When swarm robots move in an environment with obstacles to search for unknown targets, the challenge is that each individual must be able to maintain swarm cohesion, avoid obstacles, and achieve the widest possible coverage of the search space. Therefore, this paper proposes a solution that combines fuzzy logic, chaos theory, and the Dragonfly Algorithm (DA) to address this problem. Fuzzy logic is applied to determine the steering angle of robots in the swarm, enabling collision avoidance and regulating robot distribution in the environment. Chaos theory, using the logistic map, is integrated to allow robots to avoid obstacles smoothly, increase the diversity of motion trajectories, and escape local traps. At the same time, the DA is employed to optimize fuzzy rules, enhancing adaptability and coverage capability of the swarm. Simulation results in Matlab show that with the integrated Fuzzy–Chaotic–DA controller, the coverage rate exceeds 80% and collisions are significantly reduced compared with control based only on fuzzy logic, thereby confirming the effectiveness and stability of the proposed controller.