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Operation of the Collaborative Composite Manufacturing CCM System

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A Local Planner for Accurate Positioning for a Multiple Steer-and-Drive Unit Vehicle Using Non-Linear Optimization.

Henrik Andreasson¹, Jonas Larsson², Stephanie Lowry¹

¹Centre for Applied Autonomous Sensor Systems (AASS), Örebro University, 701 82 Örebro, Sweden.

Sensors (Basel, Switzerland)

|April 12, 2022

Summary

This summary is machine-generated.

This study introduces MSDU, a novel local planner for pseudo-omnidirectional vehicles. MSDU offers smoother, more accurate, and shorter paths than existing methods, enhancing mobile manipulation in confined spaces.

Keywords:

local planning obstacle avoidance optimal control

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Area of Science:

Robotics
Artificial Intelligence
Control Systems

Background:

Pseudo-omnidirectional vehicles possess unique motion capabilities, including sideways driving and in-place rotation.
Efficient local planning is crucial for mobile manipulation tasks, especially in narrow and confined environments.
Existing local path planners like Timed Elastic Band (TEB) may not fully exploit omni-directional capabilities.

Purpose of the Study:

To present a novel local planning approach, MSDU (Motion planning via Sequential Dynamic Updates), tailored for pseudo-omnidirectional vehicles.
To leverage optimal control and non-linear optimization to enable smooth, efficient, and obstacle-avoiding navigation.
To evaluate MSDU's performance against a benchmark planner in real-world mobile manipulation scenarios.

Main Methods:

Formulation of a non-linear optimization problem based on optimal control principles.
Exploitation of the vehicle's pseudo-omnidirectional capabilities for enhanced maneuverability.
Real-world platform implementation and comparative analysis with the Timed Elastic Band (TEB) planner.

Main Results:

MSDU generated smoother and more accurate paths compared to TEB, with significantly lower mean translational and angular errors.
MSDU planned consistently shorter paths in terms of both translational and angular distances traveled.
The planner demonstrated effectiveness in narrow and confined areas, crucial for mobile manipulation.

Conclusions:

MSDU is a highly effective local planner for pseudo-omnidirectional vehicles, outperforming TEB in path quality and efficiency.
The optimal control-based approach successfully utilizes omni-motion capabilities for superior navigation.
MSDU shows significant promise for real-world mobile manipulation applications requiring precise and efficient movement in constrained spaces.