Snake-Inspired Mobile Robot Positioning with Hybrid Learning

TL;DR

This paper introduces MoRPINet, a hybrid learning framework inspired by snake movement, to improve inertial-based robot positioning, achieving significant error reduction in real-world navigation scenarios.

Contribution

The paper presents a novel snake-inspired neural network approach that enhances inertial navigation accuracy for mobile robots in environments with limited sensor data.

Findings

33% reduction in positioning error compared to state-of-the-art methods

Effective in real-world field experiments with inertial sensors

Demonstrates the benefit of snake-like maneuvers for nonlinear sensor data regression

Abstract

Mobile robots are used in various fields, from deliveries to search and rescue applications. Different types of sensors are mounted on the robot to provide accurate navigation and, thus, allow successful completion of its task. In real-world scenarios, due to environmental constraints, the robot frequently relies only on its inertial sensors. Therefore, due to noises and other error terms associated with the inertial readings, the navigation solution drifts in time. To mitigate the inertial solution drift, we propose the MoRPINet framework consisting of a neural network to regress the robot's travelled distance. To this end, we require the mobile robot to maneuver in a snake-like slithering motion to encourage nonlinear behavior. MoRPINet was evaluated using a dataset of 290 minutes of inertial recordings during field experiments and showed an improvement of 33% in the positioning error over other state-of-the-art methods for pure inertial navigation.