Shared-Control Teleoperation for Robot Intent Prediction
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Solution Overview
Problem
Existing teleoperation systems in telerobotics require additional hardware and instrumentation for haptic feedback, increasing cost and cognitive load on human operators, and lack the ability to predict critical motion intended by the human operator during domain-specific tasks like pick and place, especially in known environments.
Innovation Solution
A system and method for mobile robot teleoperation using shared control, which includes acquiring human operator input, reconstructing kinematic states, and using model predictive control to follow the human operator's trajectory and intended goal, minimizing cognitive load by providing autonomous assistance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If haptic feedback is provided to the human operator using bilateral teleoperation, then system stability is improved, but device complexity and cost increase due to additional hardware and instrumentation
Solution Approach 1:
The patent extracts and removes the haptic feedback mechanism from the teleoperation system, relying solely on visual feedback for operator control. This eliminates the need for force sensing and haptic actuators, thereby reducing device complexity and cost while maintaining system functionality through autonomous capabilities.
Solution Approach 2:
The robot is equipped with autonomous capabilities including collision avoidance and intent prediction, allowing it to independently handle stability and safety concerns without requiring haptic feedback from the operator. The system serves itself by making autonomous decisions based on visual information and environmental sensing.
2Reliability
If haptic feedback is provided to the human operator, then system stability is improved, but cognitive load on the human operator increases
Solution Approach 1:
The robot autonomously manages stability and collision avoidance without requiring the operator to process haptic feedback, thereby reducing cognitive load. The autonomous system handles complex stability computations and environmental adaptations independently.
Solution Approach 2:
The system performs preliminary autonomous actions for collision avoidance and stability maintenance before the operator needs to react, reducing the operator's cognitive burden by pre-processing complex decision-making tasks.
3Device complexity
If teleoperation uses only visual feedback, then device complexity is reduced, but the robot requires autonomous capabilities for collision avoidance and intent determination
Solution Approach 1:
The patent merges teleoperation with autonomous capabilities into a hybrid system. The robot combines visual feedback-based teleoperation with autonomous collision avoidance and intent prediction, allowing simplified hardware while maintaining high-level functionality through integrated autonomous behaviors.
Solution Approach 2:
The system introduces an intermediary autonomous control layer that processes visual feedback and environmental data to generate collision avoidance commands and predict operator intent, bridging the gap between simple visual feedback and complex robotic tasks.
4Device complexity
If existing teleoperation techniques are used, then system cost is reduced, but ability to predict critical motion during domain-specific tasks is lost
Solution Approach 1:
The system performs preliminary prediction of operator intent and critical motion before the operator executes the action. By analyzing visual feedback, robot state, and task context in advance, the system anticipates fine alignment requirements for pick-and-place tasks, improving measurement precision without additional hardware.
Solution Approach 2:
The patent replaces mechanical haptic feedback systems with computational models for motion prediction. Software-based intent prediction algorithms substitute for physical force feedback, maintaining low system cost while achieving high precision in predicting critical motion for domain-specific tasks.
Data Source
AI summary
This disclosure relates to system and method to reconstruct human motion for mobile robot teleoperation using shared control. The method of the present disclosure acquire an input feed of a human operator to perform a task with assistance in a remote environment using shared control. The mobile robot reconstructs to follow a trajectory of the human operator towards the intended goal in the remote environment. The mobile robot determines at least one goal intended by the human operator based on a previously occurred state, a current kinematic state and a future trajectory of the human operator and a known position of the plurality of goals. The model predictive control generates at least one instruction to control the movement of the mobile robot to perform at least one of following the trajectory of the human operator and reaching the operator intended goal based on a joint angle position and a velocity.