Decentralized multi-robot systems are increasingly valued for their robustness, scalability, and adaptability in dynamic and uncertain environments. Inspired by the collective behavior of social insects—particularly ants—these systems achieve coordinated group behavior through simple, local interactions and sensor-based decision-making, without reliance on centralized control. This chapter presents the development and implementation of decentralized algorithms that use camera and sensor-driven responses to enable intelligent collaboration among low-computation, differential drive robots. Four bio-inspired algorithms are presented: Reactive Ant-Inspired Navigation and Obstacle Avoidance, Attraction to Object of Interest, Leader-Follower Coordination, and Autonomous Gripping. Each algorithm reflects a specific behavioral trait observed in ant colonies—such as obstacle negotiation, object attraction, trail following, and cooperative transport—and is designed for robots with limited processing capability. The robots utilize infrared proximity sensors, line sensors, IMUs, and Pixy cameras to make autonomous decisions based solely on their immediate environment. The algorithms are first validated in simulation using MATLAB and Simulink and then implemented and tested on a customized Zumo 32U4 OLED differential drive robot platform. Each algorithm was experimentally evaluated, with its performance, limitations, and potential improvements discussed. By applying biologically inspired control strategies to simple, sensor-guided robots, this chapter provides a practical and accessible framework for building cooperative robotic systems. The methods presented are particularly suitable for applications in logistics, exploration, and automated manufacturing, where simplicity, adaptability, and decentralized control are critical.