Semantic Utility Modeling for Dynamic Multi-Robot Task Allocation
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Solution Overview
Problem
Conventional multi-robot task allocation systems fail to accurately and dynamically calculate utility values, especially in scenarios with heterogeneous robots and dynamic changes, leading to inefficiencies in task assignment and automation, particularly in environments where real-time decision-making is required.
Innovation Solution
A processor-implemented method and system that utilize a structured semantic knowledge model to dynamically compute utility for robot-task pairs by considering interdependencies between parameters, including travel time, actuation time, and recharge time, using a Web Ontology Language (OWL) ontology with Resource Description Framework (RDF) for semantic interpretation and automation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional multi-robot task allocation systems are used, then task assignment can be performed, but utility calculation is inaccurate and not dynamic, leading to inefficiencies in task assignment
Solution Approach 1:
The system dynamically computes utility values for robot-task pairs by considering real-time parameters such as travel time, actuation time, and recharge time. This dynamic calculation allows the task allocation system to adapt to changing conditions and robot states, improving both accuracy and efficiency of task assignment.
Solution Approach 2:
The system calculates utility based on multiple parameters including travel time (which depends on free motion time and turning time), actuation time, and recharge time. By changing and considering multiple parameters simultaneously, the system achieves accurate and dynamic utility calculation for optimal task allocation.
2Speed
If real-time decision-making is required in dynamic environments, then task allocation responsiveness is improved, but calculation complexity increases
Solution Approach 1:
The system segments the utility calculation into distinct components: travel time calculation (with free motion time and turning time), actuation time, and recharge time. This segmentation allows for efficient computation of each parameter independently and their subsequent combination, enabling real-time decision-making without excessive complexity.
Solution Approach 2:
The system autonomously computes utility values and performs task allocation without human intervention. The automated calculation of multiple parameters and their interdependencies enables real-time decision-making while the system self-manages the computational complexity.
3Adaptability or versatility
If heterogeneous robots are considered in task allocation, then system versatility is improved, but parameter interdependency management becomes more difficult
Solution Approach 1:
The system provides a universal framework for task allocation that works with heterogeneous robots of different types and capabilities. By defining a common utility calculation approach that adapts to each robot's specific parameters (travel time, actuation time, recharge time), the system achieves versatility across diverse robot types while managing parameter complexity through a unified methodology.
Data Source
AI summary
Parameters specific to robot, environment, target objects and their inter-relations need to be considered by a robot to estimate cost of a task. As the existing task allocation methods assume a single utility value for a robot-task pair, combining heterogeneous parameters is a challenge. In applications like search and rescue, manual intervention may not be possible in real time. For such cases, utility calculation may be a hindrance towards automation. Also, manufacturers follow their own nomenclature and units for robotic specifications. Only domain experts can identify semantically similar terms and perform necessary conversions. Systems and methods of the present disclosure provide a structured semantic knowledge model to store and describe data in a uniform machine readable format such that semantics of those data can be interpreted by the robots and utility computation can be autonomous to make task allocation autonomous, semantic enabled and capable of self-decision without human intervention.