Multi-Robot Pose Estimation with Particle Exchange at Rendezvous
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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 sensor measurements and relative pose information between robots.
Innovation Solution
A probabilistic multi-robot positioning system using a particle filter that allows robots to exchange particles upon rendezvous, reducing the need for raw sensor measurements storage and communication bandwidth, and employs random pairing of particles to reduce 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 increases excessively
Solution Approach 1:
The patent extracts only the essential pose information (particles representing robot pose and covariance matrices) from the complete sensor measurements, and exchanges only this extracted information between robots during rendezvous. This eliminates the need to transmit large volumes of raw odometry and laser scan data while preserving the critical positioning information needed for accurate multi-robot localization.
Solution Approach 2:
The patent performs preliminary processing of sensor measurements by each robot to compute pose particles and covariance matrices before the rendezvous occurs. This advance preparation transforms raw measurements into compact, exchangeable pose information, reducing the communication burden during actual rendezvous while maintaining positioning accuracy.
2Measurement precision
If robots exchange all measurements between rendezvous, then positioning accuracy is improved, but memory storage requirement increases
Solution Approach 1:
The patent extracts only the essential pose information (particles representing robot pose and covariance matrices) from the complete sensor measurements, and exchanges only this extracted information between robots during rendezvous. This eliminates the need to transmit large volumes of raw odometry and laser scan data while preserving the critical positioning information needed for accurate multi-robot localization.
Solution Approach 2:
The patent performs preliminary processing of sensor measurements by each robot to compute pose particles and covariance matrices before the rendezvous occurs. This advance preparation transforms raw measurements into compact, exchangeable pose information, reducing the communication burden during actual rendezvous while maintaining positioning accuracy.
3Measurement 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 the complex, computationally intensive density tree data structure with a simpler, more efficient particle representation that uses minimal memory and computational resources. This substitution maintains pose estimation accuracy while dramatically reducing the computational burden, making the system suitable for resource-constrained mobile robots.
Solution Approach 2:
The patent changes the representation parameters from dense occupancy grid maps and complex density trees to a sparse set of pose particles with covariance matrices. This parameter transformation reduces computational complexity from polynomial to linear time complexity while preserving the probabilistic nature of pose estimation.
4Measurement precision
If all pairs of particles from two robots are compared, then pose estimation accuracy is improved, but computational complexity becomes O(K^2)
Solution Approach 1:
The patent applies partial action by comparing only a subset of particle pairs rather than all possible pairs between two robots. By strategically selecting which particles to compare based on their weights and spatial distribution, the system achieves accurate pose estimation with significantly reduced computational complexity, avoiding the O(K^2) burden of exhaustive comparison.
Data Source
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AI summary
A system for estimating a pose of a robot, the pose including at least one of a location and orientation of the robot, includes a particle filter to track the pose of the robot using particles that defining a probability of a 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 a 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.