发表机构
TJU-Aerial-Robotics（TJU空中机器人实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对端到端无人机规划器YOPO的缺陷，通过采用MINCO参数化、扩展多模态预测、添加动态约束及替换损失函数等改进，提升了轨迹质量与避障安全性。
AI 中文摘要
单阶段规划器YOPO可将单张深度图像与机器人状态直接映射为一组候选轨迹，通过可微轨迹代价的反向传播进行训练，能提供密集且包含几何信息的监督信号，但也继承了软约束优化的缺陷：安全代价与平滑性、目标到达项存在竞争，在同伦类间非凸，且单片多项式的表达能力有限。本文总结了几项有效改进：采用双片MINCO参数化，在不改变轨迹空间轮廓的前提下用时间换平滑性；进一步将YOPO的多模态预测扩展至不同同伦类，将每个运动原语视为同伦锚点，将轨迹限制在可行区域内，无需显式构建安全飞行走廊或前端搜索；为保证动态可行性，对速度和加速度施加障碍惩罚，同时采用依赖曲率的速度限制，其梯度仅作用于速度，产生自适应速度行为，在杂乱区域或急转弯处减速；用排序损失替代得分回归，防止小得分误差打乱候选集顺序。这些改进可得到更丰富的轨迹表示、更安全的避障效果及更直接的飞行路径。
英文摘要
The one-stage planner YOPO maps a single depth image and the robot state directly to a set of candidate trajectories, trained by backpropagating through differentiable trajectory costs. This yields dense, geometrically informative supervision, but inherits the pathologies of soft-constrained optimization: the safety cost competes with the smoothness and goal-reaching terms, is non-convex across homotopy classes, and the single-piece polynomial is limited in expressiveness. In this report, we summarize several effective modifications. We adopt a two-piece MINCO parameterization, trading time for smoothness without altering the trajectory's spatial profile. We further lift YOPO's multi-modal prediction to span distinct homotopy classes, treating each motion primitive as a homotopy anchor that confines the trajectory to a feasible basin - without explicit safe-flight-corridor construction or front-end search. For dynamic feasibility, we impose barrier penalties on velocity and acceleration together with a curvature-dependent speed limit whose gradient acts only on the velocity, producing an adaptive-speed behavior that decelerates in cluttered regions or sharp turns. We replace score regression with a ranking loss, preventing small score errors from reordering the candidate set. These yield richer trajectory representations, safer obstacle avoidance, and more direct flight paths.
