Warehouse Transport Robot Control Using Imitation-Learned Topological Routes
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Solution Overview
Problem
Existing automation solutions for unit load transport in intralogistics are hindered by high investment costs, increased space requirements, and reduced flexibility due to predefined transport routes, making them unsuitable for dynamic environments and requiring significant redesign efforts.
Innovation Solution
A transport robot system that includes a communication interface to receive environmental and capability models, allowing it to navigate and perform tasks autonomously or semi-autonomously. The environmental model defines key points in the warehouse as a topological map, while the capability model provides movement sequences learned through imitation learning, enabling flexible route adaptation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If automated guided vehicles (AGVs) with predefined transport routes are used, then automation is achieved, but flexibility is reduced and redesign costs increase when transport layout adjustments are needed
Solution Approach 1:
The patent applies dynamics by enabling the transport robot to dynamically adapt its navigation routes based on real-time environmental models rather than following fixed predefined paths. The robot can adjust its movement sequences dynamically in response to layout changes, achieving both automation and flexibility simultaneously.
Solution Approach 2:
The system changes parameters by using learned movement sequences that can be modified through imitation learning from human operators. When layout changes occur, the system updates the environmental model and adjusts navigation parameters accordingly, allowing automated adaptation without complete redesign.
2Productivity
If complex digital technologies and automation solutions are introduced, then productivity increases, but investment costs and infrastructure requirements increase
Solution Approach 1:
The patent uses imitation learning to copy human operators' movement sequences and decision-making processes. Instead of requiring complex programming and specialized knowledge, the system learns by observing and replicating human behavior, significantly reducing investment costs and infrastructure requirements while maintaining productivity improvements.
Solution Approach 2:
The transport robot performs self-learning through imitation learning, automatically adapting to new environments and tasks without requiring extensive programming or specialized personnel. This self-service capability reduces the need for complex infrastructure and ongoing technical support.
3Adaptability or versatility
If autonomous mobile robots (AMRs) with deliberative capabilities are deployed, then flexibility improves, but the need for highly specialized personnel for commissioning and adaptation increases
Solution Approach 1:
The system copies human operators' knowledge and skills through imitation learning. By observing and replicating human movement sequences and decision-making, the robot acquires operational knowledge without requiring specialized commissioning personnel, making flexible AMRs easier to deploy and adapt.
Solution Approach 2:
The transport robot performs self-learning and self-adaptation through imitation learning from human operators. This eliminates the need for highly specialized personnel for commissioning and adaptation, allowing flexible AMRs to be deployed by standard warehouse employees who can demonstrate tasks to the robot.
Data Source
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Figure 3a~3b
AI summary
The invention relates to a transport robot (120a), in particular an industrial truck (120a), which can be operated in an automated, semi-automated, and/or manual manner to carry out transport orders for transporting goods (140) in a warehouse having a plurality of transport robots (120a,b). The transport robot (120a) comprises a communication interface configured to receive an environmental model of the warehouse and a capability model of the plurality of transport robots (120a,b) in the warehouse. The environmental model defines a plurality of key locations of the warehouse and, for each key location, at least one adjacent key location.The capability model defines movement sequence information, in particular at least one trajectory, for an automated movement to the at least one adjacent key point for each of the key points of the environment model, wherein for each key point of the plurality of key points of the environment model, the movement sequence information of the capability model is based on at least one movement, learned by imitation learning, of a manually operated transport robot (120b) of the plurality of transport robots (120a,b) between the key point and the at least one adjacent key point.