Systems and methods for initializing a robot to autonomously travel a trained route
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current robotics systems require exhaustive coding and are often limited to controlled environments, necessitating expert technicians and being inefficient in dynamically changing or new environments, especially when programming robots to travel specific routes.
Innovation Solution
A system and method that utilize a camera and odometry unit to detect an initialization object, learn a route by user demonstration, and autonomously navigate using position associations, reducing the need for environment-specific programming and skilled technicians.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If robots are programmed with exhaustive coding to anticipate every situation, then they can perform specific tasks effectively and efficiently, but they are limited to those tasks and cannot perform others, and require expert technicians to program
Solution Approach 1:
The system captures demonstrations of task performance and creates a reusable model or record of the demonstrated behavior. This copied demonstration can then be played back or adapted to perform the same task without requiring re-programming, enabling both efficient execution and adaptability to different tasks through demonstration capture
Solution Approach 2:
The robot system is designed to handle multiple different tasks through a unified demonstration-based programming approach. Instead of requiring separate exhaustive code for each task, a single demonstration capture mechanism can record and reproduce various tasks, making the system universally applicable across multiple functions
2Productivity
If robots are programmed to perform specific tasks effectively, then they can execute those tasks efficiently, but they cannot operate in dynamically changing environments or new environments for which they were not specifically programmed
Solution Approach 1:
The system allows the robot to dynamically adapt to new environments by capturing demonstrations in those specific environments and immediately utilizing them. Rather than requiring static pre-programming for all possible environments, the robot can dynamically learn and adapt through demonstration capture when encountering new environments, maintaining efficiency while gaining adaptability
3Extent of automation
If programmers program maps and identify each point for robot navigation, then the robot can autonomously navigate desired paths, but the programming is time-consuming and requires highly skilled workers
Solution Approach 1:
Instead of manually programming navigation routes by creating maps and identifying points, the system captures a demonstration of the desired navigation path and replays it. This copied demonstration automatically encodes the route information, eliminating time-consuming manual programming while maintaining autonomous navigation capability
Solution Approach 2:
The system allows the robot to learn navigation routes through demonstration capture rather than requiring external expert programmers. The demonstration process itself serves to automatically program the navigation behavior, enabling the system to self-program or be easily programmed by non-experts
4Measurement precision
If conventional systems program starting locations for robots, then robots can determine their positions in environments, but the process is time-consuming and lacks robustness for user-friendly operation
Solution Approach 1:
The system captures the starting location information as part of the overall demonstration and replays it during execution. This copied position data is automatically associated with the demonstrated task, providing accurate position determination without requiring separate time-consuming programming steps, thereby improving both precision and ease of operation
Data Source
AI summary
Systems and methods for initializing a robot to autonomously travel a route are disclosed. In some exemplary implementations, a robot can detect an initialization object and then determine its position relative to that initialization object. The robot can then learn a route by user demonstration, where the robot associates actions along that route with positions relative to the initialization object. The robot can later detect the initialization object again and determine its position relative to that initialization object. The robot can then autonomously navigate the learned route, performing actions associated with positions relative to the initialization object.