Leveraging Machine Learning for Generating and Utilizing Motion Primitives in Adversarial Environments

IF 1.3 4区 工程技术 Q2 ENGINEERING, AEROSPACE Journal of Aerospace Information Systems Pub Date : 2023-10-26 DOI:10.2514/1.i011283
Zachary C. Goddard, Rithesh Rajasekar, Madhumita Mocharla, Garrett Manaster, Kyle Williams, Anirban Mazumdar
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Abstract

Motion primitives enable fast planning for complex and dynamic environments. Adversarial environments pose a particularly challenging and unpredictable scenario. Motion-primitive-based planners have the potential to provide benefit in these types of environments. The key challenge is to design a library of maneuvers that effectively capture the necessary capabilities of the vehicle. This work presents a primitive-based game tree search to solve adversarial games in continuous state and action spaces and applies a reinforcement learning framework to autonomously generate effective primitives for the given task. The results demonstrate the ability of the learning framework to produce maneuvers necessary for competing against adversaries. Furthermore, we propose a method for learning a model to estimate the state-dependent value of each motion primitives and demonstrate how to incorporate this model to increase planning performance under time constraints. Additionally, we compare our primitive-based algorithm against forward simulated methods from existing literature and highlight the benefits of motion primitives.
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利用机器学习在对抗环境中生成和利用运动原语
运动原语支持对复杂和动态环境进行快速规划。对抗性环境构成了一个特别具有挑战性和不可预测的场景。基于运动原始的规划器有可能在这些类型的环境中提供好处。关键的挑战是设计一个机动库,有效地捕捉车辆的必要能力。这项工作提出了一个基于原语的游戏树搜索来解决连续状态和动作空间中的对抗游戏,并应用强化学习框架为给定任务自主生成有效的原语。结果表明,学习框架能够产生与对手竞争所需的机动。此外,我们提出了一种学习模型的方法来估计每个运动原语的状态依赖值,并演示了如何结合该模型来提高时间约束下的规划性能。此外,我们将基于原语的算法与现有文献中的正向模拟方法进行了比较,并强调了运动原语的优点。
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来源期刊
CiteScore
3.70
自引率
13.30%
发文量
58
审稿时长
>12 weeks
期刊介绍: This Journal is devoted to the dissemination of original archival research papers describing new theoretical developments, novel applications, and case studies regarding advances in aerospace computing, information, and networks and communication systems that address aerospace-specific issues. Issues related to signal processing, electromagnetics, antenna theory, and the basic networking hardware transmission technologies of a network are not within the scope of this journal. Topics include aerospace systems and software engineering; verification and validation of embedded systems; the field known as ‘big data,’ data analytics, machine learning, and knowledge management for aerospace systems; human-automation interaction and systems health management for aerospace systems. Applications of autonomous systems, systems engineering principles, and safety and mission assurance are of particular interest. The Journal also features Technical Notes that discuss particular technical innovations or applications in the topics described above. Papers are also sought that rigorously review the results of recent research developments. In addition to original research papers and reviews, the journal publishes articles that review books, conferences, social media, and new educational modes applicable to the scope of the Journal.
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