ROSTA: RObotic Sequential Task Assignment Framework for Multi-Robot Collaboration in Dynamic and Uncertain Construction Environments

Efficient task allocation is critical in multi-construction robot collaboration to enhance productivity and maximize task success rates. Robots generally exhibit partial skill proficiency due to the lack of generalist and error-free control systems. For instance, robots are typically programmed with skills in specific activities and may face uncertainties in execution success rates. In such scenarios, multiple robots need to coordinate sequential activities to complete construction tasks. Optimizing task allocation becomes crucial for ensuring high-quality task assignments and a high success rate in robot execution. This research presents a dynamic programming-based framework for sequential task allocation, maximizing the compatibility between task features and robot skills. Thirty-six tasks are annotated with features such as being heavy, dexterous, or requiring careful handling. These features were robot capabilities and estimated success rate of robots handling such tasks. The framework addresses both deterministic scenarios, where success rates are fixed, and uncertain cases, where success rates are modeled as ranges and optimized using Monte Carlo simulations, extracted from the TEACH dataset. Initial evaluations demonstrate significant improvements in task efficiency and success rates when compared to conventional allocation methods. By combining robotics with advanced optimization techniques, this work contributes to the development of intelligent and adaptive systems capable of solving complex task scheduling challenges in collaborative multi-robot environments.