Mobile Robot Wireless Mesh Coverage Optimization
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Solution Overview
Problem
In infrastructure-less environments, maintaining full network connectivity for mobile robotics systems in logistics and warehousing is challenging due to the lack of global communication infrastructure, requiring self-deployment and adaptation without centralized coordination.
Innovation Solution
A method and system utilizing a wireless mesh network of mobile robots with optical sensors, microprocessors, and wireless communication modules that perform angle-based movement to optimize coverage and connectivity by selecting landmarks and adjusting positions based on signal strength and distance to ensure robust connectivity and coverage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a wireless mesh network of mobile robots is deployed without centralized infrastructure, then flexibility and adaptability in ad-hoc environments is improved, but maintaining full network connectivity and coverage becomes more difficult
Solution Approach 1:
The patent implements a feedback mechanism where robots continuously monitor network coverage conditions and signal strength. When a robot detects that coverage conditions are not met (e.g., signal strength below threshold), it triggers a coverage movement action. This closed-loop feedback system ensures the network maintains connectivity while adapting to environmental changes and robot movements.
Solution Approach 2:
The patent enables robots to autonomously perform coverage optimization without centralized coordination. Each robot independently evaluates its local coverage conditions, selects appropriate movement actions based on predefined policies, and executes movements to maintain network connectivity. This self-service capability allows the system to adapt flexibly to ad-hoc environments while maintaining reliable connectivity through distributed decision-making.
2Reliability
If robots perform coverage movement to optimize wireless coverage, then network coverage and connectivity are improved, but robot productivity and task execution efficiency may deteriorate
Solution Approach 1:
The patent implements dynamic coverage movement policies that adapt to robot operational states. When a robot is actively engaged in material handling tasks, the system dynamically adjusts or suspends coverage movements to prioritize task execution. Coverage optimization is performed during idle periods or when task priorities allow, ensuring network coverage is maintained without significantly impacting productivity.
Solution Approach 2:
The patent applies partial coverage movement actions rather than complete repositioning. Robots perform minimal necessary movements to just meet coverage thresholds, rather than optimizing coverage maximally. This partial action approach maintains adequate network connectivity while minimizing disruption to task execution and preserving productivity.
3Ease of operation
If robots autonomously select landmarks and perform angle-based movement, then ease of operation and self-deployment capability are improved, but device complexity and control algorithms increase
Solution Approach 1:
The patent transforms the complex coverage optimization problem into simpler parameter-based decisions. Robots use signal strength thresholds, angle ranges, and distance parameters to determine movement actions instead of implementing complex global optimization algorithms. By changing the decision parameters from global network state to local measurable quantities, the system achieves self-deployment capability while keeping individual robot complexity manageable.
Solution Approach 2:
The patent segments the coverage optimization problem into independent local decisions for each robot. Instead of requiring centralized coordination or complex inter-robot communication, each robot independently evaluates its local coverage conditions and executes movements based on simple rules. This segmentation reduces overall system complexity while maintaining self-deployment capability through distributed autonomous operation.
Data Source
AI summary
A method for optimizing coverage of a wireless network of a plurality of mobile robots in an environment in which each robot includes an optical sensor module, a microprocessor, and a wireless communication module. The method includes: receiving, by a first robot in the network, signals from a second robot in the network; determining, by the first robot based on the signals, that the first robot or second robot do not fulfil a network coverage condition; selecting, by the first robot, at least two landmarks in the environment; and performing, by the first robot, a movement based on an angle between the two landmarks with respect to the first robot, to improve the network coverage condition.