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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
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Posted Content
12 Jan 2020
TL;DR: An integrated control system of self-driving autonomous vehicles based on the personal driving preference to provide personalized comfortable driving experience to autonomous vehicle users and a vehicle controller based on control parameters enabling integrated lateral and longitudinal control via preference-aware maneuvering of autonomous vehicles is proposed.
Abstract: This paper issues an integrated control system of self-driving autonomous vehicles based on the personal driving preference to provide personalized comfortable driving experience to autonomous vehicle users. We propose an Occupant's Preference Metric (OPM) which is defining a preferred lateral and longitudinal acceleration region with maximum allowable jerk for users. Moreover, we propose a vehicle controller based on control parameters enabling integrated lateral and longitudinal control via preference-aware maneuvering of autonomous vehicles. The proposed system not only provides the criteria for the occupant's driving preference, but also provides a personalized autonomous self-driving style like a human driver instead of a Robocar. The simulation and experimental results demonstrated that the proposed system can maneuver the self-driving vehicle like a human driver by tracking the specified criterion of admissible acceleration and jerk.

28 citations

Posted Content
TL;DR: By combining a Sequential Quadratic Programming (SQP) method with a homotopy approach that gradually transforms the problem from a relaxed one to the original one, practically relevant locally optimal solutions to the motion planning problem can be computed.
Abstract: This paper presents a systematic approach for computing local solutions to motion planning problems in non-convex environments using numerical optimal control techniques. It extends the range of use of state-of-the-art numerical optimal control tools to problem classes where these tools have previously not been applicable. Today these problems are typically solved using motion planners based on randomized or graph search. The general principle is to define a homotopy that perturbs, or preferably relaxes, the original problem to an easily solved problem. By combining a Sequential Quadratic Programming (SQP) method with a homotopy approach that gradually transforms the problem from a relaxed one to the original one, practically relevant locally optimal solutions to the motion planning problem can be computed. The approach is demonstrated in motion planning problems in challenging 2D and 3D environments, where the presented method significantly outperforms a state-of-the-art open-source optimizing sampled-based planner commonly used as benchmark.

26 citations

Journal ArticleDOI
01 Dec 2019
TL;DR: A fast high-precision BOMP algorithm for autonomous parking concerning dynamical feasibility and collision-free property is proposed and it is demonstrated that the computation speed increases almost two orders of magnitude compared with the area criterion based collision avoidance method.
Abstract: In this paper, we present a bilevel optimal motion planning (BOMP) model for autonomous parking. The BOMP model treats motion planning as an optimal control problem, in which the upper level is designed for vehicle nonlinear dynamics, and the lower level is for geometry collision-free constraints. The significant feature of the BOMP model is that the lower level is a linear programming problem that serves as a constraint for the upper-level problem. That is, an optimal control problem contains an embedded optimization problem as constraints. Traditional optimal control methods cannot solve the BOMP problem directly. Therefore, the modified approximate Karush–Kuhn–Tucker theory is applied to generate a general nonlinear optimal control problem. Then the pseudospectral optimal control method solves the converted problem. Particularly, the lower level is the $$J_2$$-function that acts as a distance function between convex polyhedron objects. Polyhedrons can approximate objects in higher precision than spheres or ellipsoids. As a result, a fast high-precision BOMP algorithm for autonomous parking concerning dynamical feasibility and collision-free property is proposed. Simulation results and experiment on Turtlebot3 validate the BOMP model, and demonstrate that the computation speed increases almost two orders of magnitude compared with the area criterion based collision avoidance method.

15 citations

Posted Content
TL;DR: This paper presents a method to efficiently generate large, free, and guaranteed convex space among arbitrarily cluttered obstacles, which significantly outperforms state-of-the-art works in efficiency and presents practical applications with the method in 3D.
Abstract: In this paper, we present a method to efficiently generate large, free, and guaranteed convex space among arbitrarily cluttered obstacles. Our method operates directly on point clouds, avoids expensive calculations, and processes thousands of points within a few milliseconds, which extremely suits embedded platforms. The base stone of our method is sphere flipping, a one-one invertible nonlinear transformation, which maps a set of unordered points to a nonlinear space. With these wrapped points, we obtain a collision-free star convex polytope. Then, utilizing the star convexity, we efficiently modify the polytope to convex and guarantee its free of obstacles. Extensive quantitative evaluations show that our method significantly outperforms state-of-the-art works in efficiency. We also present practical applications with our method in 3D, including large-scale deformable topological mapping and quadrotor optimal trajectory planning, to validate its capability and efficiency. The source code of our method will be released for the reference of the community.

14 citations

Proceedings ArticleDOI
03 Oct 2018
TL;DR: This work defines the comfort of a trajectory as forces, specifically, translational force, received to objects carried by a robot while following the trajectory by measuring impulse, and proposes a novel, kinodynamic comfort path planning method based on this definition.
Abstract: As personal autonomous mobility is getting to be more widely adopted, it is more important to consider comfortability of stuffs and persons carried by such mobility. In this work, we define the comfort of a trajectory as forces, specifically, translational force, received to objects carried by a robot while following the trajectory by measuring impulse. To maximize such a comfort, we propose a novel, kinodynamic comfort path planning method based on our definition of comfort. Our work is based on direct collocation method for handling our nonconvex objective function. We also introduce Bidirectional Obstacle Detection(BOD)that identifies the distances along the perpendicular directions to the trajectory. This is mainly designed for avoiding obstacles while minimizing forces causing discomfort. Our experimental results show that our method can compute trajectories whose comfort measures can be up to 18 times higher than those computed by prior related objectives, e.g., squared velocity used for generating smooth trajectory.

13 citations