Semantic enhanced navigation among movable obstacles in the home environment

Abstract

This work explored the requirements of accurately and reliably predicting user intention using a deep learning methodology when performing fine-grained movements of the human hand. The focus was on combining a feature engineering process with the effective capability of deep learning to further identify salient characteristics from a biological input signal. 3 time domain features (root mean square, waveform length, and slope sign changes) were extracted from the surface electromyography (sEMG) signal of 17 hand and wrist movements performed by 40 subjects. The feature data was mapped to 6 sensor bend resistance readings from a CyberGlove II system, representing the associated hand kinematic data. These sensors were located at specific joints of interest on the human hand (the thumb's metacarpophalangeal joint, the proximal interphalangeal joint of each finger, and the radiocarpal joint of the wrist). All datasets were taken from database 2 of the NinaPro online database repository. A 3-layer long short-term memory model with dropout was developed to predict the 6 glove sensor readings using a corresponding sEMG feature vector as input. Initial results from trials using test data from the 40 subjects produce an average mean squared error of 0.176. This indicates a viable pathway to follow for this prediction method of hand movement data, although further work is needed to optimize the model and to analyze the data with a more detailed set of metrics.

A Review on Path Planning Algorithms for Mobile Robots

“…Other researchers focus on configuration control and obstacle bypass problems for multi-agent systems in an environment with unknown hindrances [14]. Several others consider the transferred obstacles and navigation of robots [15].…”

Semantic Path Planning for Indoor Navigation and Household Tasks

“…This paper is an invited extension of an extended abstract presented at the UK-RAS 2019 [7]. The remainder of this paper is structured as follows: Section 2 gives an overview of the related work in navigation and semantic mapping.…”