Over the last years advantages in autonomous agent systems and technology have created many potential applications for large numbers of collaborating robots in the field of surveillance, mapping, mining, farming and (space) exploration. The underlying principle that enables robots to collectively solve complex tasks is that of minimal interference: the basis of swarm robotics. In nature, swarms of insects use stigmergy, communication through environment marking, to connect individual and collective behavior. Many have tried to implement this stigmergic principle in swarm robotics, though it remains a challenge to implement stigmergy in robotic systems suited for real applications. In this thesis we present a biologically inspired minimalistic design for a foraging robotic swarm based on stigmergy. Our self-guiding swarm requires only very restricted robot capabilities: Robots do not require global or relative position measurements; the swarm is fully decentralized; and the robots need no infrastructure in place. Additionally, the system only requires one-hop communication over the robot network, we do not make any assumptions about the connectivity of the communication graph and the transmission of information and computation is scalable versus the number of agents. All this is realized by letting the agents in the swarm act as foragers or as guiding (beacons). We analyse the characteristics and performance of our swarm by our own developed so called 'particle'-simulator and the realistic \textit{Webots} simulator using a swarm of Elisa-3 robots. We show how the swarm self-organizes to solve a foraging problem over an unknown environment and converges to trajectories around the shortest path. In addition, we study directions of future improvements of the swarm design regarding the minimization of resources, optimality of the created paths and the convergence speed. At last, we derive formal results regarding the reachability, coverage time and hitting times of the swarm.