Human-Robot Collaborative Minimum Time Search Through Sub-Priors in Ant Colony Optimization

• Published in: IEEE robotics and automation letters Vol. 9; no. 11; pp. 10216 - 10223
• Main Authors: Gil, Oscar, Sanfeliu, Alberto
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.11.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Ant colony optimization; Artificial intelligence; Artificial neural networks; Collaboration; Computational modeling; Deep learning; deep learning methods; Design optimization; Heuristic methods; Human engineering; Human-robot collaboration (HRC); Human-robot interaction; Image analysis; Metaheuristics; Mobile robots; Multiagent systems; Optimization; Path planning; path planning for multiple mobile robots or agents; Probabilistic logic; Rescue robots; Robot sensing systems; Robots; search and rescue robots; Searching; semantic scene understanding; Tablet computers
• ISSN: 2377-3766; 2377-3766
• DOI: 10.1109/LRA.2024.3471451

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 letter 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.