Robot Elevator Navigation Using Self-Learned Button Interaction
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Solution Overview
Problem
Robots are limited in their ability to navigate through unstructured environments, particularly in dynamic settings like hospitals and homes, where they cannot rely on complete knowledge of their surroundings and must adapt to changing conditions, and they struggle with interacting with elevator systems and obstacles.
Innovation Solution
Robotic devices equipped with sensors, manipulating elements, and processors that enable them to learn and execute skills for navigating elevators, including elevator lobby management, button manipulation, and ride management, using machine-learning techniques and social cues to interact with humans and adapt to unstructured environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If robots are designed to operate in structured environments with pre-programmed skills, then manufacturing precision and reliability are improved, but adaptability to unstructured environments deteriorates
Solution Approach 1:
The robot performs self-learning through autonomous interaction with the elevator system. The learning module enables the robot to automatically acquire navigation skills by observing and interacting with elevator components (buttons, doors, car interior) without human intervention or pre-programming, allowing adaptation to unstructured environments while maintaining reliable operation
Solution Approach 2:
The system changes the operational parameters of the robot from static pre-programmed commands to dynamic learned behaviors. The learning module continuously updates the robot's navigation parameters based on real-time sensor feedback and interaction outcomes, enabling adaptation to varying elevator configurations and unstructured environmental conditions
2Ease of operation
If robots use pre-programmed manipulation skills, then ease of operation is improved, but adaptability to dynamic environments deteriorates
Solution Approach 1:
The robot eliminates the need for complex pre-programming by using self-learning capabilities. The learning module automatically generates manipulation skills through autonomous interaction with elevator systems, making the robot easy to operate in dynamic environments without requiring extensive programming knowledge or environment-specific configuration
3Adaptability or versatility
If robots are equipped with comprehensive sensors and learning modules, then adaptability to unstructured environments is improved, but device complexity increases
Solution Approach 1:
The sensor system and learning module serve multiple functions: navigation, obstacle detection, button identification, door interaction, and skill learning. This multi-functionality reduces the need for separate specialized components, managing system complexity while enhancing adaptability to unstructured environments like hospitals and homes
4Productivity
If robots autonomously learn and execute elevator navigation skills, then productivity in unstructured environments is improved, but device complexity increases
Solution Approach 1:
The robot achieves high productivity in unstructured environments through autonomous self-learning of elevator navigation skills. The learning module automatically acquires and executes manipulation skills without human intervention, enabling efficient task completion in hospitals and homes while managing complexity through automated learning processes
Data Source
AI summary
Systems, apparatus, and methods are described for robotic learning, planning, and execution of skills, including navigation using elevators. In one or more embodiments, a robotic device may need to autonomously take an elevator, e.g., by using its autonomy features and/or relating on application programming interfaces (APIs) to communicate with elevators.