Flexible Robotic Arm Control for Self-Propulsion in Confined Passages
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Snake-like robotic arms face limitations in navigating complex and cluttered environments, particularly in confined spaces like gas turbine engines, due to their slender and flexible nature, which restricts their ability to maintain traction and self-propel effectively without operator intervention.
Innovation Solution
The implementation of a system comprising a flexible robotic arm with multiple degrees of freedom, coupled with sensors and an actuator, uses a machine learning model to control the arm's movement based on environmental maps and operator instructions, enabling self-propulsion and traction in cluttered spaces through modes like sidewinding, corkscrewing, and inchworm movements, allowing the arm to automatically navigate and maintain position without direct operator control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the robotic arm is made slender and flexible to access confined spaces, then adaptability to complex environments is improved, but the ability to maintain traction and self-propel effectively deteriorates
Solution Approach 1:
The robotic arm transitions from static passive insertion to dynamic self-propelled movement through machine learning control. The system dynamically adjusts the arm's configuration and movement patterns (inchworm, sidewinding, corkscrewing) based on real-time sensor feedback and environmental maps, enabling the slender flexible arm to generate its own propulsion forces and maintain traction autonomously
Solution Approach 2:
The robotic arm becomes self-sufficient by autonomously navigating complex passages without operator intervention. The machine learning model enables the arm to self-propel through cluttered environments, automatically gain and maintain traction, and independently adjust its movement strategy based on sensor readings and environmental maps, eliminating the need for manual extraction or control
2Adaptability or versatility
If the robotic arm is made slender and flexible to access confined spaces, then adaptability to complex environments is improved, but the need for operator intervention increases
Solution Approach 1:
The robotic arm autonomously performs navigation tasks without operator intervention. The machine learning model processes sensor readings and environmental maps to generate control signals that enable self-propulsion, automatic traction maintenance, and independent navigation through complex passages, making the system self-sufficient and eliminating the need for continuous manual control
Solution Approach 2:
The patent replaces manual mechanical control with an intelligent autonomous control system. Instead of operators directly manipulating the robotic arm, a machine learning model processes environmental data and sensor feedback to automatically generate movement commands, substituting human-operated mechanical control with autonomous intelligent control
3Extent of automation
If machine learning control is implemented for autonomous navigation, then extent of automation is improved, but device complexity increases
Solution Approach 1:
The machine learning control system serves multiple functions: it processes sensor readings, generates environmental maps, determines navigation paths, controls actuator movements, and adapts to different passage geometries. This multi-functional approach consolidates what would otherwise require separate specialized systems into a single unified intelligent controller, managing complexity through functional integration
Data Source
Figure 1A
Figure 1B
Figure 2
AI summary
A robotic arm (102) is inserted into a passage (103) of a part (108) to be examined. Operator instructions defining a tip motion for a tip of the robotic arm (102), sensor readings, and an environmental map (111) are received. The operator instructions, the environmental map (111) and sensor readings are applied to a previously trained machine learning model (115) to produce control signals (120). The control signals (120) to an actuator (104) on the arm (102) to control a movement of the robotic arm (102) allowing the robotic arm (102) to automatically gain traction in the passage (103) and automatically self-propel according to a movement.