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In this paper we introduce a novel method for obtaining good quality paths for autonomous road vehicles (e.g., cars or buses) in narrow environments. There are many traffic situations in urban scenarios where nontrivial maneuvering in narrow places is necessary. Navigating in cluttered parking lots or having to avoid obstacles blocking the way and finding a detour even in narrow streets are challenging, especially if the vehicle has large dimensions like a bus. We present a combined approximation-based approach to solve the path planning problem in such situations. Our approach consists of a global planner which generates a preliminary path consisting of straight and turning-in-place primitives and a local planner which is used to make the preliminary path feasible to car-like vehicles. The approximation methodology is well known in the literature; however, both components proposed in this paper differ from existing similar planning methods. The approximation process with the proposed local planner is proven to be convergent for any preliminary global paths. The resulting path has continuous curvature which renders our method well suited for application on real vehicles. Simulation experiments show that the proposed method outperforms similar approaches in terms of path quality in complicated planning tasks.

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A well-known reactive motion planning technique, the dynamic window approach (DWA) provides an elegant way to navigate safely in the presence of obstacles, also taking the dynamic properties of the robot into account. Most of the DWA-based methods have the same limitation, namely they use an objective function consisting of weighted terms. Different situations require different weights, however, there is no algorithm for choosing them. This paper presents a global dynamic window-based navigation scheme using model predictive control and having no weighted objective function. Former DWA-based methods take dynamic limitations of the robot by acceleration constraints into account. In contrast with that, the proposed approach utilizes a dynamic motion model of the robot.

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