This project change request is being raised to agree a change to the delivery dates for milestones 2 and 3. The original milestone dates as agreed in the contract were: Milestone 1 - Stage 2 core system development - 01/05/21 Milestone 2 - Stage 3 field testing and validation - 01/07/21 Milestone 3 - Stage 4 Final project, exploitation plan, project close out meeting and demonstration 1/08/21 The following revised delivery dates are requested: Milestone 1 - complete no change Milestone 2 - not fully completed revised date 01/09/21 Milestone 3 - revised date 31/10/21

Connected and autonomous vehicles will play a significant role in a future transport system and unlock enormous social benefits at the same time. FLOURISH looks to enable the delivery of many of these benefits by helping to ensure that connected and autonomous vehicle are developed with the user in mind and are technically secure, trustworthy and private. Using older people and others with assisted living needs as an exemplar to develop an understanding of the diverse needs of a particular user group, FLOURISH will develop innovative products, processes and services that are directly transferrable to the wider community. FLOURISH will expand existing physical and virtual vehicle test capability and help deliver up to 10,000 jobs through the establishment of the Bristol City-Region as a world class independent test facility for connected and autonomous vehicles.

A robot mounted on a mobile modular system that can be deployed to identify, sort and segregate radioactive waste for safe recycling or disposal. The robot will confront a mass of radioactive waste ranging from metals, to plastics, electrical equipment, soil and more. In order to process it, the robot will first identify an individual waste item using its vision system. The robot will recognise some waste, but otherwise it can be trained by an operative to identify a new waste type by sight. Through machine learning, the more the vision system is used, the more autonomous the process becomes. The system will also measure each item's weight, size, shape, surface area and composition for efficient sorting and packing. Visual recognition of waste is combined with radiometric and chemical characterisation to classify the waste for sorting. After visual identification, each item is picked up by the robotic arm and its level of radioactivity is monitored and it is chemically analysed. The information on the item's physical characteristics, material type and radioactivity level is used to sort the item into the correct waste-stream for safe recycling or storage. Records of this information, together with images of the items will be stored as a record of what items have been placed into each waste-stream. This project innovates on current state-of-the-art by removing the person from the process. This means that there is less risk to the operators from working in hazardous environments. There is less risk of human error in this repetitive task. The process will also be quicker and cheaper than a manual system, offering savings to the UK taxpayer on the cost of decommissioning redundant nuclear equipment and facilities. Combining a robot arm with vision systems, machine learning, nuclear and chemical characterisation systems will mark a new development for nuclear decommissioning. The robot arm can be of any model and size to suit the waste type. A further key innovation comes via the intelligent vision system, which automates the recognition of different forms of waste through machine learning. Human interaction is minimised, creating an efficient, waste-minimising workflow that can adapt to location, segregate waste by various measurable criteria, and will improve the more it is deployed. Waste generated by nuclear decommissioning is therefore dealt with safely, quickly and cheaply, with minimal human interaction, efficiently packing waste containers, and with a diligent recycling process.

The world faces two pressing challenges. Congestion in cities, the lifeblood of national economies, is rising to unacceptable levels. This causes poor health outcomes and strangles economic growth. At the same time, humanity must confront the threat of climate change and reduce its dependence on fossil fuels. The Advanced Mobility Ecosystem Consortium (AMEC) is aiming to demonstrate the commercial and operational viability of Advanced Aerial Mobility (AAM) in the UK. This is an efficient, electric mode of aerial transport complementary to existing transport infrastructure, helping to deliver both increased connectivity and net zero emission targets. In doing so we will deliver cost-effective and convenient inter-regional and intra-regional travel to the British public. AMEC will demonstrate three first-of-kind air mobility services using Vertical Aerospace's emission-free VA-X4 eVTOL aircraft, operated by Virgin Atlantic. The first mission will take place between Bristol Airport and South-West node. The second will take place between Heathrow Airport and Skyports' Elstree vertiport. A third will digitally simulate a mission between Bristol Airport and London City Airport. These missions will explore and prove all aspects of the passenger journey, vehicle operation, airspace navigation, ground charging, security provision and local stakeholder management. Various technologies and methods are being proven. Vertical Aerospace is exploring novel means of compliance with civil aviation regulators as it prepares an airworthy vehicle for demonstration. Skyports is building a "living lab" vertiport at Elstree Airport to allow UK AAM stakeholders to trial technologies and operational concepts, facilitating commercial operations. Atkins and Skyports are deploying innovative digital infrastructure to modernise airspace and ensure compliance with national aviation safety regulations and border security. The consortium also involves the cooperation of world-leading public and academic institutions that are bringing their expertise to enable an economically viable AAM ecosystem. Cranfield are undertaking vertiport network and scheduling optimisation. Warwick Manufacturing Group (WMG) are developing open hardware and software standards for rapid eVTOL charging solutions that are essential to achieve fast turnarounds and high aircraft utilisation. Connected Places Catapult will manage delivery of the project and address, from a neutral perspective, the many public acceptance challenges surrounding the introduction of AAM services. The potential benefits to the UK are vast. Greater convenience for the travelling British public, substantial export earnings from the domestic manufacture of aircraft with associated products and services, enhanced connectivity driving GDP multiplier effects and levelling-up opportunities, and fewer harmful emissions.

The Atkins team presents a highly automated Sort & Segregate system using a robotic arm mounted on a modular system that can be deployed to identify, sort and segregate radioactive waste for safe recycling or disposal. The robot will confront an unorganised mass of radioactive waste comprising a diverse range of objects. To process this waste, the robot will first identify an individual waste item using its vision system. At first the robot may recognise some waste but will require training by an operative to identify new waste types by sight. The more its vision system is used, the more autonomous the process becomes through machine learning. The system will also measure each item's weight, volume, surface area and composition for efficient sorting and packing. Once identified, waste is then radiologically and chemically characterised and sorted. Each item is picked up by the robotic arm, its level of radioactivity is monitored, and it is chemically analysed. The information on the item's physical characteristics, material type and radioactivity level is used to sort the item into the correct waste stream for safe recycling or storage, aided by efficient packing ensured by the vision system's algorithms. Records will be kept for each waste item and for each waste container produced. These records maintain traceability of the hazardous waste. This project innovates on current state-of-the-art by removing the person from the process. This means that there is less risk to operators from working in hazardous environments, and less risk of human error in such a repetitive task. The process will be safer, quicker and cheaper than a manual system, offering savings to the UK taxpayer on the cost of decommissioning redundant nuclear equipment and facilities. Combining a robotic arm with machine learning, vision systems, and nuclear and chemical characterisation will mark a new advancement in nuclear decommissioning. The robot arm can be of any model and size to suit the waste type. The intelligent vision system is a key innovation, as it automates the recognition of different forms of waste through machine learning. Operator involvement is minimised, creating an efficient, low-waste workflow that can adapt to location, segregate waste by various measurable criteria, and will improve the more it is deployed. Waste generated by nuclear decommissioning is therefore dealt with safely, quickly and cheaply, with minimal human interaction, efficiently packing waste containers, and with a diligent recycling process.