Distributed Robot Localization via Barycentric Coordinates

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Solution Overview

Problem

Existing localization algorithms for mobile robots in large networks face challenges such as high computational complexity, reliance on GPS, and inaccuracies due to wheel slippage errors, especially in indoor environments without prior maps or beacons, and are inefficient in dynamic scenarios with limited communication.

Innovation Solution

A distributed algorithm that uses barycentric coordinates and convex hulls to iteratively update robot locations based on relative information from neighboring agents, ensuring convergence regardless of initial conditions, even in scenarios with limited or no proximity to beacons, and allowing for linear updates within a triangulation set.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If centralized localization algorithms are used, then reliability is improved, but device complexity and computational complexity increase

Engineering Contradiction:

Improvelocalization reliabilityVSAvoidalgorithm complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The centralized localization problem is segmented into multiple distributed localization tasks, where each agent independently performs localization using local information from neighboring agents. This distributes the computational burden across the network while maintaining localization accuracy through iterative information exchange.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

Each agent performs its own localization independently using local measurements and information from neighbors, without requiring a central coordinator. The system achieves collective localization through self-organized distributed computation, reducing central processing requirements.

Inventive Principle:

Principle #25Self-service

2Device complexity

If distributed localization algorithms are used, then device complexity is reduced, but measurement precision deteriorates due to noisy ranging data

Engineering Contradiction:

Improvealgorithm complexityVSAvoidlocation estimation accuracy

Core Design Contradiction:

Device complexityVSMeasurement precision

Solution Approach 1:

The algorithm incorporates feedback mechanisms where agents continuously exchange location estimates and measurement information with neighbors. This iterative feedback process allows the network to progressively refine location estimates and compensate for measurement noise through collective information processing.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

Multiple noisy measurements from different agents are merged through the distributed optimization process. By combining information from multiple sources and using consensus-based updates, the system reduces the impact of individual measurement errors and achieves more precise collective localization.

Inventive Principle:

Principle #5Merging (Combining)

3Measurement precision

If GPS is used for localization, then measurement precision is improved, but adaptability deteriorates in indoor environments without GPS signals

Engineering Contradiction:

Improvelocation accuracyVSAvoidenvironment adaptability

Core Design Contradiction:

Measurement precisionVSAdaptability or versatility

Solution Approach 1:

The distributed localization algorithm serves as a universal solution that works across multiple environments including indoor and outdoor settings. By using relative position measurements between agents rather than GPS-dependent absolute positioning, the system achieves environment-agnostic localization capability.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

Relative position measurements between agents serve as intermediaries to establish location information without direct GPS dependency. The network of agents acts as a mediator system that transforms local relative measurements into accurate absolute position estimates through distributed computation.

Inventive Principle:

Principle #24Intermediary (Mediator)

4Ease of operation

If dead-reckoning is used, then ease of operation is improved, but measurement precision deteriorates due to accumulating wheel slippage errors

Engineering Contradiction:

Improveimplementation simplicityVSAvoidlocation accuracy

Core Design Contradiction:

Ease of operationVSMeasurement precision

Solution Approach 1:

The system uses feedback from neighboring agents to continuously correct accumulated dead-reckoning errors. By comparing relative position estimates with neighbors and iteratively adjusting location estimates, the network compensates for drift without complicating individual agent operations.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS11536797B2Mobile network localization

Publication Date: 2022.12.27 TRUSTEES OF TUFTS COLLEGE

AI summary

A method localizes a first agent in a network including a number of agents, the number of agents including a number of mobile agents and one or more beacons located at known locations. The method includes performing a procedure including, receiving transmissions from a number of neighboring agents, processing the transmissions to determine information related to a relative location of the first agent and each neighboring agent of the number of neighboring agents, determining, based on the information related to the relative location of the first agent and each neighboring agent, that the first agent is within one or more proximity regions, and updating an estimated location of the first agent based on the information related to a relative location of the first agent and each neighboring agent.