Robot Cooperation Control for Shared Human-Robot Tasks
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current robotic systems lack effective methods for cooperative behavior planning in shared workspaces with human operators, as existing approaches are tailored to specific scenarios and do not provide a general framework for optimizing joint actions towards shared goals.
Innovation Solution
A method for controlling autonomous devices that quantifies cooperative behavior using a generic measure, allowing for optimized joint behavior planning between robots and human operators, enabling maximally cooperative actions by determining synergistic contributions and adapting to individual agent abilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If robotic systems operate in isolated workspaces strictly separated from human operators, then safety and control are maintained, but collaboration and task efficiency are limited
Solution Approach 1:
The patent merges the previously isolated robotic workspace with human operator workspace into a shared collaborative workspace. The robotic system and human operator operate together in the same physical space, enabling direct interaction and collaboration on shared tasks, thereby improving task efficiency while maintaining safety through optimized cooperative behavior planning.
Solution Approach 2:
The control system is designed to handle multiple modes of operation including isolated robotic operation, human-only operation, and collaborative operation. This universal control framework allows the system to adapt to different task requirements and workspace configurations, enabling both safety-critical isolated operation and efficiency-enhancing collaboration.
2Productivity
If collaborative robotic systems share workspace with humans and physically interact, then task efficiency and collaboration are improved, but behavior planning complexity increases
Solution Approach 1:
The behavior planning is segmented into distinct modules: a cooperative behavior optimizer that determines optimal cooperative actions, a contact sequence planner that manages physical interactions, and a motion generator that executes actions. This modular segmentation reduces overall planning complexity by breaking down the complex collaborative behavior into manageable, specialized components.
Solution Approach 2:
The system performs preliminary optimization of cooperative behavior by pre-computing optimal cooperative actions and contact sequences based on predicted human intentions and task goals. This preliminary planning reduces real-time computational complexity during actual collaboration by having the behavior optimization prepared in advance.
3Device complexity
If robotic systems use traditional isolated operation modes, then system simplicity is maintained, but adaptability to collaborate with human operators is reduced
Solution Approach 1:
The control system dynamically adapts its behavior based on the operational context, switching between isolated operation, collaborative operation, and human-only operation modes. The cooperative behavior optimizer continuously adjusts the robotic system's actions based on real-time human operator behavior and task requirements, providing high collaboration adaptability while maintaining relatively simple underlying control structures.
4Productivity
If online control mechanisms optimize cooperative behavior in real-time, then collaboration effectiveness is improved, but computational requirements and processing time increase
Solution Approach 1:
The system performs preliminary optimization of cooperative behavior by pre-computing optimal actions based on predicted human intentions and task goals before actual execution. This advance planning reduces real-time processing requirements and computational time during actual collaboration while maintaining high collaboration effectiveness.
Solution Approach 2:
The cooperative behavior optimizer focuses computational resources on optimizing only the critical aspects of cooperative behavior that most impact collaboration effectiveness, rather than optimizing all possible parameters. This selective optimization reduces processing time while maintaining high collaboration effectiveness.
Data Source
Figure 1
Figure 2
Figure 3~4
AI summary
A method for controlling at least one autonomous device concerns the at least one autonomous device as at least one of at least two agents that cooperatively perform a common task in a common environment. The method comprises obtaining variables on a current state of each of the at least two agents in the common environment, and obtaining a variable on at least one current state of the common environment. The variable describes a distance of the current state of the at least two agents to a common goal state or a task success. The method determines a quantitative measure for cooperative behaviour of the at least two agents, wherein the quantitative measure quantifies an extent to which a mutual support or an adaption in joint cooperative actions towards the common goal state increases a joint action space of the at least two agents. Then the method optimizes a joint behaviour of the at least two agents using the determined quantitative measure based on the obtained variables of the current states of the at least two agents and the obtained further variable on the current state of the common environment, and determines at least one action of the autonomous device based on the optimized joint behaviour. The method outputs a control signal for controlling the determined at least one action