Network Node Backup Planning for Dynamic Multi-Robot Coordination
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Solution Overview
Problem
Current multi-robot coordination techniques in Industry 4.0 environments are inadequate for dynamic and complex scenarios due to limited on-board processing capacities, autonomous operation, and varying communication and energy constraints, requiring a more automated and efficient method for task planning, path planning, and communication channel management.
Innovation Solution
A network node that generates initial plans and backup plans using machine learning models to account for various events and constraints, enabling dynamic reconfiguration and robust task execution, incorporating AI planning and scheduling to handle path planning, task allocation, and communication models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static optimization rules and centralized monitoring models are used for multi-robot coordination, then task completion can be achieved with simple deployment, but the system cannot dynamically reconfigure or handle the scale of complex environments
Solution Approach 1:
The patent implements dynamic coordination rules that allow the multi-robot system to adapt its behavior in real-time based on changing environmental conditions, task requirements, and robot states. The system transitions from static optimization rules to dynamic rule-based coordination, enabling robots to autonomously adjust their actions without centralized reconfiguration.
Solution Approach 2:
Each robot is equipped with local coordination rules that enable autonomous decision-making and self-organization. Robots can independently handle task allocation, path planning, and conflict resolution based on pre-defined coordination rules, eliminating the need for centralized monitoring and reducing system complexity.
2Productivity
If centralized optimizer is employed to schedule coordination policies, then initial task allocation can be achieved, but the system cannot handle increasing complexity and dynamism of future multi-robot deployments
Solution Approach 1:
The coordination system is segmented into distributed coordination rules executed by each robot rather than a single centralized optimizer. This segmentation allows the system to handle complexity by distributing decision-making across multiple autonomous agents, each following local rules for task allocation, path planning, and collision avoidance.
Solution Approach 2:
Coordination rules are pre-configured and embedded in each robot before deployment. These pre-defined rules cover various coordination scenarios including task allocation, path planning, conflict resolution, and failure handling, enabling robots to autonomously handle complex situations without real-time centralized optimization.
3Adaptability or versatility
If static deployment techniques are used, then simple coordination can be achieved, but the techniques are ill suited for large, complex, multi-robot deployments
Solution Approach 1:
The coordination rules are designed to be universal and applicable to large numbers of heterogeneous robots. The same set of coordination rules can be deployed across the entire multi-robot system, handling various tasks including exploration, surveillance, search and rescue, and coordinated manipulation, making the system scalable without increasing operational complexity.
4Productivity
If concurrent actions are prevented to aid sequential task completion, then task execution can be simplified, but task completion time increases
Solution Approach 1:
The coordination rules incorporate feedback mechanisms that monitor robot positions, task progress, and environmental conditions in real-time. Based on this feedback, the system dynamically adjusts coordination strategies to allow concurrent actions when safe and beneficial, while preventing conflicts, thus improving task completion speed without excessive complexity.
Data Source
AI summary
A method performed by a network node for handling one or more operations in a communications network comprising a plurality of computing devices performing one or more tasks. The network node obtains initial parameters relating to the plurality of computing devices, environment and the communications network; and generates a plan by taking one or more operation goals involving the plurality of computing devices into account as well as the obtained initial parameters, wherein the generated plan relates to operation of the plurality of computing devices. The network node further computes a number of back-up plans, wherein the number of back-up plans are taking one or more events into account wherein the one or more events relate to operation of the plurality of computing devices; and executes one or more operations using the generated plan, and in case the one or more events occur, using a computed back-up plan.