Collaborative Robot Control Using Work Sector Movement Prediction
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Solution Overview
Problem
Collaborative robots (cobots) face inefficiencies in working with human operators due to unpredictable human task durations and variations, leading to reduced robot efficiency as they often wait unnecessarily for human input, especially in tasks requiring both human intelligence and mechanical operations.
Innovation Solution
A predictive control method using detection devices like cameras or sensor mats to track human operator positions and residence times, processing data to predict future work sector movements and synchronize robot tasks with human activities, allowing the robot to prepare for cooperative tasks in advance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the robot waits for the human operator to complete tasks in traditional collaborative work, then the human operator can perform tasks independently, but the robot efficiency decreases due to unnecessary waiting time
Solution Approach 1:
The system performs preliminary actions by detecting the human operator's current task and predicting future tasks in advance. The robot prepares for upcoming tasks before they occur, eliminating idle waiting time. For example, when the operator is currently performing task A, the system predicts task B will follow and positions the robot accordingly, so the robot is ready immediately when task B begins.
Solution Approach 2:
The system implements continuous feedback by monitoring the human operator's real-time actions and using this information to adjust robot behavior dynamically. Detection devices track the operator's movements and task progression, providing feedback that enables the prediction algorithm to update its forecasts and the robot to adapt its actions continuously, optimizing collaboration efficiency.
2Productivity
If the robot is assigned independent tasks in traditional collaborative work, then the robot can work autonomously, but collaboration efficiency decreases due to lack of synchronization with human activities
Solution Approach 1:
The system uses continuous feedback from detection devices that monitor the human operator's actions, task progression, and workspace interactions. This real-time information flow enables the robot to understand human intent and synchronize its actions with human activities, preventing information loss about task coordination needs.
Solution Approach 2:
By detecting current human tasks and predicting future tasks in advance, the system enables the robot to perform preliminary positioning and preparation actions. This ensures the robot is synchronized with human activities before they occur, maintaining optimal collaboration efficiency without losing task coordination information.
3Adaptability or versatility
If traditional task planning is used for collaborative robots, then tasks can be assigned to human and robot, but flexibility decreases when human operations vary in duration or sequence
Solution Approach 1:
The system transitions from static, pre-programmed task plans to dynamic, real-time adaptive planning. The detection devices continuously monitor human operator actions, and the prediction algorithm dynamically adjusts task assignments and timing based on actual observed behavior rather than fixed schedules, enabling flexibility when human operations vary in duration or sequence.
Solution Approach 2:
The system enables the collaborative robot to self-adjust its task planning by automatically detecting human actions, predicting future tasks, and modifying its own schedule without external intervention. This self-service capability provides adaptability to human variations while keeping the control system relatively simple, as the robot autonomously handles the complexity of dynamic planning.
Data Source
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AI summary
This disclosure relates to a method of controlling a collaborative robot, or "cobot". According to the disclosed method the "cobot" is controlled so as to make it ready to perform a task in collaboration with the human operator only when the latter is about to move into a work sector to carry out the task collaborating with the robot. The control method of the present disclosure can be implemented by means of a control system comprising detection devices, such as for example one or more cameras or a mat equipped with sensors, which detect the position of the hands or of the entire body of the operator in the space of work, a memory in which to store identification data of the sectors of work engaged by the human operator, of the times of permanence in them and of the successive sectors of work in which the human operator moves, as well as a control microprocessor unit which processes this data stored in the memory according to the method of this disclosure to predict in which work sector the operator will move his hands and when that will happen, and which controls a robot based on this prediction information. The method of this disclosure can be implemented by means of software executed by a microprocessor unit.