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Mechatronics - Autonomous Forklift Learning for Efficient Pallet Handling Using AI and Machine Learning

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Efficient material handling is crucial in industries like warehouses, logistics, and manufacturing. This project aims to leverage AI and machine learning techniques to enable an autonomous forklift to learn and optimize the process of traveling, picking up a pallet, and placing it precisely at a designated location within a simulated environment. This technology will improve productivity and safety in various industrial settings.


Project Objectives:

Simulated Environment Setup:

Develop a realistic simulated environment that emulates the physical constraints of a warehouse or manufacturing facility.

Create a digital representation of the forklift, pallets, and the surrounding environment.

AI and Machine Learning Framework:

Choose and implement an AI and machine learning framework suitable for training the forklift model.

Develop a reinforcement learning (RL) or imitation learning (IL) approach to facilitate autonomous decision-making.

Training Data Collection:

Generate training data by simulating various scenarios, including different pallet positions, layouts, and environmental conditions.

Collect data on the forklift's actions, sensor inputs, and outcomes (e.g., successful pallet pickup and placement).

Forklift Control System:

Develop the control system for the autonomous forklift, incorporating AI and machine learning models.

Implement algorithms for path planning, pallet recognition, and collision avoidance.

Efficiency Optimization:

Train the forklift model to optimize its movements and decision-making for efficient pallet handling.

Define objectives, such as minimizing travel time and maximizing successful pallet pickups and placements.

Safety Protocols:

Implement safety mechanisms and fail-safe procedures to ensure safe operation in real-world scenarios.

Develop algorithms for emergency stops and obstacle avoidance.

Real-time Monitoring and Feedback:

Create a real-time monitoring system to track the forklift's performance and provide feedback.

Implement data logging and analysis tools to assess efficiency improvements.



Expected Outcomes:

An autonomous forklift capable of efficiently traveling, picking up pallets, and placing them accurately in a simulated environment.

Improved pallet handling efficiency, reducing travel time and errors.

Insights into the application of AI and machine learning in optimizing material handling processes.


This project will contribute to the advancement of autonomous material handling systems by utilizing AI and machine learning to optimize forklift operations. The successful implementation of an autonomous forklift in a simulated environment will pave the way for more efficient and cost-effective material handling in industrial settings.


Hive Autonomy

We lead the digital and autonomous transformation for logistics and enable our customers to grow operations while facilitating the green shift. At Hive Autonomy, we bring an advanced and valuable transformation of load-handling processes, making them safer, more productive, and more sustainable.


Type: Fra virksomhet
Publisert: 2023-09-29
Status: Ledig
Grad: Master



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