A real-time trajectory planning method for enhanced path-tracking performance of serial manipulators

Marco Faroni,Manuel Beschi,Antonio Visioli,Nicola Pedrocchi

doi:10.1016/j.mechmachtheory.2020.104152

Marco Faroni, Manuel Beschi + Show 2 more

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https://doi.org/10.1016/j.mechmachtheory.2020.104152

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Abstract

Robotized assembly and manufacturing often require to modify the robot motion at runtime. When the primary constraint is to preserve the geometrical path as much as possible, it is convenient to scale the nominal trajectory in time to meet the robot constraints. Look-ahead techniques are computationally heavy, while non-look-ahead ones usually show poor performance in critical circumstances. This paper proposes a novel technique that can be embedded in non-look-ahead scaling algorithms to improve their performance. The proposed method takes into account the robot velocity, acceleration, and torque limits and modifies the velocity profile based on an approximated look-ahead criterion. To do this, it considers only the last point of a look-ahead window and, by linearizing the problem, it computes the maximum admissible robot velocity. The technique can be applied to existing trajectory scaling algorithms to confer look-ahead properties on them. Simulation and experimental results on a 6-degree-of-freedom manipulator show that the proposed method significantly reduces path-following errors.

Highlights

Recent advances in industrial robotics require robots to work in unstructured environments or to cooperate with other agents
This has renewed the interest in motion planning algorithms that cope with dynamic situations
In the least demanding task (t f = 5 s), Timing-law Adaptation Module (TAM) and MPC’s errors are around ten times smaller than Non-Look-Ahead method (NLA)’s error; in the most demanding task (t f = 2 s), this difference grows of three orders of magnitude

Summary

Introduction

Recent advances in industrial robotics require robots to work in unstructured environments or to cooperate with other agents (e.g., human operators). This has renewed the interest in motion planning algorithms that cope with dynamic situations. Step 2: A time parametrization is assigned to the path. The result of this step is a velocity profile along the path. Such profile should respect the robot limits (e.g., joint maximum velocity, acceleration, and/or torque) and process requirements (e.g., constant-speed traits in gluing and laser-cutting operations)

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