Thanh Thao Ton Nu | Searcharxiv

arXiv2026

Bi-Layer Ant Colony Optimization for Multi-Robot Task Allocation and Routing in Delivery Applications

This paper addresses the multi-robot task allocation (MRTA) problem, which is essential for delivery and logistics applications. Our approach first defines a new cost function that transforms the MRTA into a unified optimization problem capturing both task assignment and routing. A bi-layer ant colony optimization (ACO) algorithm is then introduced, integrating two interdependent decision layers within a single colony process to solve the problem. This hierarchical framework enables simultaneous optimization of task allocation and route planning across multiple robots. Comparative experiments with mixed-integer linear programming (MILP) and particle swarm optimization (PSO) demonstrate that the proposed bi-layer ACO achieves the shortest total travel distance and fastest completion time across all task sizes. Specifically, it reduces total travel distance by up to 17.7% and completion time by nearly 20% compared with baseline methods. These results confirm the efficiency, scalability, and reliability of the proposed bi-layer ACO for multi-robot delivery tasks.

cs.RO↗

arXiv2025

Real-time Recognition of Human Interactions from a Single RGB-D Camera for Socially-Aware Robot Navigation

{Recognizing human interactions is essential for social robots as it enables them to navigate safely and naturally in shared environments. Conventional robotic systems however often focus on obstacle avoidance, neglecting social cues necessary for seamless human-robot interaction. To address this gap, we propose a framework to recognize human group interactions for socially aware navigation. Our method utilizes color and depth frames from a monocular RGB-D camera to estimate 3D human keypoints and positions. Principal component analysis (PCA) is then used to determine dominant interaction directions. The shoelace formula is finally applied to compute interest points and engagement areas. Extensive experiments have been conducted to evaluate the validity of the proposed method. The results show that our method is capable of recognizing group interactions across different scenarios with varying numbers of individuals. It also achieves high-speed performance, processing each frame in approximately 4 ms on a single-board computer used in robotic systems. The method is implemented as a ROS 2 package making it simple to integrate into existing navigation systems. Source code is available at https://github.com/thanhlong103/social-interaction-detector

cs.RO↗