scispace - formally typeset
Search or ask a question

An Efficient Spatial-Temporal Trajectory Planner for Autonomous Vehicles in Unstructured Environments

TL;DR: In this article , the authors propose a real-time trajectory optimization method that can generate a high-quality whole-body trajectory under arbitrary environmental constraints by leveraging the differential flatness property of car-like robots.
Abstract: As a core part of autonomous driving systems, motion planning has received extensive attention from academia and industry. However, real-time trajectory planning capable of spatial-temporal joint optimization is challenged by nonholonomic dynamics, particularly in the presence of unstructured environments and dynamic obstacles. To bridge the gap, we propose a real-time trajectory optimization method that can generate a high-quality whole-body trajectory under arbitrary environmental constraints. By leveraging the differential flatness property of car-like robots, we simplify the trajectory representation and analytically formulate the planning problem while maintaining the feasibility of the nonholonomic dynamics. Moreover, we achieve efficient obstacle avoidance with a safe driving corridor for unmodelled obstacles and signed distance approximations for dynamic moving objects. We present comprehensive benchmarks with State-of-the-Art methods, demonstrating the significance of the proposed method in terms of efficiency and trajectory quality. Real-world experiments verify the practicality of our algorithm. We will release our codes for the research community
References
More filters
Journal ArticleDOI
Zhou Jinyun1, He Runxin1, Yu Wang1, Jiang Shu1, Zhenguang Zhu1, Hu Jiangtao1, Jinghao Miao1, Luo Qi1 
01 Apr 2021
TL;DR: This letter presents a free space trajectory optimization algorithm for autonomous driving, which decouples the collision-free trajectory generation problem into a Dual-Loop Iterative Anchoring Path Smoothing (DL-IAPS) problem and a Piecewise-Jerk Speed Optimization (JSO) problem.
Abstract: This letter presents a free space trajectory optimization algorithm for autonomous driving, which decouples the collision-free trajectory generation problem into a Dual-Loop Iterative Anchoring Path Smoothing (DL-IAPS) problem and a Piecewise-Jerk Speed Optimization (PJSO) problem. The work leads to remarkable driving performance improvements including more robust and precise collision avoidance, higher control feasibility, higher computation efficiency and stricter driving comfort guarantee, compared with other existing algorithms. The advantages of our algorithm are attributed to our fast iterative collision checks with exact vehicle/obstacle shapes, strict non-holonomic dynamic constraints and accurate kinematics-based speed optimization. It has been validated that, through batch simulation and road experiments, compared with prior works, our algorithm is with the highest robustness and capable to maintain the lowest failure rate ( $\sim\!\text{7}\%$ ) at nearly all test conditions, achieves 10x faster computational speed than other planners, fulfills $\text{100}\%$ driving-comfort standards in complex driving scenarios, and does not induce significant time increase as boundaries or obstacles scale up.

42 citations

Proceedings ArticleDOI
01 Jul 2002
TL;DR: In this paper, the authors define a canonical representation for convex polyhedra that is minimal and unique up to some elementary operations and can be computed in polynomial time.
Abstract: Every convex polyhedron in the Euclidean space $R^d$ admits both H-representation and V-representation. When working with convex polyhedra, in particular large-scale ones in high dimensions, it is useful to have a canonical representation that is minimal and unique up to some elementary operations. Such a representation allows one to compare two H-polyhedra or two V-polyhedra efficiently. In this paper, we define such representations that are simple and can be computed in polynomial time. The key ingredients are redundancy removal for linear inequality systems and affine transformations of polyhedra.

41 citations

Proceedings ArticleDOI
03 May 2010
TL;DR: A novel and simple to implement yet effective lattice design algorithm, which simultaneously produces input and state-space sampled lattice graphs, and shows that a transformation from chained form to path coordinates allows the resulting lattice to be bent along any C1 continuous path.
Abstract: In this paper we describe a novel and simple to implement yet effective lattice design algorithm, which simultaneously produces input and state-space sampled lattice graphs. The presented method is an extension to the ideas suggested by Bicchi et al. on input lattices and is applicable to systems which can be brought into (2,n) chained form, such as kinematic models of unicycles, bicycles, differential-drive robots and car-like vehicles (pulling several trailers). We further show that a transformation from chained form to path coordinates allows the resulting lattice to be bent along any C1 continuous path. We exploit this fact by shaping it along the skeleton of arbitrary structured environments, such as the center of road lanes and corridors. In our experiments in both structured (i.e. on-road) and unstructured (i.e. parking lot) scenarios, we successfully demonstrate for the first time the applicability of lattice-based planning approaches to search queries in arbitrary environments.

40 citations

Posted Content
TL;DR: The convex feasible set algorithm (CFS) is introduced which is a fast algorithm for non-convex optimization problems that have convex costs and non-Convex constraints and the application on motion planning for mobile robots is discussed.
Abstract: With the development of robotics, there are growing needs for real time motion planning. However, due to obstacles in the environment, the planning problem is highly non-convex, which makes it difficult to achieve real time computation using existing non-convex optimization algorithms. This paper introduces the convex feasible set algorithm (CFS) which is a fast algorithm for non-convex optimization problems that have convex costs and non-convex constraints. The idea is to find a convex feasible set for the original problem and iteratively solve a sequence of subproblems using the convex constraints. The feasibility and the convergence of the proposed algorithm are proved in the paper. The application of this method on motion planning for mobile robots is discussed. The simulations demonstrate the effectiveness of the proposed algorithm.

32 citations