Autonomous robots are expected to increase drastically across many sectors over the coming years. It is commonplace in deployments for robots to traverse large spaces. This is often approached through mapping and explicit route planning. Under these approaches, problems such as scalability and deadlocks can cause serious issues. Alongside this, for an MRPP system to consider all of the vehicle dynamics of many robots moving in close proximity, it requires a large amount of processing. To better facilitate robots navigating along in shared spaces and in close proximity, platooning offers suitable approaches. In platooning approaches, robots are grouped together and planning is completed by the collective. In this approach, we place safety as the primary focus. We address safety guarantees through controlled information disclosure and construct a framework around this concept to facilitate safe, robust and efficient platooning. In this, we break down the approach to platooning into four systems which each focus on removing the potential for safety risks by limiting the information each subsequent robot is allowed. This approach is tested in practical experiments with multiple robots in an indoor environment. The experiments focus on the validation of the safety systems whilst unit-testing is used to validate the scalability of the approach and its robust nature. The results showed effective platooning despite dynamic navigation problems attributed to occasional localisation drift.