Quadrotor motion planning is critical for autonomous flight in complex environments, such as rescue operations. Traditional methods often employ trajectory generation optimization and passive time allocation strategies, which can limit the exploitation of the quadrotor's dynamic capabilities and introduce delays and inaccuracies. To address these challenges, we propose a novel motion planning framework that integrates visibility path searching and reinforcement learning (RL) motion generation. Our method constructs collision-free paths using heuristic search and visibility graphs, which are then refined by an RL policy to generate low-level motion commands. We validate our approach in simulated indoor environments, demonstrating better performance than traditional methods in terms of time span.

翻译：四旋翼运动规划对于复杂环境（如救援任务）中的自主飞行至关重要。传统方法通常采用轨迹生成优化与被动时间分配策略，这可能限制对四旋翼动态性能的充分利用，并引入延迟与误差。为应对这些挑战，本文提出一种融合可见性路径搜索与强化学习运动生成的新型运动规划框架。该方法通过启发式搜索与可见性图构建无碰撞路径，继而通过强化学习策略进行精细化处理以生成底层运动指令。我们在模拟室内环境中验证了所提方法，结果表明其在时间跨度方面优于传统方法。