Path Planning for Automatic Guided Vehicles (AGVs) Fusing MH-RRT with Improved TEB

Jiayi Wang,Xiaojun Tan,Yonghu Luo

doi:10.3390/act10120314

Jiayi Wang, Xiaojun Tan + Show 1 more

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https://doi.org/10.3390/act10120314

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Abstract

In this paper, an AGV path planning method fusing multiple heuristics rapidly exploring random tree (MH-RRT) with an improved two-step Timed Elastic Band (TEB) is proposed. The modified RRT integrating multiple heuristics can search a safer, optimal and faster converge global path within a short time, and the improved TEB can optimize both path smoothness and path length. The method is composed of a global path planning procedure and a local path planning procedure, and the Receding Horizon Planning (RHP) strategy is adopted to fuse these two modules. Firstly, the MH-RRT is utilized to generate a state tree structure as prior knowledge, as well as the global path. Then, a receding horizon window is established to select the local goal point. On this basis, an improved two-step TEB is designed to optimize the local path if the current global path is feasible. Various simulations both on static and dynamic environments are conducted to clarify the performance of the proposed MH-RRT and the improved two-step TEB. Furthermore, real applicative experiments verified the effectiveness of the proposed approach.

Highlights

The multiple heuristics rapidly exploring random tree (MH-rapidly exploring random tree (RRT)) and two-step Timed Elastic Band (TEB) are fused for further validation in real experiments
The hardware platform is the Turtlebot2 robot, which is one of the automatic guided vehicles (AGVs) driven by the two differential driving wheels
The AGV is driven by a Lenovo notebook and complied with C++

Summary

Introduction

An AGV path planning method fusing multiple heuristics rapidly exploring random tree (MH-RRT) with an improved two-step Timed Elastic Band (TEB) is proposed. A receding horizon window is established to select the local goal point On this basis, an improved two-step TEB is designed to optimize the local path if the current global path is feasible. Local trajectory optimization is important because of the time optimal and energy optimal requirement In closed environments such as factories, fixed guided lines are adopted in order to improve reliability [5]. The time cost and memory usage increase exponentially as the problem scale increases Samplingbased algorithms, such as the probabilistic roadmap algorithm (PRM) [10] and rapidly exploring random tree (RRT) [11], have been proven effective in solving many tough planning problems, with real-time nonlinear systems.

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