Logistics Robot Routing Using ML-Based Route Issue Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Logistics operations face challenges in autonomously completing the last mile delivery due to the limitations of various robot types, such as legged, wheeled, and humanoid robots, which struggle with terrain, energy efficiency, and public acceptance, leading to unexpected issues that impact delivery times and costs.
Innovation Solution
A machine learning (ML) model is trained to associate map objects with route issues encountered by logistics robots, considering robot type, energy consumption, and dynamic conditions to optimize route planning and selection, using a logistics platform and database to identify potential obstacles and adjust routes accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If different robot types (legged, wheeled, humanoid) are deployed for logistics routes, then the robots can navigate different terrains and perform specific tasks, but each robot type has limitations (energy consumption, navigation capability, complexity, cost) that reduce overall system reliability
Solution Approach 1:
The system dynamically selects robot types and routes based on real-time environmental conditions, terrain characteristics, and robot performance data. The routing approach transitions from static to dynamic, allowing the system to adapt robot deployment to changing conditions and avoid situations that would cause failure, thereby improving reliability while maintaining versatility.
Solution Approach 2:
The system implements feedback loops where robot performance data, route issues, and environmental conditions are continuously collected and used to improve future routing decisions. This feedback mechanism allows the system to learn from past experiences and optimize robot deployment, reducing failures and improving delivery reliability.
2Productivity
If traditional routing approaches are used for logistics robots, then route planning is simple, but unexpected issues occur that impact delivery times and costs
Solution Approach 1:
The system performs preliminary analysis of route conditions and robot capabilities before deployment, identifying potential issues in advance. By pre-assessing terrain complexity, environmental conditions, and robot suitability, the system can prevent route failures before they occur, improving both delivery speed and reliability.
Solution Approach 2:
The system continuously collects feedback on route issues and robot performance, using this information to improve future routing decisions. This iterative learning process enables the system to anticipate and avoid problems, reducing delivery delays and improving route completion reliability.
3Reliability
If sophisticated routing approaches are implemented to improve delivery reliability, then route planning complexity increases, but unexpected issues still occur
Solution Approach 1:
The system employs a universal routing framework that can handle multiple robot types, terrain conditions, and delivery scenarios through a single integrated approach. This multi-functional system reduces the need for separate complex routing algorithms for different situations, maintaining reliability while managing overall system complexity.
Solution Approach 2:
The system uses machine learning models that automatically learn from data and improve routing decisions without requiring manual configuration or complex rule-based systems. This self-learning capability enables the system to achieve high reliability through adaptive intelligence rather than through manually programmed complexity.
4Productivity
If robot deployment is increased to meet growing delivery demand, then delivery capacity improves, but energy consumption and operational costs increase
Solution Approach 1:
The system optimizes energy consumption by dynamically changing operational parameters such as robot selection, route timing, and traversal speed based on environmental conditions and delivery priorities. This parameter optimization allows the system to maintain high delivery capacity while minimizing energy usage per delivery.
Solution Approach 2:
The system dynamically adjusts robot deployment strategies based on real-time conditions, selecting the most energy-efficient robot types and routes for each delivery task. This dynamic optimization enables the system to scale delivery capacity without proportionally increasing energy consumption.
Data Source
Figure 1
Figure 2
Figure 3a~3b
AI summary
Method, apparatuses and computer program products for training machine learning models for logistics robots and routing the robots are disclosed. A method of training an ML model involves providing a route to a logistics robot, obtaining route issue indications from the robot when it fails to traverse the route as expected, and associating map objects with the issue locations. The trained model can then be used to determine the likelihood of route issues for a specific logistics robot type based on the presence of certain map objects along the route. The disclosure further involves calculating route penalty value(s) based on the likelihood and updating the route accordingly, including selection of a logistics robot type for the route.