Swarm robotic systems hold the potential to revo-lutionise various fields by executing complex tasks collectively. Efficient navigation remains a pivotal challenge that can significantly impact the performance and applicability of swarm robotic systems. This study delves into exploring two distinct path projection strategies, namely the Fastest Time/First Path to the Goal and the Nearest Neighbour methods, to optimise the navigation of a swarm of Kilobots towards a designated goal. Through a series of experiments, each strategy's efficiency and time effectiveness are thoroughly analysed and compared. The Fastest Time/First Path to the Goal strategy endeavours to minimize the time taken by having subsequent Kilobots follow the trail of the quickest Kilobot to reach the goal. On the other hand, the Nearest Neighbour strategy, utilizing the Euclidean Path Cost Estimation technique, aims at projecting the path with the minimum overall cost for Kilobots to follow, promoting a cost-effective navigation approach. The findings reveal that the Nearest Neighbour strategy emerges as a more balanced and efficient approach, thereby presenting substantial promise for further research in swarm robotics navigation. The insights gathered from this study have implications for the application of swarm robotics in dynamic and varied environmental conditions.