Autonomous Swarm Drones Exploration using Hybrid Frontier-Tracing and Ray-Casting Frontier Detection

Abstract

Abstract Frontier-based exploration has become a fundamental paradigm in robotic navigation, where frontiers represent the dynamic boundary between known and unknown regions. The effectiveness of this approach depends heavily on the speed and accuracy of frontier detection, as faster detection not only accelerates the overall exploration process but also enhances decision-making quality through more up-to-date environmental information. In this paper, we propose a hybrid frontier detection method for autonomous swarm drone exploration that integrates the Frontier-Tracing Frontier Detection (FTFD) algorithm with a ray-casting strategy. The FTFD component exploits previously identified frontiers and scan endpoints to efficiently update new exploration targets, while ray casting provides accurate detection of boundaries in the drone’s immediate surroundings. This integration is particularly suited for drones equipped with four directional sensors, thereby enabling full 360° situational awareness. Simulation results within a grid-based environment demonstrate the effectiveness of the proposed method in achieving efficient and scalable swarm exploration, validating its potential for real-world multi-agent navigation tasks.

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