Paper Reading AI Learner

Robust Online Epistemic Replanning of Multi-Robot Missions

2024-03-01 16:21:13
Lauren Bramblett, Branko Miloradovic, Patrick Sherman, Alessandro V. Papadopoulos, Nicola Bezzo


As Multi-Robot Systems (MRS) become more affordable and computing capabilities grow, they provide significant advantages for complex applications such as environmental monitoring, underwater inspections, or space exploration. However, accounting for potential communication loss or the unavailability of communication infrastructures in these application domains remains an open problem. Much of the applicable MRS research assumes that the system can sustain communication through proximity regulations and formation control or by devising a framework for separating and adhering to a predetermined plan for extended periods of disconnection. The latter technique enables an MRS to be more efficient, but breakdowns and environmental uncertainties can have a domino effect throughout the system, particularly when the mission goal is intricate or time-sensitive. To deal with this problem, our proposed framework has two main phases: i) a centralized planner to allocate mission tasks by rewarding intermittent rendezvous between robots to mitigate the effects of the unforeseen events during mission execution, and ii) a decentralized replanning scheme leveraging epistemic planning to formalize belief propagation and a Monte Carlo tree search for policy optimization given distributed rational belief updates. The proposed framework outperforms a baseline heuristic and is validated using simulations and experiments with aerial vehicles.

Abstract (translated)

随着多机器人系统(MRS)价格的降低和计算能力的提高,它们在环境监测、水下检查或太空探索等复杂应用中提供了显著的优势。然而,在这些应用领域中考虑潜在的通信损失或通信基础设施的不可用性仍然是一个开放的问题。MRS研究的很多部分都假定系统可以通过近距规则和组建控制以及设计一个框架来分离和遵循一个确定的计划来维持通信。后者使得MRS更加高效,但 breakdowns(故障)和 environmental uncertainties(环境不确定性)在整个系统内可能产生多米诺骨牌效应,特别是在任务目标复杂或时间敏感的情况下。为了解决这个问题,我们提出的框架有两个主要阶段:i)一个集中规划器,通过奖励机器人之间间歇性重聚来分配任务任务,减轻执行任务中不可预见事件的影响;ii)一个基于元规划的分布式规划方案,利用演绎规划来形式化信念传播和 Monte Carlo树搜索进行策略优化,给定分布式合理信念更新。所提出的框架在模拟和实验中验证了比基线启发式更优越的性能。



3D Action Action_Localization Action_Recognition Activity Adversarial Agent Attention Autonomous Bert Boundary_Detection Caption Chat Classification CNN Compressive_Sensing Contour Contrastive_Learning Deep_Learning Denoising Detection Dialog Diffusion Drone Dynamic_Memory_Network Edge_Detection Embedding Embodied Emotion Enhancement Face Face_Detection Face_Recognition Facial_Landmark Few-Shot Gait_Recognition GAN Gaze_Estimation Gesture Gradient_Descent Handwriting Human_Parsing Image_Caption Image_Classification Image_Compression Image_Enhancement Image_Generation Image_Matting Image_Retrieval Inference Inpainting Intelligent_Chip Knowledge Knowledge_Graph Language_Model Matching Medical Memory_Networks Multi_Modal Multi_Task NAS NMT Object_Detection Object_Tracking OCR Ontology Optical_Character Optical_Flow Optimization Person_Re-identification Point_Cloud Portrait_Generation Pose Pose_Estimation Prediction QA Quantitative Quantitative_Finance Quantization Re-identification Recognition Recommendation Reconstruction Regularization Reinforcement_Learning Relation Relation_Extraction Represenation Represenation_Learning Restoration Review RNN Salient Scene_Classification Scene_Generation Scene_Parsing Scene_Text Segmentation Self-Supervised Semantic_Instance_Segmentation Semantic_Segmentation Semi_Global Semi_Supervised Sence_graph Sentiment Sentiment_Classification Sketch SLAM Sparse Speech Speech_Recognition Style_Transfer Summarization Super_Resolution Surveillance Survey Text_Classification Text_Generation Tracking Transfer_Learning Transformer Unsupervised Video_Caption Video_Classification Video_Indexing Video_Prediction Video_Retrieval Visual_Relation VQA Weakly_Supervised Zero-Shot