Swarm Control Algorithm for Mesh Connectivity and Areal Coverage
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Solution Overview
Problem
Existing approaches to swarm robotics struggle to maintain optimal area coverage while ensuring connectivity among agents in decentralized multi-agent systems, especially in unknown and dynamically changing environments, where existing wireless communication infrastructure may be unavailable.
Innovation Solution
The Areal Coverage Optimising Movement Scheme (ACOMS) method, which involves identifying neighboring agents, estimating distances and directions, and using an activation function to determine movement magnitudes, allows agents to dynamically adjust their positions to maintain mesh network connectivity and optimize area coverage without prior knowledge of the environment or optimal agent locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If agents are distributed over large areas to achieve optimal area coverage, then area coverage is improved, but communication connectivity between agents deteriorates
Solution Approach 1:
The patent implements dynamic position adjustment where agents continuously monitor their distances to neighboring agents and automatically relocate when communication thresholds are exceeded. This dynamic behavior allows the system to adapt to changing environmental conditions and maintain connectivity while maximizing area coverage over time.
Solution Approach 2:
The system employs feedback mechanisms where each agent measures distances to neighboring agents using sensor data, compares these distances against communication threshold values, and adjusts positions accordingly. This closed-loop control ensures that area coverage optimization does not compromise communication connectivity.
2Reliability
If agents are positioned closely together to maintain communication connectivity, then communication reliability is improved, but area coverage capability deteriorates
Solution Approach 1:
Agents dynamically adjust their positions based on real-time distance measurements and communication threshold evaluations. This allows the swarm to expand coverage area while maintaining connectivity by only moving agents when necessary to preserve communication links.
Solution Approach 2:
The system divides the total area coverage task among multiple independent agents, each responsible for monitoring and maintaining connectivity within its local neighborhood. This segmentation allows the swarm to achieve extensive overall coverage while each agent maintains close proximity to its neighbors for reliable communication.
3Manufacturing precision
If a centralized control system is used to coordinate agent positions, then optimal positioning can be achieved, but system robustness and scalability deteriorate
Solution Approach 1:
Each agent independently performs distance measurements, threshold comparisons, and position adjustment decisions without requiring centralized coordination. This self-service approach eliminates single points of failure and enables the system to scale to large numbers of agents while maintaining positioning accuracy through local autonomous decision-making.
Solution Approach 2:
The system uses adjustable communication threshold parameters that can be modified based on environmental conditions and mission requirements. This parameter adaptability allows the decentralized system to achieve positioning accuracy comparable to centralized systems while maintaining robustness and scalability.
4Reliability
If pre-existing wireless communication infrastructure is used, then communication reliability is improved, but adaptability to environments without infrastructure deteriorates
Solution Approach 1:
The agent positioning system is designed to function universally across different environmental conditions, whether pre-existing wireless infrastructure is available or not. The same distance measurement and threshold-based control mechanism operates in both infrastructure-rich and infrastructure-less environments, making the system highly adaptable.
Solution Approach 2:
Agents autonomously establish and maintain communication links using their own sensors and processors without relying on external infrastructure. This self-sufficient approach ensures operation in remote or disaster-struck areas while maintaining reliability through distributed decision-making.
Data Source
AI summary
A method for controlling the position of an updating agent in a decentralised multi-agent system, the method comprising: identifying a first neighbouring agent within a communicative range of the updating agent; estimating a first distance to the first neighbouring agent and a first direction to the first neighbouring agent; determining a movement direction based on the first direction to the first neighbouring agent; and determining a movement magnitude based on an activation function, the first distance to the first neighbouring agent, and a desired reference distance. The activation function is configured such that the greater the difference between the first distance to the neighbouring agent and the desired reference difference, the larger the movement magnitude. The method further comprises moving the updating agent based on the movement direction and the movement magnitude.