Systems and methods for training a robot to autonomously travel a route
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing robotic systems require exhaustive programming for specific environments, limiting their adaptability and efficiency, and often necessitate skilled technicians, making them costly and inefficient in dynamic or new environments.
Innovation Solution
Robots are trained to autonomously navigate routes by demonstration, creating maps and correcting errors using sensors and machine learning, allowing them to adapt to changing environments without extensive programming.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If robots are programmed with exhaustive coding for specific environments, then task-specific performance is improved, but adaptability to new environments deteriorates
Solution Approach 1:
The system creates a digital map (copy) of the physical environment that the robot can navigate. Instead of programming the robot to handle every possible environmental variation, the robot learns to navigate by creating and following a map representation, allowing it to adapt to new environments through mapping rather than reprogramming.
Solution Approach 2:
The robot transforms environmental data into structured map parameters that can be processed and navigated. By changing environmental parameters into map representations (coordinates, features, pathways), the system enables the robot to adapt to different environments by creating new parameter sets rather than requiring new programming.
2Reliability
If robots are programmed for controlled environments with predictable conditions, then navigation reliability is improved, but effectiveness in dynamically changing environments deteriorates
Solution Approach 1:
The robot performs preliminary mapping of the environment before navigation begins. By creating a map in advance and planning the route beforehand, the robot establishes a reliable navigation framework that can then adapt to dynamic changes during execution without compromising overall reliability.
Solution Approach 2:
The system continuously compares the robot's actual position and sensor data against the pre-created map, providing feedback that allows real-time adjustments. This feedback mechanism maintains navigation reliability in controlled environments while enabling adaptation to dynamic changes through corrective actions based on map comparisons.
3Adaptability or versatility
If general rules and logic are programmed for route determination, then versatility across different routes is improved, but navigation speed and efficiency deteriorate
Solution Approach 1:
The system pre-calculates and stores optimal routes in the map structure during the mapping phase. Instead of computing routes in real-time using general logic, the robot has route information prepared in advance, enabling fast navigation while maintaining versatility through the comprehensive map representation that can accommodate different routes.
4Measurement precision
If exhaustive programming is performed for each environment and route, then navigation precision is improved, but time and cost of operation deteriorate
Solution Approach 1:
The mapping system serves multiple functions: it creates an environmental representation, stores navigation routes, identifies key locations, and provides a framework for real-time navigation. This single universal map structure replaces multiple separate programming efforts for different environments and routes, maintaining precision while reducing time and cost.
Data Source
AI summary
Systems and methods for training a robot to autonomously travel a route. In one embodiment, a robot can detect an initial placement in an initialization location. Beginning from the initialization location, the robot can create a map of a navigable route and surrounding environment during a user-controlled demonstration of the navigable route. After the demonstration, the robot can later detect a second placement in the initialization location, and then autonomously navigate the navigable route. The robot can then subsequently detect errors associated with the created map. Methods and systems associated with the robot are also disclosed.