Adaptive Mobile Robot Teleoperation for Intent-Guided Control
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Solution Overview
Problem
High-dimensional nonlinear robotic systems require efficient control methods for navigating unfamiliar scenarios, but existing teleoperation methods are inadequate due to reliance on prior knowledge, operator proficiency, and environmental assumptions, especially in high-disturbance scenarios like aerial robots.
Innovation Solution
A task-agnostic, user-independent adaptive teleoperation framework using motion primitives, which predicts operator intent and adapts available actions, allowing for robust and efficient control by minimizing entropy and leveraging onboard control systems to navigate unknown environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional teleoperation methods are used for high-dimensional nonlinear robotic systems, then human intuition can be leveraged to improve task performance, but the operator requires time-critical vigilance and reactive effort to keep the robot stable, reducing operator efficiency
Solution Approach 1:
The robot autonomously selects motion primitives from its library based on its current state and desired goals, performing self-navigation and self-stabilization without continuous human intervention. The autonomous navigation module automatically plans trajectories and selects appropriate motion primitives, allowing the robot to serve itself in navigation tasks while the operator focuses on higher-level decision-making.
Solution Approach 2:
The system dynamically adjusts the library of motion primitives based on the robot's current state, environmental conditions, and operational context. By changing the parameters and composition of the motion primitive library in real-time, the system adapts to different operational scenarios, improving both stability and operator efficiency without requiring manual reconfiguration.
2Ease of operation
If a passive robot role is assumed in teleoperation, then the operator has full control, but the robot cannot leverage its onboard control systems to enhance human capabilities in unknown scenarios
Solution Approach 1:
The autonomous navigation module acts as an intermediary between the operator and the robot's motion execution. It translates operator intent into sequences of motion primitives, leveraging the robot's onboard control systems and motion primitive library to bridge the gap between high-level operator commands and low-level robotic actuation, especially in unknown environments where pre-programmed behaviors are insufficient.
Solution Approach 2:
The system dynamically adapts the motion primitive library and navigation strategies based on real-time feedback from sensors and the robot's current state. This dynamic adjustment allows the robot to maintain ease of operation for the operator while simultaneously adapting to unknown scenarios, combining operator control with autonomous adaptability.
3Productivity
If shared autonomy is used to assist operators by combining autonomous assistive input with user input, then automation can enhance control, but these methods require prior knowledge of the task, environment, and user model
Solution Approach 1:
The autonomous navigation system segments the control task into distinct components: high-level goal setting by the operator and low-level motion execution by the robot's autonomous navigation module. This segmentation allows the system to provide control assistance through motion primitive selection and trajectory planning without requiring the operator to have prior knowledge of the specific navigation algorithms or environmental models.
Solution Approach 2:
The motion primitive library serves multiple functions: it provides stable motion patterns for navigation, enables adaptation to different environments, and offers a standardized interface between the autonomous navigation module and the operator. This multi-functionality reduces the need for task-specific, environment-specific, or user-specific models, making the system more generally applicable without increasing complexity.
Data Source
AI summary
Described herein is a framework for efficient task-agnostic, user-independent adaptive teleoperation of mobile robots and remotely operated vehicles (ROV), including ground vehicles (including legged systems), aircraft, watercraft and spacecraft. The efficiency of a human operator is improved by minimizing the entropy of the control inputs, thereby minimizing operator energy and achieving higher performance in the form of smoother trajectories by concurrently estimating the user intent online and adaptively updating the action set available to the human operator.