Autonomous crane for warehouse management
Basic information
Project ID: AEE-2019-15
Students: Arnab Chattopadhyay, Joakim Högnäsbacka, Mikko Lähteenmäki, Kaarle Patomäki, Joonas Pulkkinen, Janne Salovaara and Sampo Simolin
Project manager: Mikko Lähteenmäki
Instructor: Timo Oksanen, Panu Kiviluoma
Other advisors: Juuso Autiosalo and Matti Kleemola
Starting date: 10.1.2019
Completion date: 5.4.2019
Abstract
The project “Autonomous crane for warehouse management” started as an entry to the SICK innovation competition 2019 in addition to being a project in the Project Work course for AEE students and in the Mechatronics Project course for the mechanical engineering students in the project team. The team was provided with a high-end LIDAR by SICK and the ultimate goal of the project in the context of the competition was to utilize the LIDAR in creating a new innovation. Given access to a programmable overhead crane and a project budget, it was possible to build a system combining the crane and the LIDAR. With the time and resources available and with the team's desire to build something practical and challenging, the scope of the project was set to enabling the crane to operate automatically in a warehouse type of environment using the LIDAR. The desired functionality was that the crane would detect an object in a certain area, move the crane hook on top of it allowing it to be attached manually to the hook and moving and placing the object to a suitable position in a target location. Additionally, the crane was supposed to be able to recalculate its path to the next location and avoid unexpected obstacles and humans.
In the end, the final implementation of the enhanced crane possessed most of the planned features and was able to perform a pick-and-place demonstration. The features left out due to time and complexity constraints were mainly the final object positioning and human avoidance. The system is also limited by the fact that it can currently hold only a limited map of the obstacles. Nevertheless, the autonomous crane successfully demonstrates the feasibility of such an application and it achieved the 1st place in the SICK innovation competition.
The crane in operation and visualization of the system's internal map.
1st place in SICK innovation competition 2019. (Photo: Tero Lehto)
Objective
The goal of our project was to create a system around an overhead crane that would, with the help of a high-end LIDAR, be able to autonomously navigate around obstacles and so could perform simple tasks such as going to pick up an object and placing it in another location.
Results
The final result of the project is a semi-automatic intelligent crane system, which can move an object from a pick-up area to a predetermined spot while avoiding obstacles. This is accomplished by using a LIDAR laser scanner, a stepper motor, a Windows PC, a Raspberry Pi 3 Model B+ -microcontroller, A* path planning algorithm and an overhead crane with wireless control. The crane used for this project is a Konecranes intelligent overhead crane with no pre-existing obstacle avoidance systems. The crane was already usable at the start of the project with OPC UA communication protocol for crane position value and speed control. The LIDAR scanner (MRS6000) was provided by SICK as the starting point for their innovation competition.
The completed system can start from any position inside the working area of the overhead crane and by using scanner data and path planning algorithms the crane automatically avoids obstacles on its way to the preprogrammed pick-up area. Within the pick-up area, the system is able to detect the pickable object from the floor and moves the crane hook on top of the object so the hook can be attached to it. For this project, there was no automatic gripping system to pick up the object and thus the object needs to be attached by hand. After this, the object is lifted up and transported to a preprogrammed storage position while continuing to avoiding obstacles. When the storage position is reached, the crane lowers the object down and once the object is detached, the crane can start the process again from the start.
The LIDAR on the crane. The dataflow of the system.
Demonstration video:
On the right side of the video, the actual crane operation can be seen matched with the corresponding visualization window to the left of it in the center, and the leftmost window is for responding to user prompts and showing the calculations for the walls. In the visualization, the yellow diamond and the small rotating yellow diamond shape in the middle of the window correspond to the crane hoist and the LIDAR respectively. The purple line connected to the yellow diamond shape is the planned movement trajectory to the next destination. The green square grids are the detected obstacles, and the observed points outside of the grids are assumed to be noise. The blue square represents the 2x2 meter pick-up area, which has its location hardcoded in the system. Once the crane has moved to the pick-up area, it will automatically search for a pickable object within it, calculate its exact position and move the hook above the center point of the object. The target position, where the object is to finally be moved to, is a hardcoded point marked to the floor with an x with white tape.
Demonstration (8x speed) (description).mp4
Demonstration video (without intro, different playback speeds):
Final report:
Describes how the system was implemented and includes a user manual and the project plan.
