W e are currently witnessing the fourth industrial revolution 1 . Technological innovations have altered the way in which economies operate and how people interact with built, social and natural environments. One area of transformation is the emergence of robotics and autonomous systems (RAS), defined as technologies that can sense, analyse, interact with and manipulate their physical environment 2 . RAS include unmanned aerial vehicles (drones), self-driving cars, robots able to repair infrastructure, and wireless sensor networks used for monitoring. RAS therefore have a large range of potential applications, such as autonomous transport, waste collection, infrastructure maintenance and repair, policing 2,3 and precision agriculture 4 (Fig. 1). RAS have already revolutionized how environmental data are collected 5 and how species populations are monitored for conservation 6 and/or control 7 . Globally, the RAS market is projected to grow from $6.2 billion in 2018 to $17.7 billion in 2026 8 .Concurrent with this technological revolution, urbanization continues at an unprecedented rate. By 2030, an additional 1.2 million km 2 of the planet's surface will be covered by towns and cities, with ~90% of this development happening in Africa and

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In the quest to achieve the ambitious climate and clean energy targets the broad implementation of Integrated Photovoltaics (BIPV) is one of the keys. Photovoltaic (PV) modules can be installed above or on current roofing or traditional wall structures. In addition, BIPV devices substitute the skin of the exterior construction frame, i.e. the weather screen, thus simultaneously acting as both a climate screen and an energy producing source. However, while the integral planning strategy to building projects promotes the effective execution of BIPV, the limitation lies in the absence of both instruments and easy-to-use planning aid guidelines, particularly by non-PV experts in the early design stage. This study presents computational methods that help to quickly analyze the BIPV potential for a given building project and to suggest the optimal economical amount and location of the panels based on the building's energy demand profile.