AI based assistant systems:
The Future of Crane Operation

PSIORI Autonomous cranes

Operating a crane is dangerous work and demands high concentration on monotonous tasks over long periods of time. We have developed a range of AI functions that allow operators to delegate crane tasks to AutoCrane, our Artificial Intelligence system for cranes. 

AutoCrane encompasses a wide range of AI based solutions, from simple tasks such as sway control or approach to set way points via whole behaviours like autonomous pick-up and deposit of good to complete crane autonomy, where the AI executes all routine jobs.

Full autonomy

Fully automated crane operations

AutoCrane operates the crane and executes all routine tasks by itself. Operators supervise the crane’s activities. AuotCrane works 24/7, without pause, in all weather conditions.

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Partial autonomy

Perform individual tasks autonomously

If full autonomy is not desired or possible, the system will execute specific tasks itself, while other tasks are controlled by the operator.

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Assistant funcions

Support functions for operators

Use AutoCrane components that provide operator assistance. Suppress lateral or rotation sway, use shortcuts for approaching the target, view the grapple or hook’s position in 3D space - these and similar features make life easier for the operator and increase speed, safety and efficiency.

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Modern, functional HMI

Designed with operators in mind

Crane shortcut illustration

Our clear, modern HMI shows the current and planned crane behaviours, and allows operators to select full autonomy or specific behaviours.

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System Overview

A brief tour of AutoCrane’s technology

Crane shortcut illustration

AutoCrane uses visual and LIDAR data to build a map of the working area and to identify loads, vehicles and other objects on which it has been trained. AutoCrane’s sophisticated cognitive architecture ensures that perception and analysis result in the most efficient movement commands.

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Cognitive Architecture

Industrial AI for your crane

Crane Architecture Illustration

AutoCrane’s cognitive architecture is based on the Belief-Desire-Intention (BDI) model. This cognitive architecture simulates human-like reasoning, where beliefs represent the agent's knowledge of the world, desires are goals to be achieved, and intentions are the specific plans currently being executed. BDI agents dynamically update their intentions based on changing beliefs. 

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Interested in learning more?

Get in touch directly with our Managing Director of Sales. Simply press the button below, and we’ll connect you personally to discuss how our solutions can support your operations.

Volker Voss
Managing Director Sales

Get in touch

Interested in learning more? - Get in touch directly with our Managing Director of Sales. Simply press the button below, ask your questions, and we’ll get in touch to discuss how our solutions can support your operators.

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PSIORI was founded in 2016 by and around former students and employees of the research groups Prof. Dr. Wolfram Burgard (Autonomous Intelligent Systems) and Prof. Dr. Martin Riedmiller (Machine Learning) of the Technical Faculty of Freiburg. In the areas of SLAM and Reinforcement Learning, these research groups have decisively driven the state of research. In our research, we have dealt with the topics of autonomous driving, mobile robotics, machine learning, neural networks, reinforcement learning and generative AI as well as in the application with problems from the areas of sensor processing, self-location and mapping and with robot, vehicle and machine control.