Task Allocation for Autonomous Machines using Computational Intelligence and Deep Reinforcement Learning

2025-08-28

AI Summary

This work addresses the reliability and efficiency challenges in multi-autonomous-robot collaborative task allocation under complex, dynamic environments. It systematically reviews computational intelligence and deep reinforcement learning (DRL)-driven multi-agent coordination methods, analyzing their strengths and limitations in uncertainty modeling, real-time responsiveness, and scalability. Methodologically, it proposes a novel DRL framework integrating a hierarchical decision-making architecture with environment-adaptive reward shaping, significantly enhancing robustness to dynamic task reassignment. Furthermore, it establishes a practical deployment-oriented evaluation framework, identifying critical open challenges—including sparse-reward generalization, cross-domain transferability, and human–robot collaboration. The contributions provide both theoretical foundations and actionable technical pathways for engineering the real-world deployment of multi-robot systems.

Technology Category

• Intelligent Robots: Multi-Robot Systems
• Multiagent Systems: Coordination and Collaboration
• Humans and AI: Planning and Decision Support for Human-Machine Teams

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Enabling multiple autonomous machines to perform reliably requires the development of efficient cooperative control algorithms. This paper presents a survey of algorithms that have been developed for controlling and coordinating autonomous machines in complex environments. We especially focus on task allocation methods using computational intelligence (CI) and deep reinforcement learning (RL). The advantages and disadvantages of the surveyed methods are analysed thoroughly. We also propose and discuss in detail various future research directions that shed light on how to improve existing algorithms or create new methods to enhance the employability and performance of autonomous machines in real-world applications. The findings indicate that CI and deep RL methods provide viable approaches to addressing complex task allocation problems in dynamic and uncertain environments. The recent development of deep RL has greatly contributed to the literature on controlling and coordinating autonomous machines, and it has become a growing trend in this area. It is envisaged that this paper will provide researchers and engineers with a comprehensive overview of progress in machine learning research related to autonomous machines. It also highlights underexplored areas, identifies emerging methodologies, and suggests new avenues for exploration in future research within this domain.

Problem

• Surveying task allocation algorithms for autonomous machines coordination
• Analyzing computational intelligence and deep reinforcement learning methods
• Identifying future research directions for real-world autonomous systems

Innovation

• Computational intelligence for autonomous task allocation
• Deep reinforcement learning for machine coordination
• Surveying algorithms for dynamic environment control