Prevention of Covid-19 affected patient using multi robot cooperation and Q-learning approach: a solution

Sahu, Binod Kumar; Das, Pradipta Kumar; Kabat, Manas Ranjan; Kumar, Raghvendra

doi:10.1007/s11135-021-01155-1

Cited by 10 publications

(6 citation statements)

References 31 publications

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“…Multi-robot task assignment for serving people quarantined in multiple hotels during COVID-19 pandemic potential for disinfection, delivering medications and food, and reconnaissance (e.g., body temperature measurement as shown in Figure 1 and oropharyngeal swabs) (5). Some studies have investigated the deployment of multiple robots for the combat with the COVID-19 (6)(7)(8). In (6), the task assignment for patient-carrying robots to transfer patients to a COVID-19 hall and the task assignment for service-providing robots to deliver medicines and food to the patients on time are studied based on the Q-learning approach.…”

Section: Original Articlementioning

confidence: 99%

“…Some studies have investigated the deployment of multiple robots for the combat with the COVID-19 (6)(7)(8). In (6), the task assignment for patient-carrying robots to transfer patients to a COVID-19 hall and the task assignment for service-providing robots to deliver medicines and food to the patients on time are studied based on the Q-learning approach. Blockchain technology has been investigated for controlling and managing multi-drone collaboration for transporting goods and medical supplies to quarantine areas experiencing an epidemic outbreak (7,8).…”

Section: Original Articlementioning

confidence: 99%

“…[4] requires that the people quarantined in each hotel i will be served by one and only one robot; Eqs. [5] and [6] illustrate the remaining operation time of each robot after successively serving the people quarantined in two hotels i and j; Eq. [7] guarantees that the maximum operation time of each robot is satisfied; Eq.…”

Section: Problem Formulationmentioning

confidence: 99%

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Multi-robot task assignment for serving people quarantined in multiple hotels during COVID-19 pandemic

Bai¹,

Li²,

Li³

et al. 2023

Quant Imaging Med Surg

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Background: Efficiently combating with the coronavirus disease 2019 (COVID-19) has been challengingfor medics, police and other service providers. To reduce human interaction, multi-robot systems are promising for performing various missions such as disinfection, monitoring, temperature measurement and delivering goods to people quarantined in prescribed homes and hotels. This paper studies the task assignment problem for multiple dispersed homogeneous robots to visit a set of prescribed hotels to perform tasks such as body temperature assessment or oropharyngeal swabs for people quarantined in the hotels while trying to minimize the robots' total operation time. Each robot can move to multiple hotels sequentially within its limited maximum operation time to provide the service. Methods:The task assignment problem generalizes the multiple traveling salesman problem, which is an NP-hard problem. The main contributions of the paper are twofold: (I) a lower bound on the robots' total operation time to serve all the people has been found based on graph theory, which can be used to approximately evaluate the optimality of an assignment solution; (II) several efficient marginal-cost-based task assignment algorithms are designed to assign the hotel-serving tasks to the robots.Results: In the Monte Carlo simulations where different numbers of robots need to serve the people quarantined in 30 and 90 hotels, the designed task assignment algorithms can quickly (around 30 milliseconds) calculate near-optimal assignment solutions (within 1.15 times of the optimal value).Conclusions: Numerical simulations show that the algorithms can lead to solutions that are close to the optimal compared with the competitive genetic algorithm and greedy algorithm.

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Section: Original Articlementioning

confidence: 99%

Section: Original Articlementioning

confidence: 99%

See 1 more Smart Citation

Multi-robot task assignment for serving people quarantined in multiple hotels during COVID-19 pandemic

Bai¹,

Li²,

Li³

et al. 2023

Quant Imaging Med Surg

View full text Add to dashboard Cite

show abstract

“…In the current scenario, Internet application services, such as Voice-over-IP (VoIP), video streaming, audio streaming, and online games, are in high demand. Because of the COVID spread, there has been significant growth in online transactions in the last two years [9] [10]. Multimedia services account for more than half of all current Internet traffic.…”

Section: Figure 1 Wireless Mesh Networkmentioning

confidence: 99%

Adaptive Routing in Wireless Mesh Networks Using Hybrid Reinforcement Learning Algorithm

Mahajan

Kotecha

2022

IEEE Access

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Wireless mesh networks are popular due to their adaptability, easy-setup, flexibility, cost, and transmission time-reductions. The routing algorithm plays a vital role in transferring the data between the nodes. The network's performance is significantly impacted by the route opted by the algorithm. The router takes the decision to send the packet to the next router as per the policy of that algorithm. So even though that decision does not favor the right path selection, the router tends to follow its policy. This can be avoided by having intelligent routers that can make routing decisions on the fly.This paper presents the QL-Feed Forward routing algorithm(QFFR), a new generation of routing algorithms that combines reinforcement learning based on the Q-learning algorithm with a Feed Forward neural network. This algorithm(QFFR) can learn from the network environment and make routing decisions based on the algorithm's learnings. The AI agent's ability to select the fastest path, which enhances the efficiency of the routing operation, is demonstrated by the working of the suggested QFFR algorithm. This paper also evaluates the performance of traditional algorithms, namely, Ad-hoc On-Demand Distance-Vector, Optimized-Link-State-routing, Destination-Sequenced Distance-Vector and Distance Source routing. The evaluation parameters include throughput, packet delivery ratio, and delay. The parameters are the outcomes of the time the information takes to reach from source to destination. This analysis highlights the improvement in the routing decision ability of a router. As per analysis, Ad hoc On-Demand Distance Vector Algorithm outperforms with throughput 723.13 Kbps, delay 343.73 ns. Q-learning agent identifies the route and reaches the destination in average of 3.7s in non-grid architecture. The Q-learning agent takes 0.49sec with a grid size ten by ten and 0.53sec in three by four grid size. The suggested QFFR takes 7.62s score-over time with stable, consistent performance. INDEX TERMSDeep Learning , Reinforcement Learning, Q-learning , Markov Decision Process , routing algorithms, wireless mesh networks I. INTRODUCTION D URING this era of digital development, networks play a critical role. Figure 1 shows Wireless Mesh Networks with a mesh architecture. Wireless mesh networks' key advantages are their versatility and customizing capabilities. Any future modifications would be simple to accommodate, resulting in lower expenses and upkeep of the network. Wireless mesh networking is a recent development that emerged from a decade of Ad-hoc networking development [1]. A wireless mesh network (WMN) is a decentralized network system built on existing wireless technology, namely the 802.11 standards, that works on an Ad-hoc communication mode [2] [3]. The wireless mesh network is ideal for nextgeneration communication with its flexibility and extensive coverage. Multi-radio mesh routers and single-radio mesh clients form Wireless Mesh Networks(WMN) [4]. As a result, such networks are developed by connecting wireless access...

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“…The approach produces an optimal path by exercising the Arduino bots. Cooperation among multiple mobile robots is achieved by implementing Qlearning algorithm for patient transportation (QCOV-R) [11]. Sine cosine algorithm (SCA) is a simple approach used to compute the collision free path based on its sine and cosine selection policy [12].…”

Section: Introductionmentioning

confidence: 99%

Cooperative Path Planning for Object Transportation with Fault Management

Sindhuja

Sahu

Das

et al. 2023

Preprint

Self Cite

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The current research intents on enhancing the service ability of mobile robot by cooperative path planning. The strategy is developed by fusion of sine cosine algorithm and particle swarm optimization approach for the transition of service robot in complex environment. To ensure the successful execution of the intended task it is essential to have a faultless and collision-less path for the mobile robot. This supposition can be achieved by producing an intelligent fault-managed approach. The proposed paper addresses the object transportation by a pair of robots from source to destination, this task can be accomplished in three step, such as fault identification, fault resolution using robot replacement and computation of a collision-less path. At each step of transition, path planning is carried out to reach the target location. Sine cosine algorithm improves the exploration capability of the robots in the multi robot environment. Particle swarm optimization being the simplest technique to exercise the path planning problem produces an optimal global position for each particle along each dimension. The fusion of both provides a balance exploration-exploitation ability of the mobile robot. Fault identification overcomes the faulty transition and unsuccessful transition of the robot by detecting the fault of any robot through its non-responding time. K- nearest neighbor approach identifies the nearest working robot to replace the faulty on. The algorithm has been exercised in C language to showcase its capability in terms of execution time, path traveled, path deviated etc. The comparative analysis proofs the supremacy of the proposed algorithm in terms of several metrics such as path planning, cooperation, fault management, etc.

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Prevention of Covid-19 affected patient using multi robot cooperation and Q-learning approach: a solution

Cited by 10 publications

References 31 publications

Multi-robot task assignment for serving people quarantined in multiple hotels during COVID-19 pandemic

Multi-robot task assignment for serving people quarantined in multiple hotels during COVID-19 pandemic

Adaptive Routing in Wireless Mesh Networks Using Hybrid Reinforcement Learning Algorithm

Cooperative Path Planning for Object Transportation with Fault Management

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