In this paper, we address the problem of online dynamic multi-robot task allocation (MRTA) problem. In the existing literature, several works investigated this problem as a multi-objective ...optimization (MOO) problem and proposed different approaches to solve it including heuristic methods. Existing works attempted to find Pareto-optimal solutions to the MOO problem. However, to the best of authors’ knowledge, none of the existing works used the task quality as an objective to optimize. In this paper, we address this gap, and we propose a new method, distributed multi-objective task allocation approach (DYMO-Auction), that considers tasks’ quality requirement, along with travel distance and load balancing. A robot is capable of performing the same task with different levels of perfection, and a task needs to be performed with a level of perfection. We call this level of perfection quality level. We designed a new utility function to consider four competing metrics, namely the cost, energy, distance, type of tasks. It assigns the tasks dynamically as they emerge without global information and selects the auctioneer randomly for each new task to avoid the single point of failure. Extensive simulation experiments using a 3D Webots simulator are conducted to evaluate the performance of the proposed DYMO-Auction. DYMO-Auction is compared with the sequential single-item approach (SSI), which requires global information and offline calculations, and with Fuzzy Logic Multiple Traveling Salesman Problem (FL-MTSP) approach. The results demonstrate a proper matching with SSI in terms of quality satisfaction and load balancing. However, DYMO-Auction demands 20% more travel distance. We experimented with DYMO-Auction using real Turtlebot2 robots. The results of simulation experiments and prototype experiments follow the same trend. This demonstrates the usefulness and practicality of the proposed method in real-world scenarios.
Motion planning of agents is one of the fundamental research problems in autonomous systems. An important aspect of motion planning is collision avoidance of the agents with other agents and ...obstacles that are present in the agent's environment. Typically, the collision avoidance constraints are non-linear and non-convex. Thus, the mathematical formulation of motion planning of multiple agents, in the presence of other agents and obstacles, is NP-Hard. In this paper, a novel heuristic approach for motion planning in multi-agent dynamic environment is proposed. The approach is computationally cheap, and can be launched locally on each agent for the trajectory planning. The applicability of the proposed approach is illustrated by numerical examples considering uncertainty in the environment. Detailed discussions on the performance of the proposed approach are presented. Finally, the observations on the key characteristics of the proposed approach are summarized.
A new technique is presented to design energy-efficient large-scale tracking systems based on mobile clustering. The new technique optimizes the formation of mobile clusters to minimize energy ...consumption in large-scale tracking systems. This technique can be used in large public gatherings with high crowd density and continuous mobility. Utilizing both Bluetooth and Wi-Fi technologies in smart phones, the technique tracks the movement of individuals in a large crowd within a specific area, and monitors their current locations and health conditions. The new system has several advantages, including good positioning accuracy, low energy consumption, short transmission delay, and low signal interference. Two types of interference are reduced: between Bluetooth and Wi-Fi signals, and between different Bluetooth signals. An integer linear programming model is developed to optimize the construction of clusters. In addition, a simulation model is constructed and used to test the new technique under different conditions. The proposed clustering technique shows superior performance according to several evaluation criteria.
Obstacle avoidance based on a monocular camera is a challenging task due to the lack of 3D information for Unmanned Aerial Vehicle. Recent methods based on Convolutional Neural Networks for monocular ...depth estimation and obstacle detection become widely used. However, collision avoidance with depth estimation usually suffers from long computational time and low avoidance success rate. A new collision avoidance system is proposed which uses monocular camera and intelligent algorithm to avoid obstacles on real time processing. Several experiments have been conducted on crowded environments with several object types. The results show outstanding performance in terms of obstacles avoidance and system response time compared to contemporary approaches. This makes the proposed approach of high potential to be integrated in crowded environments.
This study proposes a new collision avoidance system using monocular camera and intelligent algorithm to avoid obstacles on real time processing. Experimental results using Telo drone conducted in crowded environments with several object types show outstanding performance in terms of obstacles avoidance and system response time compared to contemporary approaches. This makes the proposed system of high potential to be integrated in crowded environments.
Electric vehicles (EVs) are experiencing substantial investment and widespread acceptance. However, successful penetration of the global market is contingent upon the development of a strategic plan ...for the efficient allocation of EVs to optimal charging stations (CSs). This study combines several optimization models to systematically assign EVs to the optimal charging stations, with the goal of maximizing trading energy while simultaneously minimizing total response time. Factors taken into consideration include traveling distance, charging (V2G), and discharging (G2V) energy trading, total response time, and energy prices. The efficacy of the combined models is validated using GAMS and BARON solvers, with a focus on EV satisfaction factor, updated energy and response time, number of served EVs, and alleviation of range anxiety. The proposed models demonstrate 85% satisfaction factor for the majority of charging requests, reaching almost 99% for discharging requests. These results surpass those of contemporary models, underscoring the heightened effectiveness of the proposed approach.
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•Optimal EV allocation balances energy trading and response time.•Comprehensive model considers mobility, V2G, G2V, response, and prices.•GAMS simulations show consistently high EV satisfaction levels.•Superior outcomes compared to COP and EVaaS models in satisfaction.•Future work targets an emergency model for EVs with low energy levels.
Path planning is extensively used in different fields not only in robotics but also in games, manufacturing, auto‐motive applications, and so on. Robot path planning is one of the major research ...issues in the area of autonomous mobile robot. The critical step in path planning problem is to find the shortest path from the start position to a defined goal position through a known, unknown, or partially known environment. Hazardous events that may devastate some parts of the intended area convert those areas to untraversable areas. These events introduce topological constraints for the robot motion because of information discrepancy about the environment before and after the damage. In this study, the authors propose a novel method, FreeD∗, to find the shortest path by exploiting the benefits of D∗, Dijkstra, and artificial potential field (APF) algorithms. The generated path using D∗ is optimised using Dijkstra by combining D∗ sub‐paths into a single diagonal path if there is no known obstacle between them. Then, APF is used in unknown obstacle avoidance. The simulation results using Webots simulator demonstrate the effectiveness of FreeD∗ in avoiding unknown obstacles with shortest path.
Demand-side management (DSM) plays a key role in the future of smart grids. Recently, DSM researchers have developed various mathematical models to optimize the demand response. Most of these works ...ignore the channel impairments' impact on the optimization process. In this paper, we propose a new noncooperative game theoretic model for the management of smart grid's demand considering the packet error rate in our formulation. We set the Nash equilibrium conditions for the proposed model. Under an assumption on the form of the utility functions, we develop a 0-1 mixed linear programming approach to compute nondominated extreme Nash equilibria. Results on a numerical example are provided and some useful insights are presented. Under some assumptions and a fully proven proposition, a feasible nondominated Nash equilibrium solution is found. Finally, we report and comment on computational experiments on randomly generated smart grid DSM game instances with different characteristics.
•In general, the cyber-attacks in the literature can be classified into three main types: denial of service (DoS) attacks, deception attacks, and replay attacks. The focus of this paper will be on ...each of the aforementioned attacks such that the modeling and detection of each attack will be addressed, and the control of CPS under such attack will be discussed in details.•After a preliminary introduction of the subject, the paper is organized as follows. Detection of cyber-attacks are summarized in Section 2. In Section 3 the DoS attack is addressed. In Section 4, the results on the deception attack are given. Section 5 covers the replay attack.•Finally, challenges and future work are discussed in Section 6.
Cyber Physical Systems (CPS) are almost everywhere; they can be accessed and controlled remotely. These features make them more vulnerable to cyber attacks. Since these systems provide critical services, having them under attack would have dangerous consequences. Unfortunately, cyber attacks may be detected, but after the damage is done. Therefore, developing a cyber system that can survive an attack is a challenge. In this paper, we are surveying the literature on security aspects of CPSs. First, we present some of existing methods for detecting cyber attacks. Second, we focus on three main cyber attacks, which are: Denial of service (DoS), deception, and replay attacks. In our discussion, we have surveyed some exiting models of these attacks, approaches of filtering CPS subject to these attacks, and approaches of control CPS subject to these attacks.
Federated Learning (FL) is a collaborative training method for machine learning (ML) that aggregates model weights from multiple participants during the training phase. The learning phase of machine ...learning techniques is distributed, in which each participating device trains a model using its local data set and sends model weights to a centralized node. The central node aggregates weights and sends the updated weights back to devices. The process continues until a specific threshold is reached such accuracy, response time. In this paper, we present a performance evaluation of FL in a clustering-based multi-hop network to simulate the effect of the dynamic environment on the accuracy of the global model. It is observed that a minimum number of participating nodes is required within a cluster to maintain a high level of global accuracy. A global threshold value needs to be defined to maintain high global accuracy and avoid degradation of model performance.
A Radiometric signature refers to transceiver specific features that are caused by variations in the manufacturing process even for the same circuit design. While such a radiometric signature ...constitutes a fingerprint that can be exploited for device authentication, it is a threat to privacy. Particularly, in the realm of wireless networks, an adversary may exploit radio frequency (RF) fingerprinting to identify devices and conduct traffic analysis in order to uncover the topology and categorize the role of various nodes. In this paper, we show how an adversary could employ RF fingerprinting to distinguish among nodes and bypass the provisioned anonymity protection in the network. We analyze the accuracy of RF fingerprinting and highlight how the accuracy affects the success of adversary attacks. To counter such a threat, we propose a novel methodology that requires no hardware changes to the radio transceiver and the associated host device. Our methodology is based on coordinated switching among preset link-layer and physical-layer communication protocols. For the latter, we particularly exploit distributed beamforming. We employ adversarial machine learning to select the protocol configuration for each transmission so that the accuracy of the RF fingerprinting diminishes. We demonstrate the effectiveness of our scheme through simulation and prototype experiments.
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