AI Delivery Zone Identification Using Imaging and LIDAR
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Solution Overview
Problem
Current systems for package delivery and transportation, including autonomous surface and aerial vehicles, face challenges in accurately identifying and navigating to pickup and drop-off zones, especially in complex environments where GPS and location-based services may be unreliable, leading to inefficiencies and incorrect deliveries.
Innovation Solution
The implementation of an AI and machine learning system that uses a combination of wireless signal strength, GPS, video, and image acquisition to learn and associate specific delivery zones with environmental features, such as trees or custom points of interest, to determine accurate pickup and drop-off locations, and enables the use of imaging and LIDAR systems to create precise profiles for transport devices like drones and rovers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS and location-based services are used to identify delivery zones, then the system can provide location information, but the accuracy becomes unreliable in complex environments
Solution Approach 1:
The patent introduces an intermediary imaging system that captures visual data of the environment and uses machine learning to associate images with delivery zones. This intermediary visual recognition system bridges the gap between GPS location and actual delivery zone identification, allowing accurate zone identification even when GPS signals are unreliable in complex environments.
Solution Approach 2:
The patent replaces the mechanical/GPS-based location identification system with an optical/electronic imaging system. Instead of relying on satellite-based mechanical positioning, the system uses cameras and machine learning algorithms to visually identify and associate delivery zones, providing more reliable identification in complex environments where GPS fails.
2Measurement precision
If multiple imaging systems are used to learn and associate environmental features with delivery zones, then the accuracy improves, but the device complexity increases
Solution Approach 1:
The patent applies multi-functionality by using the imaging system for multiple purposes: capturing environmental features, training machine learning models, identifying delivery zones, and navigating to locations. This universal use of the imaging system across multiple functions justifies the added complexity by providing comprehensive delivery zone identification capabilities.
Solution Approach 2:
The patent implements preliminary action by pre-training machine learning models with environmental images and associated delivery zone information before actual delivery operations. This preliminary training phase allows the system to accurately identify delivery zones during execution without requiring complex real-time processing, thereby managing device complexity while maintaining high accuracy.
3Adaptability or versatility
If the system learns from crowd-sourced data and multiple devices, then the adaptability improves, but the data processing complexity increases
Solution Approach 1:
The patent merges data from multiple devices and crowd-sourced sources into a unified machine learning model. By combining imaging data, GPS information, and user feedback from multiple sources, the system creates a comprehensive understanding of delivery zones across different environments, enhancing adaptability while managing data processing complexity through centralized processing.
Solution Approach 2:
The patent implements feedback mechanisms where the system learns from actual delivery outcomes and user feedback to continuously improve its delivery zone identification accuracy. This feedback loop allows the machine learning model to adapt to new environments and correct errors, enhancing versatility while the feedback processing is managed through iterative model updates rather than complex real-time processing.
Data Source
AI summary
An autonomous transport device system is enabled for one or more autonomous vehicles and autonomous surface delivery devices. Specific pickup and drop off zones for package delivery and user transport may be defined. The system leverages an artificial intelligence based learning algorithm to understand various environments. Packages may be dropped off in the geofenced areas. In some instances, packages may be stored in hidden areas that are purposely cached local to a likely delivery area. Some areas may be marked for pickup and drop off. Shippers may cache certain packages proximate to locations based on demand, joint distribution centers, and the presence of multiple transport devices including rovers, drones, UAVs, and autonomous vehicles.