Delivery Robot Route Mapping From Frontage Images and Drop-Off Labels
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Solution Overview
Problem
Conventional delivery robot operations for the 'last mile' face challenges such as risk of damage to properties, high costs associated with pre-mapping, and time inefficiencies, particularly when pre-mapping is unnecessary or when obstacles are undetectable by sensors.
Innovation Solution
A method where an individual captures an image of the property's frontage, including a package drop-off point, and converts it into a travel map that the delivery robot can use, incorporating labels for traversable and non-traversable areas, to generate a safe and efficient route, minimizing lawn damage and avoiding obstacles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the delivery robot uses sensors to avoid obstacles on the lawn, then it can navigate the last mile, but it risks causing damage to the lawn and objects
Solution Approach 1:
The system performs pre-mapping of the delivery area before actual deliveries. A robot or other device traverses the area in advance to create a digital map identifying obstacles, traversable paths, and safe zones. This preliminary action allows subsequent delivery robots to navigate using the pre-created map, avoiding obstacles without needing to detect them in real-time, thus eliminating lawn damage risk while maintaining navigation capability.
2Reliability
If the delivery robot executes a pre-mapping procedure, then it obtains a pre-mapped route, but it incurs high costs including deployment, setup, and procedure execution time
Solution Approach 1:
The pre-mapping system is designed to serve multiple delivery locations and multiple robots simultaneously. A single pre-mapping operation creates a reusable digital map that can be stored and accessed by multiple delivery robots for various deliveries to different addresses within the mapped area. This multi-functionality amortizes the time and resource costs of pre-mapping across numerous deliveries, making the process economically viable.
Solution Approach 2:
The system performs area mapping in advance before actual deliveries occur. The digital map is created once and stored for future use, eliminating the need to repeat the mapping process for each delivery. This preliminary action separates the time-consuming mapping phase from the delivery execution phase, allowing deliveries to proceed efficiently using pre-existing route information.
3Productivity
If the delivery robot follows a pre-mapped route, then it reduces delivery time, but it requires expensive pre-mapping deployment for every location
Solution Approach 1:
The pre-mapping system creates universal digital maps that serve multiple delivery locations and multiple robots. A single mapped area can handle numerous deliveries to different addresses within that area, spreading the pre-mapping cost across many deliveries. This eliminates the need to deploy pre-mapping resources for every individual location, reducing overall energy and resource consumption while maintaining fast delivery speeds.
Solution Approach 2:
The system creates digital copies of the physical delivery environment through pre-mapping. Instead of physically deploying robots to map each location repeatedly, a digital replica of the area is created once and reused indefinitely. This digital copy contains all necessary navigation information, allowing robots to execute deliveries quickly without incurring repeated pre-mapping costs.
4Loss of energy
If the delivery robot traverses the last mile without pre-mapping, then it avoids pre-mapping costs, but it faces undetectable obstacles that sensors cannot identify
Solution Approach 1:
The system performs preliminary traversal and mapping of the delivery area in advance, creating a comprehensive digital map that identifies all obstacles including those undetectable by robot sensors. This preliminary action captures information about hidden obstacles such as uneven terrain, soft ground, or objects below sensor detection thresholds. Subsequent robots use this pre-collected information to avoid these obstacles without needing to detect them in real-time, maintaining low operational costs while overcoming detection limitations.
Data Source
AI summary
This disclosure is generally directed to systems and methods for generating a last mile travel route for a delivery robot. In an example embodiment, an individual captures an image of a frontage of a property. The image may include a package drop-off spot where a package can be dropped off by the delivery robot. The individual may also insert labels and/or illustrations into the image for conveying information such as object descriptors (front door, drop-off spot, etc.), traversable areas (driveway, walkway, etc.), and non-traversable areas (lawn, flower bed, etc.). The image is converted by a computer into a travel map that the delivery robot can use to identify a travel route through the frontage area of the property. The travel map may be provided in various forms such as a semantic map of the frontage area or a bird's eye view rendering of the frontage area.