# Real-time human-robot interaction and service provision using hybrid intelligent computing framework

## TL;DR

This paper introduces a new computing model to improve robots' ability to interact with humans and provide efficient, real-time services.

## Contribution

The novel Hybrid Intelligent Computing Model (HICM) enhances robotic query processing with improved speed and accuracy.

## Key findings

- HICM reduced calculation time by 8.67%, service time by 15.09%, and failure rates by 7.87%.
- The model outperformed existing systems with a 11.8% higher success factor and 14.88% higher matching ratio.
- HICM demonstrated greater reliability and efficiency in real-time robotic service delivery.

## Abstract

Human-robot interaction has gained significant attention in various domains, including healthcare, customer service, and industrial automation. High computational cost, inefficient service matching, and elevated failure rates in dynamic service contexts are some primary disadvantages of existing query-processing systems. This research introduces a Hybrid Intelligent Computing Model (HICM) to improve robots’ ability to process inquiries autonomously. The goal is to make robots better at responding to human questions in real time with efficient, personalized, and context-specific solutions. Using self-organized computing approaches, robotic agents can reliably provide end-users with services suited to their demands. Due to their autonomous nature, robots must be able to calculate quickly and accurately to provide timely services. To meet these needs, the proposed HICM incorporates a sophisticated decision-support system to handle human questions and find the appropriate services. Within this decision-making framework, the model evaluates the characteristics and relevance of questions about accessible services by combining annealing and Tabu Search approaches. To avoid addressing queries incompatibly, the Tabu Search technique approaches query resolution as a non-convergent optimization issue. Comparing HICM’s performance to other models reveals significant improvements over CDS, DGTA, and CCS. In particular, HICM reduced calculation time by 8.67%, service time by 15.09%, and failure rates by 7.87%. In terms of important metrics, HICM fared better than the competing models. Its success factor was 11.8% higher, its matching ratio was 14.88% higher, and its failure rates were 6.22% lower. These findings demonstrate the model’s efficiency and reliability in terms of robotic query processing and real-time service delivery.