This work proposes a biologically inspired collective behaviour for a team of co-operating robots. Collective behaviour is achieved by controlling the local interactions among a set of identical mobile robots, each robot performing a set of simple behaviours in order to realise group goals. A modification of the subsumption architecture is proposed for implementing control of individual robots. This architecture is adopted because it is computationally inexpensive and potentially suitable for low-level reactive and reflexive behaviours. In this scenario, the individual behaviours of the robots have different aims, which may cause conflict. To address this issue, a fuzzy logic-based approach for multiple behaviour coordination within each robot is proposed. The work also focuses on the development of intelligent multi-agent robot teams capable of acting autonomously and of collaborating in a dynamic environment to achieve team objectives. A knowledge-based software architecture is proposed that enables these robots to select co-operative behaviours and to adapt their performance during the specified time of the mission. These abilities are important because of uncertainties in the environmental conditions and because of possible functional failures in some team members. Improvement in team performance is achieved by updating the control of the robots based on knowledge acquired on-line. This architecture is implemented in a simulated team of mobile robots performing a proof-of-concept collaborative task. The results show a significant improvement in overall group performance and the robot team is able to achieve adaptive cooperative control despite dynamic changes in the environment and variation in the capabilities of the team members. Finally, a task involving real mobile robots is undertaken to demonstrate a practical, though simplified, implementation of the proposed collective behaviour.