Urban Swarms: A new approach for autonomous waste management

Modern cities are growing ecosystems that face new challenges due to the increasing population demands. One of the many problems they face nowadays is waste management, which has become a pressing issue requiring new solutions. Swarm robotics systems have been attracting an increasing amount of attention in the past years and they are expected to become one of the main driving factors for innovation in the field of robotics. The research presented in this paper explores the feasibility of a swarm robotics system in an urban environment. By using bio-inspired foraging methods such as multi-place foraging and stigmergy-based navigation, a swarm of robots is able to improve the efficiency and autonomy of the urban waste management system in a realistic scenario. To achieve this, a diverse set of simulation experiments was conducted using real-world GIS data and implementing different garbage collection scenarios driven by robot swarms. Results presented in this research show that the proposed system outperforms current approaches. Moreover, results not only show the efficiency of our solution, but also give insights about how to design and customize these systems.Comment: Manuscript accepted for publication in IEEE ICRA 201

Implicit Cooperative Positioning in Vehicular Networks

Absolute positioning of vehicles is based on Global Navigation Satellite Systems (GNSS) combined with on-board sensors and high-resolution maps. In Cooperative Intelligent Transportation Systems (C-ITS), the positioning performance can be augmented by means of vehicular networks that enable vehicles to share location-related information. This paper presents an Implicit Cooperative Positioning (ICP) algorithm that exploits the Vehicle-to-Vehicle (V2V) connectivity in an innovative manner, avoiding the use of explicit V2V measurements such as ranging. In the ICP approach, vehicles jointly localize non-cooperative physical features (such as people, traffic lights or inactive cars) in the surrounding areas, and use them as common noisy reference points to refine their location estimates. Information on sensed features are fused through V2V links by a consensus procedure, nested within a message passing algorithm, to enhance the vehicle localization accuracy. As positioning does not rely on explicit ranging information between vehicles, the proposed ICP method is amenable to implementation with off-the-shelf vehicular communication hardware. The localization algorithm is validated in different traffic scenarios, including a crossroad area with heterogeneous conditions in terms of feature density and V2V connectivity, as well as a real urban area by using Simulation of Urban MObility (SUMO) for traffic data generation. Performance results show that the proposed ICP method can significantly improve the vehicle location accuracy compared to the stand-alone GNSS, especially in harsh environments, such as in urban canyons, where the GNSS signal is highly degraded or denied.Comment: 15 pages, 10 figures, in review, 201

Envisioning Digital Europe 2030: Scenarios for ICT in Future Governance and Policy Modelling

The report Envisioning Digital Europe 2030 is the result of research conducted by the Information Society Unit of IPTS as part of the CROSSROAD Project - A Participative Roadmap on ICT research on Electronic Governance and Policy Modelling (www.crossroad-eu.net ). After outlining the purpose and scope of the report and the methodological approach followed, the report presents the results of a systematic analysis of societal, policy and research trends in the governance and policy modelling domain in Europe. These analyses are considered central for understanding and roadmapping future research on ICT for governance and policy modelling. The study further illustrates the scenario design framework, analysing current and future challenges in ICT for governance and policy modelling, and identifying the key impact dimensions to be considered. It then presents the scenarios developed at the horizon 2030, including the illustrative storyboards representative of each scenario and the prospective opportunities and risks identified for each of them. The scenarios developed are internally consistent views of what the European governance and policy making system could have become by 2030 and of what the resulting implications for citizens, business and public services would be. Finally, the report draws conclusions and presents the proposed shared vision for Digital Europe 2030, offering also a summary of the main elements to be considered as an input for the future development of the research roadmap on ICT for governance and policy modelling.JRC.DDG.J.4-Information Societ

The shape of things to come: visions for the future of Aboriginal and Torres Strait Islander health research

This paper presents the results of a project that considered how research might best contribute to Aboriginal and Torres Strait Islander health and wellbeing in the year 2030. Executive summary In late 2012, the Lowitja Institute embarked on a project using ‘futures thinking’ to consider how research might best contribute to Aboriginal and Torres Strait Islander health and wellbeing in the year 2030. The project was motivated by a desire to ‘get ahead of the game’: to anticipate and prepare for the potential research demands of the future. In particular, there was a desire to ‘close the gap’ between the point at which important research needs are identified by policy makers or service providers, and when research findings can be delivered. To think about the research needs of the future, it was necessary to first imagine what life might be like in 2030. What might Australia be like then, and the world? And what might the lives of Aboriginal and Torres Strait Islander people be? Workshops were held around the country to consider issues and trends visible in the current landscape, and how these might play out to influence life in 2030. A range of possible scenarios emerged, clustering around two divergent futures: an inclusive, vibrant Australia in which Aboriginal and Torres Strait Islander cultures are valued and embraced as central to the Australian identity; or an Australia in which economic and/or spiritual poverty drive a rejection of diversity and increase the divide between rich and poor. Participants then grappled with the question: If this (or that) scenario occurs, then what will be needed from research? By thinking about the range of possible scenarios for life in 2030, what capabilities are required to deliver the research that will be needed to address emerging issues and create a healthy future? The inspirational and empowering answer—perhaps not surprisingly—was not simply a list of research topics. Instead, participants articulated a strong and widely shared desire for a profoundly different system of research. A vision emerged of a research system in which research and practice are closely interwoven and which would enable greater integration of health services, policy and research. Such a system would be responsive to changing research demands, but also to changing social, economic, technological and knowledge landscapes. Specific research capabilities were also identified. An urgent necessity to actively address the social determinants of health was articulated in every workshop, along with a growing sense that the health and health research sectors may need to play a facilitating role, inviting other sectors—such as education, justice, local government—to collaborate and maximise the impact of their collective efforts to bring about change. A need for more evidence and evaluation around early childhood development programs (social as well as physical development) was seen as a priority for the immediate future. The Aboriginal and Torres Strait Islander health and health research sectors have played a pioneering role in the reform of research in Australia. The strength of vision articulated by participants in this project, and the desire to see that vision become a reality, suggests the sector will succeed in its drive toward a vision of a more effective research system

A multiagent urban traffic simulation Part I: dealing with the ordinary

We describe in this article a multiagent urban traffic simulation, as we believe individual-based modeling is necessary to encompass the complex influence the actions of an individual vehicle can have on the overall flow of vehicles. We first describe how we build a graph description of the network from purely geometric data, ESRI shapefiles. We then explain how we include traffic related data to this graph. We go on after that with the model of the vehicle agents: origin and destination, driving behavior, multiple lanes, crossroads, and interactions with the other vehicles in day-to-day, ?ordinary? traffic. We conclude with the presentation of the resulting simulation of this model on the Rouen agglomeration

LanguageMPC: Large Language Models as Decision Makers for Autonomous Driving

Existing learning-based autonomous driving (AD) systems face challenges in comprehending high-level information, generalizing to rare events, and providing interpretability. To address these problems, this work employs Large Language Models (LLMs) as a decision-making component for complex AD scenarios that require human commonsense understanding. We devise cognitive pathways to enable comprehensive reasoning with LLMs, and develop algorithms for translating LLM decisions into actionable driving commands. Through this approach, LLM decisions are seamlessly integrated with low-level controllers by guided parameter matrix adaptation. Extensive experiments demonstrate that our proposed method not only consistently surpasses baseline approaches in single-vehicle tasks, but also helps handle complex driving behaviors even multi-vehicle coordination, thanks to the commonsense reasoning capabilities of LLMs. This paper presents an initial step toward leveraging LLMs as effective decision-makers for intricate AD scenarios in terms of safety, efficiency, generalizability, and interoperability. We aspire for it to serve as inspiration for future research in this field. Project page: https://sites.google.com/view/llm-mp

3D multi-robot patrolling with a two-level coordination strategy

Teams of UGVs patrolling harsh and complex 3D environments can experience interference and spatial conflicts with one another. Neglecting the occurrence of these events crucially hinders both soundness and reliability of a patrolling process. This work presents a distributed multi-robot patrolling technique, which uses a two-level coordination strategy to minimize and explicitly manage the occurrence of conflicts and interference. The first level guides the agents to single out exclusive target nodes on a topological map. This target selection relies on a shared idleness representation and a coordination mechanism preventing topological conflicts. The second level hosts coordination strategies based on a metric representation of space and is supported by a 3D SLAM system. Here, each robot path planner negotiates spatial conflicts by applying a multi-robot traversability function. Continuous interactions between these two levels ensure coordination and conflicts resolution. Both simulations and real-world experiments are presented to validate the performances of the proposed patrolling strategy in 3D environments. Results show this is a promising solution for managing spatial conflicts and preventing deadlocks