Efficient spatial coverage by a robot swarm based on an ant foraging model and the Lévy distribution

This paper describes an implementation of Levy walk (or Levy flight) to pheromone communicating swarm robots. Levy flight is a special class of random walk in which the step length distribution is given by power law distribution. Levy flight is known to maximize the efficiency of resource searches in uncertain environments. Using computer simulations, we show that the Levy walk-like searching strategy can maximize the group foraging efficiency of the swarm robots using pheromone trails (mimicking ant group foraging), as well as maximize individual searching area. The Levy walk was achieved by adjusting the probability per unit time with which an individual robot moves forward (otherwise it turns to right, to left, and reverse). We discuss the effect of swarming on optimal parameter values of Levy walk. Optimization of individual searching strategies should be studied further, both in swarm robots and real organisms.

The supply chain industry is one of the fast pace and technology-driven industries. The dynamic supply chain concept expresses how significant is the adaption to immediate changes in the industry. Many organizations strive to manage their operations with high visibility to adopt changes for performance excellence. This study identifies the metrics of supply chain performance that affect the performance excellence of supply chain operations. It investigates categories of performance metrics in terms of time, cost, and quality. Likewise, it discovers the characteristics of performance metrics through measuring working capital in the supply chain. In addition, it defines the key performance indicators (KPIs) and its core calculations for the end-to-end supply chain to measure the practicality of operations' efficiency. The supply chain operations with respect to critical KPIs are determined as plan, source, make, delivery, and return. The proposed performance metrics- KPIs presented as the milestones of dynamic supply chain models. Moreover, the change in the supply chain was reviewed due to the Covid-19 pandemic along with performance metrics.

The area coverage problem solution is one of the vital research areas which can benefit from swarm robotics. The greatest challenge to the swarm robotics system is to complete the task of covering an area effectively. Many domains where area coverage is essential include exploration, surveillance, mapping, foraging, and several other applications. This paper introduces a survey of swarm robotics in area coverage research papers from 2015 to 2022 regarding the algorithms and methods used, hardware, and applications in this domain. Different types of algorithms and hardware were dealt with and analysed; according to the analysis, the characteristics and advantages of each of them were identified, and we determined their suitability for different applications in covering the area for many goals. This study demonstrates that naturally inspired algorithms have the most significant role in swarm robotics for area coverage compared to other techniques. In addition, modern hardware has more capabilities suitable for supporting swarm robotics to cover an area, even if the environment is complex and contains static or dynamic obstacles.

Levy walk is a family of random walks whose distance of a walk is chosen from the power law distribution. There are lots of works of Levy walk in the context of target detection in swarm robotics, analyzing human walk patterns, and modeling the behavior of animal foraging in recent years. According to these results, it is known as an efficient method to search in a two-dimensional plane. On the other hand, mobile agents which move based on Levy walk diffuse messages efficiently by opportunistic communication on unit disk graphs. In this paper, we focus on Nomadic Levy Walk which is a variant of Levy walk, and propose a base movement strategy for Nomadic Levy Walk and analyze the impact of the base repositioning strategy of Nomadic Levy Walk on the efficiency on message dissemination by measuring the number of steps for diffusing messages by opportunistic communication on unit disk graphs. Our simulation results indicate that the proposed strategy is significantly efficient to message dissemination.

We address the problem of repeated coverage of a target area, of any polygonal shape, by a team of robots having a limited visual range. Three distributed Cluster-based algorithms, and a method called Cyclic Coverage are introduced for the problem. The goal is to evaluate the performance of the repeated coverage algorithms under the effect of changes in the robots' visual range. A comprehensive set of performance metrics are considered, including the distance the robots travel, the frequency of visiting points in the target area, and the degree of balance in workload distribution among the robots. The Cyclic Coverage approach, used as a benchmark to compare the algorithms, produces optimal or near-optimal solutions for the single robot case under some criteria. The results show that the identity of the optimal repeated coverage algorithm depends on the metric and the robots' visual range.

In 1989 Gerardo Beni and Jing Wang first proposed the name ―Swarm Intelligence‖ in their paper ―Swarm Intelligence in Cellular Robotic Systems‖. Some remarkable observations of different researchers are studied in this paper, like the proximity principle, the quality principle, the principle of diverse response, the principle of stability, the principle of adaptability. To enhance the capabilities of robot and different systems, researchers started to exploit the behavior of natural systems. Swarm groups are governed by three rules, move in the same direction as your neighbor, remain close to your neighbor, and avoid collision with your neighbor .Characteristics of swarm groups are emergence and stigmergy. Different insects like ants, wasps, termites carry out a work locally for global goal with sufficient flexibility as they are not controlled centrally. In this paper the existing research works are analysed to show the behavior in social insects by using self-organization, positive feedback, negative feedback, amplification of fluctuation, multiple interactions. It has also been observed that these insects are almost blind and memoryless, still they communicate indirectly among themselves for stigmergic effect by using pheromone. Implementation of swarm intelligence in robotics i.e., swarm robots are narrated. The limitations of swarm robots as well as factors behind the success of swarm robotics have also been encompassed. Finally authors focus on swarm robots applications in telecommunication fields, civil engineering and digital image processing.

Automating mosquito control is a pivotal advancement in the pest control industry with the primary objective of mitigating the prevalence of vector-borne diseases. Recent progress in pest control robotics has enabled the automation of mosquito activity restrictions. However, existing robotic solutions have exhibited limitations in effectively addressing mosquito control while lacking a sensitive strategy for maximizing area coverage with crowded areas as a priority. In response to these challenges, this article proposes a novel human-first approach for complete coverage path planning (HFA-CCPP) that leverages the Glasius Bio-inspired Neural Network (GBNN) to cover areas that simulate and consider human activity patterns systematically. In this study, a mosquito-capturing robot, Dragonfly, is demonstrated with HFA-CCPP. This article provides an in-depth exploration of the technical intricacies of the proposed solution. The efficacy of the proposed technique is evaluated in terms of total area coverage and times taken to cover the high human activity region in simulation and real-world environments by comparing results with traditional GBNN. Across all scenarios, the proposed HFA-CCPP surpasses the traditional method by delivering efficient area coverage with minimal time for human-dense area coverage and efficiency in mosquito trapping. This finding stands as a newfound direction in automated mosquito control, holding great potential for curbing vector-borne diseases.

Using swarm robots to deal with the task of area coverage has broad applications from regional surveillance, material transportation, to search and rescue. To achieve the goal of higher coverage rate and lower time cost, we present the K-means clustering strategy to execute the dynamical selection of the target position of the next step for each robot, based on the anti-flocking framework. The proposed strategy clusters the non-explored area points to distribute the covering task for each robot and finally assists the swarm to cover the area efficiently. Compared with the classical anti-flocking algorithm, the coverage rate of the proposed method in this paper has been significantly improved. For different combinations of swarm size and interaction radius, this paper uses the genetic algorithm to optimize the model parameters and obtain the optimal model parameters. The simulation results prove that when the model parameters are all optimal, the larger the swarm size is, the more efficient the swarm coverage is, but the interaction radius has no significant effect on the efficiency of the swarm coverage. Finally, we successfully transfer the dynamical clustering algorithm to a semi-physical simulation platform and implement our method adaption in a hardware-in-the-loop simulation to achieve swarm coverage with 7 quadrotors.

This paper discusses the problem of building efficient coverage paths for a team of robots. An efficient multirobot coverage algorithm should result in a coverage path for every robot, such that the union of all paths generates a full coverage of the terrain and the total coverage time is minimized. A method, underlying several coverage algorithms, suggests the use of spanning trees as base for creating coverage paths. Current studies assume that the spanning tree is given, and try to make the most out of the given configuration. However, overall performance of the coverage is heavily dependent on the given spanning tree. This paper tackles the open challenge of constructing a coverage spanning tree that minimizes the time to complete coverage. We argue that the choice of the initial spanning tree has far reaching consequences concerning the coverage time, and if the tree is constructed appropriately, it could considerably reduce the coverage time of the terrain. Therefore the problem studied here is finding spanning trees that would decrease the coverage time of the terrain when used as base for multi-robot coverage algorithms. The main contributions of this paper are twofold. First, it provides initial sound discussion and results concerning the construction of the tree as a crucial base for any efficient coverage algorithm. Second, it describes a polynomial-time tree construction algorithm that, as shown in extensive simulations, dramatically improves the coverage time even when used as a basis for a simple, inefficient, coverage algorithm

Swarm robotics is an approach to the coordination of large numbers of robots that has become an increasingly popular field of research in recent years, not least because properly engineered robot swarms are scalable, flexible, and robust, making them an attractive alternative to single-robot systems in many application domains. Since its inception, the field of swarm robotics has grown beyond its roots in purely decentralised control inspired by social insect behaviour, now often utilising hybrid centralised/decentralised control architectures that incorporate human operators who guide swarm actions during tasks such as firefighting, or the localisation of radiation sources. This kind of human-swarm interaction has attracted significant interest from the research community, spawning an entire sub-field of its own that investigates how human operators, supervisors, and team-mates can interact with robot swarms and receive feedback from them. To date, human-swarm control methods such as the use of graphical user interfaces and spatial gestures have received much attention, but there has been little investigation into the potential of controlling swarm robotic systems with an operator’s voice. The few studies that have explored this idea are restricted to the use of specific predefined phrases that the human operator is required to learn, resulting in interactions that are unnatural in comparison to the way a human would normally express themselves in speech. In this paper, we present a novel architecture for conversational human-swarm interaction that addresses these issues, allowing swarm robotic systems to be engineered in such a way that a human operator can guide a swarm using spoken dialogue in a more natural manner.

Wireless sensor networks (WSNs) are often utilized to detect the physical obstacles due to their heterogeneous movement and self-organizing, and local cooperation capabilities. Meanwhile, with the instantaneous growth of smart automobiles, mobile devices can be used to collect data in WSNs. Although those mobile devices can considerably improve network performance, it is difficult to create an appropriate trip path for efficient data collection. In this study, we provide an itinerary planning schema (IPS- DM) to discover a route having minimum distance that passes through as multiple sensors as feasible. Sensor nodes will probably refer to use a single-hop connection for networks, raw data uploading in their transmission range to save energy usage. Monitor nodes (MNs) are defined initially for the Data gathering module (DM) to collect sensor basic metrics and then their quantity is establish found on the absolute coverage rate. Finally, we use the route scheduling planning module (RPM) model to organize the quickest loop for MNs.KeywordsSensor networkRoute planningCoverage optimizationWireless devices

Wireless sensor networks generally have unique lifetime necessities. In any case, the density of the sensors may not be sufficiently substantial to fulfil the coverage requirement while meeting the lifetime constraint in the mean time. Once in a while coverage has to be traded for network lifetime. The proposed efficient pipeline based spatial temporal optimization scheduling for coverage optimization satisfies the coverage problem while meeting the lifetime constraint at the same time. In the proposed optimal scheduling, initially number of nodes in the network is clustered by using energy based one hop clustering algorithm. After the formation of clusters pipeline based spatial temporal optimization algorithm is used for the optimal scheduling. Here the optimization is improved by using trust of each sensor nodes and the area of clusters. Finally, data is aggregated through the optimally scheduled cluster nodes. The experimental results show that our proposed optimization scheduling substantially outperforms other schemes in terms of network lifetime, coverage redundancy and convergence time.

The integration between cellular and satellite systems permits to provide wider and more efficient area coverage. In this paper the viability of a full integration between the GSM cellular network and a spot-beam satellite system is dealt with. The integration analysis focuses on the network aspects. Full integration means to reuse, as far as possible, the same equipment and protocols, i.e. the same hardware and software, for the satellite and the GSM network. In the integrated system the user need not be aware whether the actual connection goes through the satellite or the GSM network. The paper identifies the most promising satellite system configuration and the satellite system counterpart of the GSM functional areas (cell, location area, MSC area). Moreover, the problems and the relevant solutions related to the extension of the GSM procedures (e.g. call set-up, cell selection/reselection, handover, initial acquisition, tracking) to the satellite system are dealt with.

The integration between cellular and satellite systems permits to provide wider and more efficient area coverage. In this paper the viability of a full integration between the GSM cellular network and a satellite system (the L-band Land Mobile payload has been assumed as a reference, nevertheless the results can be extended to other payloads) is dealt with. The integration analysis focuses on the link feasibility. Full integration entails that the satellite system reuses the same channels, access technique, modulation, and coding as the GSM system. The presented results have been obtained by means of an ad hoc simulation tool developed at CSELT (Turin). The paper outlines a reference scenario for the integrated system and explains the characteristics of the satellite propagation channel, of the reference payload and of the satellite-compatible mobile station (both the RF equipment and the IF equipment main features are discussed). The simulation results are displayed for the GSM speech channels at both full rate (TCH/FS) and half rate (TCH/HS). Such results are used in order to compute the overall link budget and the satellite system capacity.

The problem of area coverage in unknown environments is crucial in applications such as environmental monitoring, disaster response, and autonomous navigation. Traditional methods struggle to balance exploration, obstacle avoidance, and efficient coverage. In this paper, we propose a deep reinforcement learning-based multi-agent approach that optimizes coverage efficiency while adapting to unknown obstacles. We integrate a two-layer architecture, where the upper layer employs reinforcement learning for global path planning, while the lower layer handles local obstacle avoidance and movement execution. Experimental results demonstrate that our algorithm effectively converges in unknown environments and completes the area coverage task.