Multi-Robot Pose Estimation With Particle Exchange at Rendezvous
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Solution Overview
Problem
Current multi-robot positioning methods face challenges such as excessive communication bandwidth requirements, high computational complexity, and potential overconfidence in pose estimates due to frequent information fusion, leading to inaccurate and inefficient cooperative localization.
Innovation Solution
A probabilistic multi-robot positioning system using particle filters, where robots exchange particles upon rendezvous, reducing the need for raw sensor measurements storage and communication bandwidth, and employing random pairing to lower computational complexity while ensuring performance guarantees through Bayesian inference principles.
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 becomes excessive
Solution Approach 1:
The patent extracts only the essential information (particles representing pose probability 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 transmitting only the critical pose uncertainty information rather than all raw sensor data.
Solution Approach 2:
Instead of transmitting original sensor measurements, the patent uses particles as simplified copies that represent the essential pose distribution information. These particle representations serve as compressed substitutes that maintain the probabilistic pose information needed for accurate positioning while dramatically reducing data volume for communication.
2Measurement precision
If robots exchange all measurements between rendezvous, then positioning accuracy is improved, but memory storage requirement becomes excessive
Solution Approach 1:
The patent extracts only the essential pose distribution information into particle representations, storing only these compressed particle sets rather than all raw sensor measurements. This extraction reduces memory storage requirements while maintaining positioning accuracy by retaining only the critical probabilistic pose information.
Solution Approach 2:
The patent uses particles as lightweight, computationally inexpensive representations that can be easily created and discarded. These particle representations serve as temporary, low-cost substitutes for storing expensive raw sensor data, enabling accurate positioning with minimal memory storage requirements.
3Measurement precision
If robots perform dense pairing of particles for pose estimation, then positioning accuracy is improved, but computational complexity becomes excessive
Solution Approach 1:
The patent applies partial action by performing particle pairing only when robots are in proximity during rendezvous, rather than continuously. This partial application of the pairing operation reduces computational complexity while maintaining positioning accuracy by updating pose estimates only at necessary moments when information exchange is possible.
Solution Approach 2:
The patent changes the parameter of particle pairing from dense/comprehensive to sparse/selective by using random pairing strategies. This parameter change reduces computational complexity from O(K²) to O(K) while preserving the probabilistic guarantees of pose estimation through Bayesian inference principles.
Data Source
AI summary
A robot configured for multi-robot pose estimation execute iteratively the primary particle filter and the secondary particle filter independently from each other to update the pose estimations in the primary and the secondary particles. In response to each encounter of the robot with a neighboring robot, the robot receives neighboring particles of a particle filter of the neighboring robot identifying poses and likelihoods of the poses of the neighboring robot and replaces the primary particles of the primary particle filter with the secondary particles fused with the neighboring particles. The robot outputs its pose according to the primary particles of the primary particle filter.