In mobile robotics, LASER scanners have a wide spectrum of indoor and outdoor applications, both in structured and unstructured environments, due to their accuracy and precision. Most works that use this sensor have their own data representation and their own case-specific modeling strategies, and no common formalism is adopted. To address this issue, this manuscript presents an analytical approach for the identification and localization of objects using 2D LiDARs. Our main contribution lies in formally defining LASER sensor measurements and their representation, the identification of objects, their main properties, and their location in a scene. We validate our proposal with experiments in generic semi-structured environments common in autonomous navigation, and we demonstrate its feasibility in multiple object detection and identification, strictly following its analytical representation. Finally, our proposal further encourages and facilitates the design, modeling, and implementation of other applications that use LASER scanners as a distance sensor.
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BACKGROUND: Rapid technological development has been opening new possibilities for children with disabilities. In particular, robots can enable and create new opportunities in therapy, rehabilitation, education, or leisure. OBJECTIVE: The aim of this article is to share experiences, challenges and learned lessons by the authors, all of them with experience conducting research in the field of robotics for children with disabilities, and to propose future directions for research and development. METHODS: The article is the result of several consensus meetings to establish future research priorities in this field. CONCLUSIONS: This article outlines a research agenda for the future of robotics in childcare and supports the establishment of R4C – Robots for Children, a network of experts aimed at sharing ideas, promoting innovative research, and developing good practices on the use of robots for children with disabilities. RESULTS: Robots have a huge potential to support children with disabilities: they can play the role of a play buddy, of a mediator when interacting with other children or adults, they can promote social interaction, and transfer children from the role of a spectator of the surrounding world to the role of an active participant. To fulfill their potential, robots have to be “smart”, stable and reliable, easy to use and program, and give the just-right amount of support adapted to the needs of the child. Interdisciplinary collaboration combined with user centered design is necessary to make robotic applications successful. Furthermore, real-life contexts to test and implement robotic interventions are essential to refine them according to real needs.
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Even though considerable amounts of valuable wood are collected at waste collection sites, most of it remains unused and is burned: it is too labor-intensive to sort, process and upcycle useable parts. Valuable wood thus becomes worthless waste, against circular economy principles. In MoBot-Wood, waste collection organizations HVC and the municipality of Amsterdam, together with Rolan Robotics, Metabolic and AUAS investigate how waste wood can be sorted and processed at waste collection sites, using an easy-to-deploy robotic solution. In various preceding and on-going projects, AUAS and partners are exploring circular wood intake, sorting and processing using industrial robots, including processes like machine vision, 3D scanning, sawing, and milling. These projects show that harvesting waste wood is a challenging matter. Generally, the wood is only partially useable due to the presence of metal, excessive paint, deterioration by fungi and water, or other contamination and damages. To harvest useable wood thus requires intensive sorting and processing. The solution of transporting all the waste wood from collection sites to a central processing station might be too expensive and have a negative environmental impact. Considering that much of collected wood will need to be discarded, often no wood is harvested at all, due to the costs for collection and shipping. Speaking with several partners in related projects, the idea emerged to develop a mobile robotic station, which can be (temporarily) deployed at waste collection sites, to intake, sort and process wood for upcycling. In MoBot-Wood, research entails the design of such station, its deployment conditions, and a general assessment of its potential impact. The project investigates robotic sorting and processing on location as a new approach to increase the amount of valuable, useable wood harvested at waste collection sites, by avoiding material transport and reducing the volume of remaining waste.
Automation is a key enabler for the required productivity improvement in the agrifood sector. After years of GPS-steering systems in tractors, mobile robots start to enter the market. Localization is one of the core functions for these robots to operate properly on fields and in orchards. GNSS (Global Navigation Satellite System) solutions like GPS provide cm-precision performance in open sky, but buildings, poles and biomaterial may reduce system performance. On top, certain areas do not provide a dependable grid communication link for the necessary GPS corrections and geopolitics lead to jamming activities. Other means for localization are required for robust operation. VSLAM (Visual Simultaneous Localization And Mapping) is a complex software approach that imitates the way we as humans learn to find our ways in unknown environments. VSLAM technology uses camera input to detect features in the environment, position itself in that 3D environment while concurrently creating a map that is stored and compared for future encounters, allowing the robot to recognize known environments and continue building a complete, consistent map of the environment covered by its movement. The technology also allows continuous updating of the map in environments that evolve over time, which is a specific advantage for agrifood use cases with growing crops and trees. The technology is however relatively new, as required computational power only recently became available in tolerable cost range and it is not well-explored for industrialized applications in fields and orchards. Orientate investigates the merits of open-source SLAM algorithms on fields - with Pixelfarming Robotics and RapAgra - and in an orchard - with Hillbird - preceded by simulations and initial application on a HAN test vehicle driving in different terrains. The project learnings will be captured in educational material elaborating on VSLAM technology and its application potential in agrifood.
De ontwikkeling in mobiele robotica gaat snel. Personenwagens bevatten steeds meer bestuurderassistentiesystemen. In de logistiek is grote behoefte aan voertuigautomatisering om het chauffeurstekort het hoofd te bieden en in de agrifood is grote robotiseringsslag gaande, enerzijds vanwege personeelstekort en anderzijds richting duurzaamheid: compacte, mobiele robots beperken bijvoorbeeld de bodemverdichting en zijn een enabler voor efficiënt biologisch telen. Mobiele robots zijn complex qua besturing. Denk aan padplanning, padvolgen, herkenning en ontwijken van obstakels en daarnaast het autonoom uitvoeren van specifieke taken op basis van een complexe set van sensoren en actuatoren. Ontwikkeling van robotsoftware gebeurt grotendeels in simulatie, omdat praktische tests complex, tijdrovend, kostbaar en soms onveilig kunnen zijn. In Nederland bestaan veel ontwikkelaars van mobiele robots. Veel van dit werk startte in een pioniersstadium, waarbij ontwikkelaars een eigen, simulatie-gebaseerde ontwikkelomgeving opzetten en prioritaire algoritmes ontwikkelden. Langzamerhand ontstaan standaarden waarmee softwarebouwblokken voor de besturing van mobiele robots eenvoudig gekoppeld en hergebruikt kunnen worden, zoals Robot Operating System (ROS). ROS biedt naast interoperabiliteit ook een groot ecosystemen met (open-source) softwarefuncties voor robots. ROS startte in de onderzoekswereld en wordt steeds breder geaccepteerd in de industrie en ingebed in onderwijs. Ondanks de krachtige ROS-interfacing blijft het complex en tijdrovend om een ROS-gebaseerde ontwikkelomgeving met allerlei tools, packages en drivers op te zetten. Daarnaast kunnen simulaties van allerlei scenario’s zeer tijdrovend zijn. RoboSIM werkt met partners aan een pilot van Asimovo, een schaalbare cloudgebaseerde methodiek die zowel de setuptijd als de simulatietijd van ROS-gebaseerde ontwikkeling drastisch verkort en tevens locatieonafhankelijke samenwerking ondersteunt. Deze oplossing zal als pilot worden ingezet in de RDW Self Driving Challenge: een goede praktische use case om de inzetbaarheid en operationele efficiëntie te testen en waar gewenst te verbeten. De resultaten helpen het bedrijfsleven, maar tevens de onderzoeks- en onderwijswereld om sneller te innoveren.
