BIO-INSPIRED AUTONOMOUS ROBOTICS AND HUMAN–ROBOT COLLABORATIVE INTELLIGENCE: ADVANCING ADAPTIVE INTERACTION IN INTELLIGENT SYSTEMS

Abstract

This work explored autonomous robotics and human-robot collaborative intelligence inspired by biological systems to advance adaptive interaction within intelligent systems. The use of a quantitative, descriptive-correlational design enabled the assessment of respondents' perceptions across robotics, artificial intelligence, engineering, automation, computer science, and intelligent systems. A purposive sampling technique was used, yielding 150 respondents. A structured questionnaire was used to collect data, which was analysed using means, standard deviations, Pearson correlation, and regression. The results showed a very high level of agreement concerning bio-inspired autonomous robotics (mean = 4.28, SD = 0.73). The mean rating for human–robot collaborative intelligence was also very high (mean = 4.33, SD = 0.70). The mean score for AI integration was 4.33 (SD = 0.71), and adaptive interaction had the highest mean score at 4.34 (SD = 0.69). The correlation analysis showed positive correlations between adaptive interaction and autonomous robotics inspired by nature (r = 0.64), human–robot collaborative intelligence (r = 0.71) and integration of artificial intelligence (r = 0.69). Adaptive interaction was predicted by the predictors and accounted for 61% of the variance (adjusted R² = .59). The most powerful predictors were human–robot collaborative intelligence (β = 0.39), artificial intelligence integration (β = 0.34), and bio-inspired autonomous robotics (β = 0.28). The results indicate that implementing biological inspiration, artificial intelligence, and collaborative intelligence can improve the effectiveness of adaptive robotic systems.

Keywords : Adaptive interaction, Artificial intelligence, Autonomous robotics, Bio-inspired robotics, Collaborative intelligence, Human–robot interaction.