2022 IEEE International Symposium on Robotic and Sensors Environments (ROSE)最新文献

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UWB Role Allocation with Distributed Ledger Technologies for Scalable Relative Localization in Multi-Robot Systems 基于分布式账本技术的超宽带角色分配用于多机器人系统的可扩展相对定位

2022 IEEE International Symposium on Robotic and Sensors Environments (ROSE) Pub Date : 2022-08-29 DOI: 10.1109/ROSE56499.2022.9977431

Paola Torrico Morón, Salma Salimi, J. P. Queralta, Tomi Westerlund

{"title":"UWB Role Allocation with Distributed Ledger Technologies for Scalable Relative Localization in Multi-Robot Systems","authors":"Paola Torrico Morón, Salma Salimi, J. P. Queralta, Tomi Westerlund","doi":"10.1109/ROSE56499.2022.9977431","DOIUrl":"https://doi.org/10.1109/ROSE56499.2022.9977431","url":null,"abstract":"Systems for relative localization in multi-robot systems based on ultra-wideband (UWB) ranging have recently emerged as robust solutions for GNSS-denied environments. Scalability remains one of the key challenges, particularly in adhoc deployments. Recent solutions include dynamic allocation of active and passive localization modes for different robots or nodes in the system. with larger-scale systems becoming more distributed, key research questions arise in the areas of security and trustability of such localization systems. This paper studies the potential integration of collaborative-decision making processes with distributed ledger technologies. Specifically, we investigate the design and implementation of a methodology for running an UWB role allocation algorithm within smart contracts in a blockchain. In previous works, we have separately studied the integration of ROS2 with the Hyperledger Fabric blockchain, and introduced a new algorithm for scalable UWB-based localization. In this paper, we extend these works by (i) running experiments with larger number of mobile robots switching between different spatial configurations and (ii) integrating the dynamic UWB role allocation algorithm into Fabric smart contracts for distributed decision-making in a system of multiple mobile robots. This enables us to deliver the same functionality within a secure and trustable process, with enhanced identity and data access management. Our results show the effectiveness of the UWB role allocation for continuously varying spatial formations of six autonomous mobile robots, while demonstrating a low impact on latency and computational resources of adding the blockchain layer that does not affect the localization process.","PeriodicalId":265529,"journal":{"name":"2022 IEEE International Symposium on Robotic and Sensors Environments (ROSE)","volume":"382 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132015059","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

Path Planning of Cleaning Robot with Reinforcement Learning 基于强化学习的清扫机器人路径规划

2022 IEEE International Symposium on Robotic and Sensors Environments (ROSE) Pub Date : 2022-08-17 DOI: 10.1109/ROSE56499.2022.9977430

Woohyeon Moon, Bumgeun Park, Sarvar Hussain Nengroo, Taeyoung Kim, Dongsoo Har

{"title":"Path Planning of Cleaning Robot with Reinforcement Learning","authors":"Woohyeon Moon, Bumgeun Park, Sarvar Hussain Nengroo, Taeyoung Kim, Dongsoo Har","doi":"10.1109/ROSE56499.2022.9977430","DOIUrl":"https://doi.org/10.1109/ROSE56499.2022.9977430","url":null,"abstract":"Recently, as the demand for cleaning robots has steadily increased, therefore household electricity consumption is also increasing. To solve this electricity consumption issue, the problem of efficient path planning for cleaning robot has become important and many studies have been conducted. However, most of them are about moving along a simple path segment, not about the whole path to clean all places. As the emerging deep learning technique, reinforcement learning (RL) has been adopted for cleaning robot. However, the models for RL operate only in a specific cleaning environment, not the various cleaning environment. The problem is that the models have to retrain whenever the cleaning environment changes. To solve this problem, the proximal policy optimization (PPO) algorithm is combined with an efficient path planning that operates in various cleaning environments, using transfer learning (TL), detection nearest cleaned tile, reward shaping, and making elite set methods. The proposed method is validated with an ablation study and comparison with conventional methods such as random and zigzag. The experimental results demonstrate that the proposed method achieves improved training performance and increased convergence speed over the original PPO. And it also demonstrates that this proposed method is better performance than conventional methods (random, zigzag).","PeriodicalId":265529,"journal":{"name":"2022 IEEE International Symposium on Robotic and Sensors Environments (ROSE)","volume":"506 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123566418","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

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