which is used during aircraft rerouting. By connecting two adjacent cells' centroids to their common midpoint located on the shared edge, the resulting flying legs remain within the two cells. The next step for paths between two cells is to be converted into flyable paths, and the conversion uses main and fallback methods to achieve that. The preferred method is the closed-form Dubins paths method involving the design of sequences of arc circle-straight line-arc circle (CLC) in order to account for the minimum radius turn constrain of the UAS. An additional geometric transformation is developed and applied to the initial waypoints used in the Dubins method so the flying leg directions are respected which is not possible by using the Dubins method alone. When consecutive waypoints are too close from one another, a condition called the Dubins condition cannot be respected, and the UAS trajectory design switches to the numerical integration of a system of ordinary differential equations accounting for the minimum turning constraint. Using the Dubins method and the ODE method makes it possible for the AWMG to design flyable offline trajectories accounting for the lateral dynamic of the fixed-wing UAS. The second original contribution of this research is the development and demonstration of the Double Dispersion reduction RRT (DDRRT), an algorithm which employs two new developed logic schemes respectively referred to as Punctual Dispersion Reduction (PDR), and Spatial Dispersion Reduction exploration (SDR). The DDRRT is employed during the real-time in-flight phase where it initially assumes a perfect terrain and no unpredictable threat, consequently following a 100% adaptive goal biasing toward the next waypoint in its list. When a threat such as an unpredicted obstacle is detected, the (PDR) acknowledges the fact that the DDRRT tree branches have met an obstacle and the its goal-biasing toward the next waypoint is decreased. If the PDR keeps decreasing, the DDRRT develops awareness of its surrounding obstacles by relaxing its PDR and switching to SDR which has the effect of increasing the dispersion of its branches, but keeping their extension bounded by the cell containing the last good position of the UAS, Csafe. If a number of branches reach a limit proportional to the Csafe and its relative area, then the STA network is queried for alternative rerouting. The two phases provide real-time reactive mid - air threat avoidance scenarios with the ability for a UAS to develop local and realistic threat awareness before considering intelligent rerouting. Either the local exploration of the DDRRT is successful before reaching a maximum number of points, or the STA Network is required to find another route

Adaptive nonlinear guidance law using neural networks applied to a quadrotor

© 2019IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.The NonLinear Guidance Law (NLGL) is a geometric algorithm commonly employed to solve the path following problem on different unmanned vehicles. NLGL is simple (does no depend on the model of the vehicle), effective and has only one tunning parameter. Its control parameter (L) depends on various factors, such as the velocity of the vehicle, the shape of the reference path and the dynamics of the vehicle. This paper analyses the effect of parameter L on the performance of NLGL when it is applied to a quadrotor vehicle. An Adaptive NLGL, which includes a velocity reduction term, is proposed. Stability proofs are given. Simulation results show that the proposed algorithm enhances the performance of the standard NLGL. Furthermore, it has no parameters to tune.Peer ReviewedPostprint (author's final draft

Real-Time Navigation and Flight Path Generation for Tracking Stop-and-Go Targets with Miniature Air Vehicles

This research effort focuses on using a heuristic approach to determine the optimal flight path required to put an Unmanned Aircraft System’s (UAS) sensor on a moving target in the presence of a constant wind field. This thesis builds on past work using dynamic optimization techniques to calculate minimum time to target. The computationally intensive dynamic optimization routines in their current form take a prohibitive amount of time to calculate and ultimately result in erroneous flight path predictions due to inherent execution time latencies. Therefore an iterative, suboptimal heuristic approach was explored to mitigate excessive calculation times and ultimately yield improved flight path predictions. This report not only explores the heuristic techniques used for flight path calculation, but also includes real world application and flight test results in a Micro Air Vehicle equipped with an autopilot

Navigasi dan Kendali pada Pesawat Udara Nir Awak (Puna) untuk Menghindari Halangan

Pesawat udara nir awak(PUNA) adalah pesawat udara multifungsi yang dikendalikan tanpa menggunakan awak manusia. PUNA dapat bergerak sampai ke tempat tujuan jika diterapkan sebuah navigasi dan kendali. Masalah yang muncul pada pernerbangan PUNA diantaranya masalah jalur tempuh dan halangan pada lintasan. Navigasi penerbangan adalah proses mengarahkan posisi pesawat dari satu titik ke titik yang lain dengan selamat dan lancar untuk menghindari rintangan penerbangan. Navigasi yang digunakan adalah dengan merancang Algoritma perencanaan lintasan menggunakan geometri Dubins. Agar PUNA tetap pada lintasan yang dibangun maka diperlukan suau kendali optimal. Kendali yang digunakan adalah Prin- sip Minimum Pontryagin(PMP) yang berguna untuk meminimumkan atau memaksimumkan fungsi tujuan. Kasus yang diteliti dalam paper ini, yaitu PUNA bergerak mengikuti lintasan yang dibangun dengan metode geometri dubins. Hasil yang diperoleh dalam paper ini adalah mendapatkan suatu lintasan optimal untuk menghindari halangan berupa lingkaran

Kendali optimal pada pesawat udara nir awak (PUNA) untuk mengikuti lintasan dengan halangan

Pesawat udara nir awak (PUNA) adalah pesawat udara yang dikendalikan tanpa menggunakan awak manusia. PUNA memiliki banyak kegunaan salah satunya dapat menjangkau daerah yang tidak dapat dijangkau manusia. Karena memiliki banyak kegunaan, PUNA membutuhkan sistem navigasi, panduan dan kendali agar dapat bergerak sesuai keinginan. Permasalahan yang paling sederhana dalam sistem navigasi adalah menemukan jalur dari posisi awal sampai ke tujuan dan menghindari tabrakan dengan halangan (obstacle). Jalur yang paling dibutuhkan adalah jalur terpendek dan waktu yang singkat untuk menempuhnya. Metode geometri Dubins adalah salah satu metode dalam perencanaan lintasan yang digunakan untuk menemukan jalur terpendek dengan membangkitkan kelengkungan garis dengan interpolasi kekontinuannya. Untuk menentukan waktu yang optimal digunakan metode Prinsip Minimum Pontryargin (PMP). Dalam Tesis ini metode geometri Dubins digunakan untuk menentukan pilihan lintasan terpendek dari beberapa kemungkinan lintasan yang ada. ======================================================================================================== Unmanned aircraft vehicle (UAV) is controlled aircraft without a human crew. UAV has many functions, one of which can reach areas inaccessible by humans. Because it has many functions, UAV require navigation systems, guidance and control in order to move as desired. The simplest problems in the navigation system is to find a path from the starting position to the destination and avoid a collision with an obstacle. The most path needed is the shortest path and a shortest time to take it. Dubins geometry method is one of the methods in the planning of the track that is used to find the shortest path is sought to evoke the curvature of the line by continuity interpolation. The optimal time is solved by UAV use one of the methods of optimal control that Pontryargin Minimum Principle (PMP). In this thesis Dubins geometry method used to determine the shortest path choice of several possible paths exist