Human-Robot Collaborative Minimum Time Search through Sub-priors in Ant Colony Optimization

TL;DR

This paper introduces a novel multi-agent search algorithm combining CNN-based priors and an extended ACO method for efficient human-robot collaborative object search, validated through real experiments with a humanoid robot.

Contribution

It extends Ant Colony Optimization with sub-priors and integrates CNN-based object priors for improved human-robot collaborative search tasks.

Findings

Enhanced search efficiency with human-robot collaboration

Improved user perception without sacrificing performance

Successful real-world implementation with humanoid robot IVO

Abstract

Human-Robot Collaboration (HRC) has evolved into a highly promising issue owing to the latest breakthroughs in Artificial Intelligence (AI) and Human-Robot Interaction (HRI), among other reasons. This emerging growth increases the need to design multi-agent algorithms that can manage also human preferences. This paper presents an extension of the Ant Colony Optimization (ACO) meta-heuristic to solve the Minimum Time Search (MTS) task, in the case where humans and robots perform an object searching task together. The proposed model consists of two main blocks. The first one is a convolutional neural network (CNN) that provides the prior probabilities about where an object may be from a segmented image. The second one is the Sub-prior MTS-ACO algorithm (SP-MTS-ACO), which takes as inputs the prior probabilities and the particular search preferences of the agents in different sub-priors to generate search plans for all agents. The model has been tested in real experiments for the joint search of an object through a Vizanti web-based visualization in a tablet computer. The designed interface allows the communication between a human and our humanoid robot named IVO. The obtained results show an improvement in the search perception of the users without loss of efficiency.