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A Non-Optimization-Based Dynamic Path Planning for Autonomous Obstacle Avoidance
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AI
DOI：10.1109/TCST.2022.3196880.png)
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Abstract
En 中文
This article presents a non-optimization-based framework for path planning and tracking for evasive maneuvers in autonomous cars. The framework exploits a two-layer approach where a path planner generates a reference trajectory that is then tracked by a path-tracking controller. A nested curvature preview controller (CPC) implements path tracking. In this article, we show how to describe the closed-loop performance of the controller. The quantification of the closed-loop performance in the frequency domain guides the generation of the evasive path. In this way, the algorithm generates a path that avoids the obstacle (if possible) accounting for both static and dynamic constraints. The proposed framework, thus, provides a non-optimization-based way to integrate the characteristics of the path tracker in the path-planner algorithm, thus avoiding the need to define cost functions and use the third-party optimizers. This article validates the proposed evasive maneuver strategy in simulation and on an instrumented vehicle. First, we test the trajectory tracker, showing that it tracks aggressive trajectories (with a lateral acceleration close to 1 g) with an error smaller than 30 cm. Subsequently, we integrate the curvature preview with the path generator and show the joint generation-tracking performance in two different scenarios.
Keywords:
Vehicle dynamics
Trajectory
Autonomous vehicles
Bandwidth
Tires
Mathematical models
Collision avoidance
Automotive control
autonomous driving
autonomous vehicles
path planning
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