Visual Modeling System for Optimization-Based Real-Time Trajectory Planning for Autonomous Aerial Drones

Engineering notes

Key topics: autonomous driving, trajectory planning, planning, control. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

In this paper, we present a visual modeling system to enable users to seamlessly describe the constraints of trajectory planning problems for autonomous aerial drones. The proposed modeling system comes with an intuitive GUI-based interface that enables the user to specify trajectory objectives, add and remove motion constraints, and update the constraint parameters in real-time. The interface algorithm acts as a high-level parser to convert graphically specified constraints into a standard form of the underlying optimal control problem. Then a sequence of convex optimization problems, convex subproblems, are generated whose solutions will converge to a solution of the trajectory planning problem. This convex optimization based method is referred to as successive convexification (SCvx) [1]. Beneath the interface, there is another low-level layer of problem parsing, which aims to model each convex subproblem as a Second Order Cone Programming (SOCP) problem in a standard form. Once each SOCP is formulated in this standard form, it can be passed to our in-house developed primal-dual interior point method (IPM) SOCP solver [2], [3] to obtain a solution for each convex subproblem within SCvx. This paper is aimed to describe the functional architecture of the visual modeling system and its core algorithms, and also presents some illustrative flight experiments.

Links and sources

• Official / arXiv page

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