B-spline-based trajectory optimization has been widely used in the field of robot navigation, as the convex hull property of the B-spline curve guarantees its dynamical feasibility with a small number of control variables. Several recent works demonstrated that a holonomic system like a drone, which has simple dynamical feasibility constraints, fully utilizes the B-spline property for trajectory optimization. Nevertheless, it is still challenging to leverage the B-splined-based optimization algorithm to generate a collision-free trajectory for autonomous vehicles because their complex vehicle kinodynamic constraints make it difficult to use the convex hull property. In this paper, we propose a novel incremental path flattening method with a new swept volume method that enables a B-splined-based trajectory optimization algorithm to incorporate vehicle kinematic collision avoidance constraints. Furthermore, a curvature constraint is added with other feasibility constraints (e.g. velocity and acceleration) for the vehicle kinodynamic constraints. Our experimental results demonstrate that our method outperforms state-of-the-art baselines in various simulated environments and verifies its valid tracking performance with an autonomous vehicle in a real-world scenario.

翻译：基于B样条的轨迹优化在机器人导航领域得到了广泛应用，因为B样条曲线的凸包性质能以少量控制变量保证其动力学可行性。近年研究表明，诸如无人机等具有简单动力学可行性约束的全向系统已充分利用B样条性质进行轨迹优化。然而，由于自动驾驶车辆复杂的运动学约束使得凸包性质难以直接应用，基于B样条的优化算法仍难以生成无碰撞轨迹。本文提出一种新颖的增量路径展平方法，结合新型扫掠体积方法，使基于B样条的轨迹优化算法能够融入车辆运动学避碰约束。此外，针对车辆运动学约束，我们在速度、加速度等可行性约束基础上增加了曲率约束。实验结果表明，在多种仿真环境中，本方法优于现有最优基线方法，并通过真实场景下的自动驾驶车辆验证了其有效的跟踪性能。