Abstract
Path planning is often considered as an important task in autonomous driving applications. Current planning method only concerns the knowledge of robot kinematics, however, in GPS denied environments, the robot odometry sensor often causes accumulated error. To address this problem, an improved path planning algorithm is proposed based on reinforcement learning method, which also calculates the characteristics of the cumulated error during the planning procedure. The cumulative error path is calculated by the map with convex target processing, while modifying the algorithm reward and punishment parameters based on the error estimation strategy. To verify the proposed approach, simulation experiments exhibited that the algorithm effectively avoid the error drift in path planning.
Highlights
Path planning is often utilized to design the optimal trajectory from the start point to the destination
Path planning methods consist of heuristic searching, sampling planning, and model-dependent methods [6]
Since the localization drift based on the noisy measurement is unbounded, it is challenging to estimate the cumulative error; the error could be presented by its statistical characteristics
Summary
Path planning is often utilized to design the optimal trajectory from the start point to the destination. Optimization based on error statistical characteristics is considered, by using the machine learning method. [11] combined soft mobile robot modeling with iterative learning control, and considered adaptive motion planning and trajectory tracking algorithms as its kinematic and dynamic states. The proposed approach bridges gaps between the localization process with theoretical qualitative analysis, and the path planning process with the reinforcement learning algorithm, to address cumulative error issues during the navigation procedure. An improved path model based on error strategy is established, and the optimal path is calculated through offline learning. The error drift is considered and processed in process of path planning, which can effectively reduce the tracking error.
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