Mobile Telerobot Path Sensing With Virtual RSS Source Prediction
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Solution Overview
Problem
Existing methods for predicting radio signal strength (RSS) in dynamic environments for mobile telerobots are inefficient for real-time applications, require prior knowledge of radio source locations, and are computationally complex, leading to connectivity issues and increased navigation time.
Innovation Solution
A radio-source agnostic online RSS prediction algorithm using a novel concept of virtual radio source, which dynamically localizes the virtual source by estimating RSS at future unvisited locations using an underlying path loss model, allowing for real-time prediction without initial training or dependency on physical radio sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If offline prediction systems are used for RSS prediction, then prediction accuracy is improved, but real-time performance deteriorates
Solution Approach 1:
The patent transitions from static offline prediction to dynamic online prediction by continuously updating the RSS prediction model as the telerobot moves. The system dynamically adapts to changing environmental conditions and maintains prediction accuracy in real-time by incorporating new measurements and updating the path loss model parameters on-the-fly.
Solution Approach 2:
The patent performs preliminary RSS predictions at future candidate locations before the telerobot actually reaches them. By predicting RSS values at potential destination points in advance, the system enables the remote operator to select optimal paths before committing to navigation, thus achieving real-time decision-making with accurate predictions.
2Measurement precision
If linear regression or Gaussian Random Process models are used for online RSS prediction, then prediction accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent employs a simplified path loss model that uses basic geometric relationships and minimal parameters instead of complex machine learning models. This lightweight approach uses simple distance-based calculations and basic signal propagation assumptions, making it computationally inexpensive and suitable for deployment on resource-constrained telerobot platforms while maintaining adequate prediction accuracy.
3Reliability
If additional radio sources are deployed to plan optimal paths, then connectivity is improved, but dependency on physical environment increases
Solution Approach 1:
The patent creates a virtual copy of the radio source by estimating the position of the access point based on RSS measurements taken at multiple locations. Instead of requiring additional physical radio sources, the system constructs a virtual model of the radio propagation environment that can be used for prediction, thereby reducing environmental dependency while maintaining connectivity reliability.
4Measurement precision
If initial training phase is conducted for RSS prediction, then prediction accuracy is improved, but time to reach destination increases
Solution Approach 1:
The patent implements a self-training mechanism where the RSS prediction model continuously learns and adapts during the telerobot's navigation task itself. As the telerobot collects new RSS measurements while moving through the environment, the system automatically updates its path loss model parameters without requiring separate training phases, thus eliminating time loss while maintaining improving prediction accuracy.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The algorithm enables the remote operator to choose the best-connected path for the mobile telerobot by predicting RSS at future points, reducing the risk of losing connectivity and minimizing navigation time, while being computationally efficient for deployment on telerobots and IoT devices.
Implementation Method 1
spatial variations of Received Radio Signal Strength (RSS) in such a dynamic environment depends on a combination of various effects like path loss, shadowing, and multipath fading
Implementation Method 2
spatial variations of Received Radio Signal Strength (RSS) in such a dynamic environment depends on a combination of various effects like path loss, shadowing, and multipath fading
Implementation Method 3
spatial variations of Received Radio Signal Strength (RSS) in such a dynamic environment depends on a combination of various effects like path loss, shadowing, and multipath fading
Data Source
AI summary
This disclosure relates generally to a method and system for sensing best-connected future path for a mobile telerobot based on radio signal strength (RSS) prediction algorithm through in-situ radio-sensing. State-of-the-art methods predict the future path from the plurality of possible paths based on a radio-source in the environment. However, prediction of the suitable future path in the absence of the radio-source or in no signal zone is not yet achieved. The proposed in-situ algorithm is based on Log-Normal Shadowing Model (LNSM) and found efficient for prediction error minimization. The method enables the mobile telerobot to predict the future path on a trajectory of the telerobot even without prior knowledge of a radio-source location. The mobile telerobot can predict the most suitable path from a plurality of possible paths for a move based on virtual location estimation.