Guided Navigation from Multiple Viewpoints using Qualitative Spatial Reasoning

TL;DR

This paper proposes a method for guiding sensory-deprived agents using high-level, qualitative commands derived from multiple autonomous agents' viewpoints, enabling navigation without detailed perception or motion models.

Contribution

It introduces algorithms that synthesize high-level guidance commands from multi-agent qualitative spatial reasoning for sensory-deprived navigation.

Findings

Algorithms successfully generate guidance commands from multi-viewpoint data.

Guided navigation achieved with limited perception and high-level commands.

Effective for autonomous agents with minimal sensory input.

Abstract

Navigation is an essential ability for mobile agents to be completely autonomous and able to perform complex actions. However, the problem of navigation for agents with limited (or no) perception of the world, or devoid of a fully defined motion model, has received little attention from research in AI and Robotics. One way to tackle this problem is to use guided navigation, in which other autonomous agents, endowed with perception, can combine their distinct viewpoints to infer the localisation and the appropriate commands to guide a sensory deprived agent through a particular path. Due to the limited knowledge about the physical and perceptual characteristics of the guided agent, this task should be conducted on a level of abstraction allowing the use of a generic motion model, and high-level commands, that can be applied by any type of autonomous agents, including humans. The main task considered in this work is, given a group of autonomous agents perceiving their common environment with their independent, egocentric and local vision sensors, the development and evaluation of algorithms capable of producing a set of high-level commands (involving qualitative directions: e.g. move left, go straight ahead) capable of guiding a sensory deprived robot to a goal location.