One of the most difficult parts of motion planning in configuration space is ensuring a trajectory does not collide with task-space obstacles in the environment. Generating regions that are convex and collision free in configuration space can separate the computational burden of collision checking from motion planning. To that end, we propose an extension to IRIS (Iterative Regional Inflation by Semidefinite programming) [5] that allows it to operate in configuration space. Our algorithm, IRIS-NP (Iterative Regional Inflation by Semidefinite & Nonlinear Programming), uses nonlinear optimization to add the separating hyperplanes, enabling support for more general nonlinear constraints. Developed in parallel to Amice et al. [1], IRIS-NP trades rigorous certification that regions are collision free for probabilistic certification and the benefit of faster region generation in the configuration-space coordinates. IRIS-NP also provides a solid initialization to C-IRIS to reduce the number of iterations required for certification. We demonstrate that IRIS-NP can scale to a dual-arm manipulator and can handle additional nonlinear constraints using the same machinery. Finally, we show ablations of elements of our implementation to demonstrate their importance.

翻译：运动规划在配置空间中最困难的环节之一，是确保轨迹不与环境中的任务空间障碍物发生碰撞。在配置空间中生成凸且无碰撞的区域，可将碰撞检测的计算负担从运动规划中分离出来。为此，我们提出了IRIS（基于半定规划的迭代区域膨胀算法）[5]的扩展版本，使其能够在配置空间中运行。我们的算法IRIS-NP（基于半定规划与非线性规划的迭代区域膨胀算法）利用非线性优化来添加分离超平面，从而支持更一般的非线性约束。与Amice等人的研究[1]并行开展，IRIS-NP将严格保证区域无碰撞的要求转化为概率性保证，并获得了在配置空间坐标下更快生成区域的优势。此外，IRIS-NP还为C-IRIS提供了可靠的初始化方法，以减少达到严格证明所需的迭代次数。我们证明了IRIS-NP可扩展至双机械臂场景，并能够利用同一机制处理额外的非线性约束。最后，我们通过消融实验展示了算法各组成部分的重要性。