Swarms of robots can quickly search large environments through parallelisation, are robust due to redundancy, and can simplify complex tasks like navigation compared to a single robot. Flying swarms can rapidly cover rough terrain and have elevated sensing, aiding tasks such as search or surveillance. However, flying robots have severe limitations in sensing, processing and energy, which raise three open challenges to realise indoor aerial swarms. The first is goal-directed autonomous flight, which normally requires absolute positioning from GPS or external tracking systems, unavailable in unknown indoor environments. On-board sensing approaches using illumination-dependent cameras or laser scanners that can fail in homogeneous environments require processing on ground stations using long-range communication due to high computational demands. Although optic-flow based approaches can be less computationally demanding, they require significant illumination and contrast, and need additional sensors for goal-directed flight. The second challenge is cooperative search and navigation, which typically requires either absolute positioning, localisation using a priori environment maps or the computationally expensive online creation of maps. Thirdly, collective systems frequently suffer inefficiencies due to interference and unnecessary locomotion, resulting in wasted energy. Prior research to improve coordination usually required maps, centralised processing or GPS and considered only ground robots, but flying robots have significantly different energy characteristics and demands. Alternative strategies are therefore required that do not rely on absolute positioning or localisation, environment maps, long-range communication or centralised processing, referred to as Global Information. This thesis introduces a holistic approach to achieve autonomous flight and navigation by embedding a network of robots in the environment with local sensing, processing and communication. A new methodology is presented for goal-directed autonomous flight using relative positioning in reference to nearby static robots. This allows flying robots to directly perceive their motion and fly across the network through the environment. Comprehensive validation tests demonstrated a quadrotor successfully exploring a confined indoor environment. Efficient navigation is achieved using communication messages propagated across the robot network to retrieve the shortest paths. Search is accomplished with a systematic strategy to dynamically redeploy the robot network to unexplored areas. Extensive realistic simulation analysis showed that swarms of 20–30 robots could search a variety of large indoor environments. To mitigate the short flight endurance, different swarm deployment strategies are systematically compared that reduce robot interference and decrease unnecessary locomotion by exploiting gained environment information. The strategies either control the dispatching rate, robot activation or exploit the network to recruit robots when and where necessary. These comparisons facilitate the selection of the best strategy according to the robot capabilities and application requirements. In summary, this thesis resolves three major challenges in aerial swarm robotics and contributes new methodologies to significantly progress the realisation of energy-efficient indoor search by swarms of flying robots. This research should enable many applications such as locating toxic gas leaks, crowd monitoring, and searching for suspicious objects or injured victims.