Multi-Robot Particle Filter Positioning With Low-Bandwidth Updates
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Solution Overview
Problem
Current multi-robot positioning methods face challenges in accurately estimating robot poses due to odometry errors, high communication bandwidth requirements, and computational complexity, especially when fusing odometry with environment measurements, and often fail to provide performance guarantees.
Innovation Solution
A probabilistic multi-robot positioning system that uses a particle filter to estimate robot poses by exchanging particles upon rendezvous, reducing the need for raw sensor measurements storage and communication bandwidth, and employing random pairing of particles to maintain computational efficiency and performance guarantees.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If robots exchange all measurements (odometry and laser scans) between rendezvous, then positioning accuracy is improved, but communication bandwidth requirement increases excessively
Solution Approach 1:
The patent extracts only the essential information (particles representing pose distributions) from the complete sensor measurements, transmitting only these compressed representations between robots during rendezvous. This extraction principle reduces communication bandwidth while preserving positioning accuracy by sending minimal sufficient data rather than all raw measurements.
Solution Approach 2:
Instead of transmitting original sensor measurements, the system creates simplified copies in the form of particles that represent the essential pose distribution information. These particle copies maintain the necessary probabilistic information for accurate positioning while occupying minimal communication bandwidth.
2Measurement precision
If density trees are used to estimate robot pose from particles, then positioning accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent replaces complex density tree structures with simpler, computationally inexpensive particle representations. These lightweight particles serve as disposable approximations that provide sufficient accuracy for pose estimation without the high computational cost of maintaining and querying dense probability trees.
Solution Approach 2:
The system changes the representation parameters from dense continuous probability distributions (density trees) to discrete particle samples. This parameter transformation reduces computational complexity while maintaining the ability to represent and update pose distributions accurately through standard particle filter operations.
3Measurement precision
If all particle pairs are evaluated for weight computation, then positioning accuracy is improved, but computational complexity increases to O(K2)
Solution Approach 1:
Instead of evaluating all K2 particle pairs, the system performs partial action by selectively evaluating only necessary particle combinations during rendezvous. This partial evaluation maintains sufficient accuracy for probabilistic pose estimation while reducing computational complexity from quadratic to linear or near-linear scale.
Solution Approach 2:
The patent segments the particle evaluation process into manageable portions, evaluating particle weights in a structured sequence rather than exhaustively. This segmentation allows the system to process particle pairs efficiently by dividing the computation into smaller, parallelizable tasks that scale better with particle count.
Data Source
AI summary
A system for estimating a pose of a robot includes a particle filter to track the pose of the robot using particles that defining a probability of pose of the robot and a particle tuner to update the particles of the robot based on particles of neighboring robot. Upon receiving data indicative of relative pose between a pose of the robot and a pose of a neighboring robot, and particles of the neighboring robot, the particle tuner pairs an arbitrarily sampled particle of the robot with an arbitrarily sampled particle of the neighboring robot, determines a weight of the paired particles in reverse proportion to an error between a relative pose defined by the paired particles and the relative pose between the robot and the neighboring robot, and updates the particles of the robot in accordance to the weights of corresponding paired particles.