Algoritma Penghindaran Tabrakan pada Multi-Robot Berbasis Posisi dan Pembagian Orientasi

Multi-robot merupakan suatu sistem yang terdiri dari 2 atau lebih robot yang bekerja untuk menyelesaikan suatu tugas. Akan tetapi sering juga terjadi tabrakan antara satu robot dengan yang lain maupun halangan yang dihapi oleh robot. Tabrakan pada multi-robot juga sering terjadi karena robot tidak mengetahui posisi robot lain dan algoritma yang ditanamkan untuk menghidari tabrakan tersebut, kurang efisien sehingga sulit untuk Multi-Robot menyelesaikan suatu tugas dengan cepat. Pada penelitian ini akan bergerak di Bidang Multi-Robot yang terdiri dari 2 robot, dimana masing-masing robot bertujuan dapat menyelesaikan sebuah labirin yang tidak diketahui. Selama masing-masing robot berjalan, robot 1 dan robot 2 selalu membagi posisi mereka kepada satu sama lain, dan algoritma untuk penyelesaian labirin yaitu menggunakan algoritma Right Wall Follower(RWF) dengan pengimplementasian logika fuzzy. Dimana algorritma tersebut sekaligus menjadi algoritma untuk Penyelesaian maze dan penghindaran tabrakan antara robot dengan labirin dan antara satu robot dengan yang lain. Dengan metode yang diimplementasikan ke setiap robot untuk penghindaran tabrakan selama berada di dalam maze. Maka tingkat keberhasilan pada penghindara tabrakan yang terjadi didalam maze adalah 73%. Dan tingkat keberhasilan pada setiap robot berjalan mengikuti maze dengan metode RWF adalah 86%. Dengan waktu keberhasilan penyelesaian maze pada Multi-Robot adalah sekitar 3 sampai 7 menit. Kata kunci: Multi-Robot, algoritma penghindaran tabrakan, Right wall followe

Toward Task Capable Active Matter: Learning to Avoid Clogging in Confined Collectives via Collisions

Social organisms which construct nests consisting of tunnels and chambers necessarily navigate confined and crowded conditions. Unlike low density collectives like bird flocks and insect swarms in which hydrodynamic and statistical phenomena dominate, the physics of glasses and supercooled fluids is important to understand clogging behaviors in high density collectives. Our previous work revealed that fire ants flowing in confined tunnels utilize diverse behaviors like unequal workload distributions, spontaneous direction reversals and limited interaction times to mitigate clogging and jamming and thus maintain functional flow; implementation of similar rules in a small robophysical swarm led to high performance through spontaneous dissolution of clogs and clusters. However, how the insects learn such behaviors and how we can develop &ldquo;task capable&rdquo; active matter in such regimes remains a challenge in part because interaction dynamics are dominated by local, potentially time-consuming collisions and no single agent can survey and guide the entire collective. Here, hypothesizing that effective flow and clog mitigation could be generated purely by collisional learning dynamics, we challenged small groups of robots to transport pellets through a narrow tunnel, and allowed them to modify their excavation probabilities over time. Robots began excavation with equal probabilities to excavate and without probability modification, clogs and clusters were common. Allowing the robots to perform a &ldquo;reversal&rdquo; and exit the tunnel when they encountered another robot which prevented forward progress improved performance. When robots were allowed to change their reversal probabilities via both a collision and a self-measured (and noisy) estimate of tunnel length, unequal workload distributions comparable to our previous work emerged and excavation performance improved. Our robophysical study of an excavating swarm shows that despite the seeming complexity and difficulty of the task, simple learning rules can mitigate or leverage unavoidable features in task capable dense active matter, leading to hypotheses for dense biological and robotic swarms. &nbsp;</p

Lähestymistapa autonomiseen törmäyksenestoon Monorail-kuljetinjärjestelmässä

Collision Avoidance Systems are utilized both in industry and traffic in attempt to prevent material losses and injuries. These systems are implemented using sensors, communication systems or a combination of these. The most used sensors in collision avoidance applications are optical, electromagnetic and ultrasonic sensors. The communicationbased systems use both wireless and wired communications utilizing various protocols. This document describes the process of creating a prototype of a sensor-based Collision Avoidance System for Cimcorp Monorail Transfer system. Monorail Transfer is an automatic transportation system used in tire manufacturing plants for moving the tires between different process stations. It consists of a rail, which is either fastened into the roof of the plant or into a leg-like support structure, carriers which move along the rail and a cell controller. The aim is to prevent the collisions between the carriers and between carriers and other objects. The aim of the work is to have a functional prototype of an autonomous, sensor-based Collision Avoidance System which does not depend on Wi-Fi. The work begins with introducing the concept of Monorail Transfer in detail. Next the technologies behind the existing collision avoidance systems in industry and traffic are reviewed. The sensor types used in the implementations are identified and reviewed. Their suitability for the Monorail is considered. It is found that electromagnetic and optical sensors would be most suitable for the system. Electromagnetic sensors are discarded due to their high price and power consumption. Communication-based systems are reviewed. The Monorail Transfer’s current Collision Avoidance System is studied. After the theoretical part the new system is designed after defining its requirements. The sensors are chosen and reviewed. A Raspberry Pi 2 model B is chosen for pre-processing the sensor data prior to introducing it to the PLC’s in the Monorail Transfer. The Collision Avoidance software is programmed in Node.js, the communications between the PLC and Raspberry Pi are programmed in Node.js and the Graphical User Interface is implemented using HTML5, CSS and JS. The system is tested thoroughly and modified according to the findings. The prototype is found to be functional. Finally the document ends with conclusions about the implemented prototyp

Online Learning and Planning for Crowd-aware Service Robot Navigation

Mobile service robots are increasingly used in indoor environments (e.g., shopping malls or museums) among large crowds of people. To efficiently navigate in these environments, such a robot should be able to exhibit a variety of behaviors. It should avoid crowded areas, and not oppose the flow of the crowd. It should be able to identify and avoid specific crowds that result in additional delays (e.g., children in a particular area might slow down the robot). and to seek out a crowd if its task requires it to interact with as many people as possible. These behaviors require the ability to learn and model crowd behavior in an environment. Earlier work used a dataset of paths navigated by people to solve this problem. That approach is expensive, risks privacy violations, and can become outdated as the environment evolves. To overcome these drawbacks, this thesis proposes a new approach where the robot learns models of crowd behavior online and relies only on local onboard sensors. This work develops and tests multiple planners that leverage these models in simulated environments and demonstrate statistically significant improvements in performance. The work reported here is applicable not only to navigation to target locations, but also to a variety of other services

Swarm Robotic interactions in an open and cluttered environment: a survey

Recent population migrations have led to numerous accidents and deaths. Little research has been done to help migrants in their journey. For this reason, a literature review of the latest research conducted in previous years is required to identify new research trends in human-swarm interaction. This article presents a review of techniques that can be used in a robots swarm to find, locate, protect and help migrants in hazardous environment such as militarized zone. The paper presents a swarm interaction taxonomy including a detailed study on the control of swarm with and without interaction. As the interaction mainly occurs in cluttered or crowded environment (with obstacles) the paper discussed the algorithms related to navigation that can be included with an interaction strategy. It focused on comparing algorithms and their advantages and disadvantages

Entwurfsmuster für Fahrerlose Transportsysteme

Fahrerlose Transportsysteme (FTS) sind eine flexible Alternative zu starrer Fördertechnik. In dieser Arbeit werden Probleme und Lösungsansätze zur Gestaltung von FTS in Form von Entwurfsmustern aufgezeigt. Die Muster betrachten die Themen Modularität, Sicherheit, Mensch-Maschine-Interaktion, Navigation und dezentrale Steuerung. Sie dienen einerseits der Strukturierung von Lösungsansätzen und sind andererseits ein Hilfsmittel zur Entwicklung Fahrerloser Transportsysteme

Behavior-based multi-robot collision avoidance

Abstract-Autonomous robot teams that simultaneously dispatch transportation tasks are playing a more and more important role in the industry. In this paper we consider the multi-robot motion planning problem in large robot teams and present a decoupled approach by combining decentralized path planning methods and swarm technologies. Instead of a central coordination, a proper behavior which is directly selected according to the context is used by the robot to keep cooperating with others and to resolve path collisions. We show experimentally that the quality of solutions and the scalability of our method are significantly better than those of conventional decoupled path planning methods. Furthermore, compared to conventional swarm approaches, our method can be widely applied in large-scale environments