Kendali Formasi Mobile Robot berdasarkan Jarak menggunakan Algoritma Cosinus

ABSTRAKKendali formasi adalah topik penelitian kendali multi-robot, dimana sekelompok robot dapat mencapai formasi tertentu dan mempertahankannya ketika berpindah ke arah yang diinginkan. Salah satu pengembangan kendali formasi adalah kendali formasi berdasarkan jarak dimana setiap individu robot menggunakan informasi jarak antara sesamanya untuk mencapai tujuan formasi. Banyak pengembangan yang dilakukan pada kendali formasi berdasarkan jarak menggunakan model yang sederhana dan membutuhkan pengembangan lebih lanjut untuk penerapan kendali ke model yang lebih nyata. Ketika penerapan kendali formasi berdasarkan jarak, terdapat permasalahan kondisi awal yaitu robot tidak dapat menentukan koordinat tetangganya. Penelitian ini akan mengembangkan algoritma cosinus sebagai solusi untuk kondisi awal kendali formasi berdasarkan jarak. Algoritma cosinus terinspirasi dari rumus segitiga sederhana dan mengharuskan robot melakukan dua langkah saja untuk dapat menemukan koordinat tetangganya. Hasil percobaan simulasi, kendali formasi berdasarkan jarak menggunakan tiga model robot holonomic dan penerapan algoritma cosinus membutuhkan waktu rata-rata 6.5 detik untuk menemukan koordinat tetangganya.Kata kunci: Kendali Formasi, Multi-Robot, Algoritma Cosinus, Mobile Robot. ABSTRACTFormation control is a research topic of multi-robot control, where a group of robots can reach a certain formation and defend it when moving in the desired direction. One of the developments is distance-based where formation goals achieved using the distance between each other only. Many developments are using a simple model and need further development into a realistic model. When applying distance-based, there is a problem in the initial condition, namely that the robot cannot find the coordinates of its neighbors when using only distance. In this work, the cosine algorithm was developed as a solution to the initial conditions which are inspired by a simple triangle formula and need only two steps to find the coordinates. From simulation experiment results, distance-based formation control using three holonomic robot models and the application of the cosine algorithm takes an average of 6.5 seconds to find the coordinates of its neighbors.Keywords: Formation Control, Multi-robot, Cosine Algorithm, Mobile Robot.

Extended Kalman Filter Enhanced by Neural Network to Solve the SLAM Problem

This work presents a way for online estimation of the location and mapping of a non-holonomic robot by means of an algorithm that uses EKF and in the output of this algorithm, a multilayer perceptron neural network (MLP) has been added that aims to improve the estimation of the robot pose in an unfamiliar environment. The effectiveness was proven through the comparison between the EKF-SLAM and the EKFMLP-SLAM, where it was evidenced a significant improvement in relation to the location of the poses of the robot.

An Integrated Strategy for Autonomous Exploration of Spatial Processes in Unknown Environments

Exploration of spatial processes, such as radioactivity or temperature is a fundamental task in many robotic applications. In the literature, robotic exploration is mainly carried out for applications where the environment is a priori known. However, for most real life applications this assumption often does not hold, specifically for disaster scenarios. In this paper, we propose a novel integrated strategy that allows a robot to explore a spatial process of interest in an unknown environment. To this end, we build upon two major blocks. First, we propose the use of GP to model the spatial process of interest, and process entropy to drive the exploration. Second, we employ registration algorithms for robot mapping and localization, and frontier-based exploration to explore the environment. However, map and process exploration can be conflicting goals. Our integrated strategy fuses the two aforementioned blocks through a trade-off between process and map exploration. We carry out extensive evaluations of our algorithm in simulated environments with respect to different baselines and environment setups using simulated GP data as a process at hand. Additionally, we perform experimental verification with a mobile holonomic robot exploring a simulated process in an unknown labyrinth environment. Demonstrated results show that our integrated strategy outperforms both frontier-based and GP entropy-driven exploration strategies.

Lane Tracking pada Robot Beroda Holonomic menggunakan Pengolahan Citra

ABSTRAK Robot merupakan teknologi yang dapat diterapkan bidang pertanian dan industri. Salah satu teknik navigasi robot yang dapat diterapkan di bidang pertanian dan industri adalah lane tracking. Untuk bernavigasi robot membutuhkan sebuah alat untuk mengenali lingkungannya, alat tersebut dapat berupa sensor atau kamera. Salah satu kelebihan menggunakan kamera jika dibandingkan dengan sensor adalah dapat mengurangi penggunaan perangkat keras untuk mengenali lingkungan robot. Fokus utama penelitian ini adalah membuat robot beroda holonomic untuk bernavigasi di antara dua garis yang berada di sebelah kiri dan kanan robot menggunakan kamera. Kamera digunakan untuk menangkap citra di depan robot, citra tersebut diolah disebuah SBC (Single Board Computer) untuk mendapatkan parameter jumlah pixel antara garis tengah robot dengan garis sebelah kanan dan kiri robot. Parameter tersebut kemudian diolah untuk menentukan kecepatan motor pada roda robot holonomic. Hasil yang diperoleh adalah dari setiap pengujian robot mampu bernavigasi pada jalur yang telah ditentukan. Kata kunci: Lane Tracking, Pengolahan Citra, Robot Beroda Holonomic ABSTRACT Robotic navigation techniques that can be applied in agriculture and industry is lane tracking. To navigate, robots need device to recognize the environment, the device can use sensors or cameras. The main focus of this research is to make holonomic wheeled robot to navigate between two lines located on the left and right of the robot using the camera. The camera is used to capture the image in front of the robot, the image is processed in an SBC (Single Board Computer) to get the paramters of the number of pixels between the center line with the right and left lines of the robot. These paramaters are the processed to determine the motor speed on the holonomic robot wheel. The result of each test is that the robot is able to navigate on a predetermined path. Keywords: Lane Tracking, Image Processing, Holonomic Wheeled Robot

Autonomous Area Exploration and Mapping in Underground Mine Environments by Unmanned Aerial Vehicles

SummaryIn this paper, we propose a method of using an autonomous flying robot to explore an underground tunnel environment and build a 3D map. The robot model we use is an extension of a 2D non-holonomic robot. The measurements and sensors we considered in the presented method are simple and valid in practical unmanned aerial vehicle (UAV) engineering. The proposed safe exploration algorithm belongs to a class of probabilistic area search, and with a mathematical proof, the performance of the algorithm is analysed. Based on the algorithm, we also propose a sliding control law to apply the algorithm to a real quadcopter in experiments. In the presented experiment, we use a DJI Guidance sensing system and an Intel depth camera to complete the localization, obstacle detection and 3D environment information capture. Furthermore, the simulations show that the algorithm can be implemented in sloping tunnels and with multiple UAVs.