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Exploration in Extreme Environments with Swarm Robotic System

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... In general, search and rescue missions are complex tasks in which autonomous robots must safely explore the disaster sites and provide rescue teams with important information, such as victim locations and status, environmental conditions, and the locations of dangerous objects [8,10,11]. While designing robots capable of addressing these tasks, several strategies can be taken into consideration. ...
... Environmental exploration by autonomous mobile robots is a process through which an unknown environment is analysed and mapped by visiting all available areas. Many recent studies were aimed at improving this process [15,16], including research in extreme environments [11,17] and planetary exploration [18,19]. Although many different strategies have been introduced to address unknown environment exploration problems, a popular and easy-to-implement basis for autonomous mobile robot testing remains the frontier-based environment exploration method, originally proposed by Yamauchi [20]. ...
... The multiplication of interval-valued neutrosophic number ( * ) = 10), and the complementary neutrosophic number component can be defined by Equation(11). The summation of two IVNNs ( * ) = ⟨[ ...
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The application of autonomous robots in search and rescue missions represents a complex task which requires a robot to make robust decisions in unknown and dangerous environments. However, imprecise robot movements and small measurement errors obtained by robot sensors can have an impact on the autonomous environment exploration quality, and therefore, should be addressed while designing search and rescue (SAR) robots. In this paper, a novel frontier evaluation strategy is proposed, that address technical, economic, social, and environmental factors of the sustainable environment exploration process, and a new extension of the weighted aggregated sum product assessment (WASPAS) method, modelled under interval-valued neutrosophic sets (IVNS), is introduced for autonomous mobile robots. The general-purpose Pioneer 3-AT robot platform is applied in simulated search and rescue missions, and the conducted experimental assessment shows the proposed method efficiency in commercial and public-type building exploration. By addressing the estimated measurement errors in the initial data obtained by the robot sensors, the proposed decision-making framework provides additional reliability for comparing and ranking candidate frontiers. The interval-valued multi-criteria decision-making method combined with the proposed frontier evaluation strategy enables the robot to exhaustively explore and map smaller SAR mission environments as well as ensure robot safety and efficient energy consumption in relatively larger public-type building environments.
... Definition 1: "Swarm robotics is defined following the principles that a swarm system should have a large number of robots, tasks should be solved and improved using a swarm system, and that the robots exchange local information through limited communication distances" [22]. ...
Chapter
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Humans have always been inspired by their environment to solve their problems. When it directly imitates the behavior of living things, it is called biomimicry. Biomimicry seeks to identify winning life strategies to apply them in our world to solve challenges. It is a practice that learns from and mimics the technique used by species alive today. Fish, birds, bats, bees, fireflies, many animals, and insects provide us with a permanent demonstration of a phenomenon as simple as it is complex and will be discussed in this reading: swarms. Swarm intelligence is a subfield of computer science that draws inspiration from the behavior of swarms to solve problems. It is possible to characterize a swarm as a structured set of individuals with limited individual capacities who offer collective intelligence to solve complex problems. Swarm robotics is an application of swarm intelligence. By applying the concept to multi-robot systems, behaviors similar to those observed in the living world are reproduced and make it possible to solve problems, propose new approaches or improve existing ones. This paper reviews the swarm robotics approach from its history to its future. First, we review several Swarm Intelligence concepts to define Swarm Robotics systems, reporting their essential qualities and features and contrasting them to generic multi-robotic systems. Then, we discuss the basic idea of swarm robotics, its important features, simulators, real-life applications, and some future ideas.
... However, they have shown that robots with different sensors, such as Relative Localization Sensor, can provide faster and more accurate results in swarm applications [6]. Another study proposes the control of swarm robotics in order to perform challenging tasks [7]. Here, exploratory mission simulation was carried out with proximity and color sensors in the region thought to contain radiation. ...
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In this study, the general structure of swarm robotics is examined. Algorithms inspired by nature, which form the basis of swarm robotics, are introduced. Communication topologies in robotic swarms, which are similar to the communication methods between living things moving in nature, are included and how these can be used in swarm communication is emphasized. With the developed algorithms, how the swarm can imitate nature and what tasks it can perform have been explained. The various problems that will be encountered in terms of the design of the optimization methods used during the control of the swarm and the solutions are simulated using the Webots software. As a result, ideas on the solutions of these problems and suggestions are proposed.
... Future study could include integrating sensors into autonomous and swarm-based robot systems. Swarm robots or unmanned vehicles can monitor the environment, search for the hazardous environment [60,61], or grade the risk value [62]. In this case, new artificial intelligence-based research activities such as efficient communication protocols, efficient use of sensor energy, optimal environment coverage in mobile sensor nodes, autonomous movement, CBRN risk assessment, intelligent decision making and selforganized response strategies without human commanders in swarm robots can be carried out. ...
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Chemical reconnaissance, defined as hazards detection, identification, and monitoring, requires tools and solutions which provide reliable and precise data. In this field, the advances of artificial intelligence can be applied. This article aims to propose a novel approach for developing a chemical reconnaissance system that relies on machine learning, modelling algorithms, as well as the contaminant dispersion model to combine signals from different sensors and reduce false alarm rates. A case study of the European Union Horizon 2020 project–EU-SENSE is used and the main features of the system are analysed: heterogeneous sensor nodes components, chemical agents to be detected, and system architecture design. Through the proposed approach, chemical reconnaissance capabilities are improved, resulting in more effective crisis management. The idea for the system design can be used and developed in other areas, namely, in biological or radiological threat reconnaissance.
... These swarm robotic systems also proved to be scalable, as the number of robots deployed could be changed with little or no effect on their overall performances. The system's functions could also be easily modified by selecting an appropriate number of robots, although the real-world application of these techniques could be complex and their developments more tasking [47]. ...
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During nuclear facility decommissioning, workers are continuously exposed to high-level radiation. Hence, adequate path planning is critical to protect workers from unnecessary radiation exposure. This work exhaustively discusses recent developments in radioactive path planning and the algorithms recommended for the task. Specifically, we review the conventional methods for nuclear decommissioning path planning, analyze the techniques utilized in the development of the algorithms, and enumerate the decision factors that should be considered to optimize path planning algorithms. As a major contribution, we present the quantitative performance comparisons of different algorithms utilized in solving path planning problems in nuclear decommissioning and highlight their merits and drawbacks. Also, we discuss techniques, and critical considerations necessary for efficient application of path planning algorithms used in other fields, such as robotics, into nuclear facility decommissioning. Moreover, we analyze the influence of obstacles, and the environmental/radioactive source dynamics on the efficiency of algorithms developed. The evaluation metrics utilized to measure the performance of the algorithms are also presented. Finally, we recommend future research focus and highlight critical improvements required for the existing approaches, towards the development of better, more reliable, and safer paths for a nuclear decommissioning project.
... Furthermore, authors in [159] investigate how SR could collaboratively fight against the spread of wildfires. Another risky scenario is addressed in [160], related with different levels of radioactive or chemical leakage from drums in a nuclear storage facility. Additionally, a BA is implemented in [161] for the guidance of a swarm in the exploration of closed environments and reaching a fixed objective. ...
Chapter
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Optimization is one of the most studied fields within the wider area of artificial intelligence. In the current literature, hundreds of works can be found focused on solving many diverse problems of this kind by resorting to a vast spectrum of solvers. In this context, Swarm Intelligence methods have gained significant popularity in the related community, maintaining a constant momentum in recent years, and having been applied to problems coming from a wide variety of real-world contexts. This chapter contributes to this line by presenting a systematic overview of Swarm Intelligence solvers applied to different branches of optimization problems. To do that, we have focused our attention on four of the most intensively studied application fields: transportation, energy, medicine, and industry. Apart from this systematic review, we also share in this paper our envisioned status of this area, by identifying the most interesting opportunities. These open challenges should stimulate the scientific efforts made by the community in the upcoming years.
... Innocente and Grasso show in [13] that SR can collaboratively battle against the spread of wildfires. Another extreme scenario is tackled in [14], which relates to different levels of radioactive and chemical leakage from drums in a nuclear storage facility. Furthermore, in [15] a Bat Algorithm is introduced for guiding a swarm of small robots in their exploration of a closed environment towards reaching a fixed objective location. ...
Chapter
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The term Swarm Robotics collectively refers to a population of robotic devices that efficiently undertakes diverse tasks in a collaborative way by virtue of computational intelligence techniques. This paradigm has given rise to a profitable stream of contributions in recent years, all sharing a clear consensus on the performance benefits derived from the increased exploration capabilities offered by Swarm Robotics. This manuscript falls within this topic: specifically, it gravitates on an heterogeneous Swarm Robotics system that relies on Stochastic Diffusion Search (SDS) as the coordination heuristics for the exploration, location and delimitation of areas scattered over the area in which robots are deployed. The swarm is composed by agents of diverse kind, which can be ground robots or flying devices. These agents communicate to each other and cooperate towards the accomplishment of the exploration tasks comprising the mission of the overall swarm. Furthermore, maps contain several obstacles and dangers, implying that in order to enter a specific area, robots should meet certain conditions. Experiments are conducted over three different maps and three implemented solving approaches. Conclusions are drawn from the obtained results, confirming that i) SDS allows for a lightweight, heuristic mechanism for the coordination of the robots; and ii) the most efficient swarming approach is the one comprising a heterogeneity of ground and aerial robots.
... However, due to the strong nonlinearity and coupling of the manipulator system, the PID control method may lead to a considerable output torque of the controller [11]. However, in the actual application process, the control torque provided by the drive motor must be limited, and the characteristics of the mechanical arm itself are not allowed to sustain a particularly large control torque, which will cause great damage to the drive motor [12]. ...
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In recent years, robot industry has developed rapidly, which has been widely used in industrial production and social life. The control problem of manipulator system with strong nonlinearity has become a hot spot. The difficulty of the trajectory tracking control of the manipulator is that the manipulator system is generally complex, and its dynamic model is very complex, including multiple mutually coupled inputs and outputs, sensor noise, friction in the manipulator and the flexibility of the joint link, which makes the manipulator system highly nonlinear. At the same time, due to some errors in the measurement of some inherent parameters of the manipulator, as well as different interference in the external environment, it is difficult to establish the accurate mathematical model of the manipulator system. In this paper, the Gauss process feedback linearization method based on the updating of the event triggered model is applied to a manipulator system with three degrees of freedom to realize the trajectory tracking control of the manipulator. And for the real-time requirement of trajectory tracking of manipulator, sparse Gaussian process regression is used to solve the problem of large amount of calculation of Gaussian process regression under large data samples.
... Huang et al. [1] propose an uncoordinated algorithm for a swarm of robots to localize chemical leakages or radiation in a factory. The authors define three main stages. ...
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Recent Mars Exploration Rovers (MERs) launched in 2021 show the growing interest in the robot’s mobility improvement and application of cooperative robotics. Increased mobility has been addressed by launching the first flying robot to Mars, bringing new opportunities to explore previously inaccessible areas. Such collaborative solutions expand the possibilities for Mars exploration. This paper suggests a concept for using a modular robotic swarm consisting of several independent two-wheeled robots. To evaluate the proposed system, we introduce a development methodology for MERs along with metrics for assessing modular surface exploration systems. This article focuses on how cooperative modular robotic solutions for Mars exploration can improve such Figures of Merit (FOMs) as mission lifetime, exploration speed, and cost of the mission. To validate the FOMs for a proposed solution, we use Pareto Frontiers as a decision instrument for multi-objective optimization. Our results illustrate the engineering tradeoffs and potential advantages between two-wheeled rovers and existing Mars Exploration Rovers.
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Using robots for exploration of extreme and hazardous environments has the potential to significantly improve human safety. For example, robotic solutions can be deployed to find the source of a chemical leakage and clean the contaminated area. This paper demonstrates a proof-of-concept bio-inspired exploration method using a swarm robotic system based on a combination of two bio-inspired behaviors: aggregation, and pheromone tracking. The main idea of the work presented is to follow pheromone trails to find the source of a chemical leakage and then carry out a decontamination task by aggregating at the critical zone. Using experiments conducted by a simulated model of a Mona robot, the effects of population size and robot speed on the ability of the swarm was evaluated in a decontamination task. The results indicate the feasibility of deploying robotic swarms in an exploration and cleaning task in an extreme environment.
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Autocatalytic interactions between the members of an animal group or society, and particularly chemically or visually mediated allelomimesis, can be an important factor in the organisation of their collective activity. Furthermore, the interactions between the individuals and the environment allow different collective patterns and decisions to appear under different conditions, with the same individual behaviour. While most clearly demonstrable in social insects, these principles are fundamental to schools of fishes, flocks of birds, groups of mammals, and many other social aggregates. The analysis of collective behaviour in these terms implies detailed observation of both individual and collective behaviour, combined with mathematical modelling to link the two.
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This paper describes an approach to solve the Simultaneous Localization and Mapping (SLAM) problem with a team of cooperative autonomous vehicles. We consider that each robot is equipped with a stereo camera and is able to observe visual landmarks in the environment. The SLAM approach presented here is feature-based, thus the map is represented by a set of three dimensional landmarks each one defined by a global position in space and a visual descriptor. The robots move independently along different trajectories and make relative measurements to landmarks in the environment in order to jointly build a common map using a Rao-Blackwellized particle filter. We show results obtained in a simulated environment that validate the SLAM approach. The process of observing a visual landmark is simulated in the following way: first, the relative measurement obtained by the robot is corrupted with gaussian noise, using a noise model for a standard stereo camera. Second, the visual description of the landmark is altered by noise, simulating the changes in the descriptor which may occur when the robot observes the same landmark under different scales and viewpoints. In addition, the noise in the odometry of the robots also takes values obtained from real robots. We propose an approach to manage data associations in the context of visual features. Different experiments have been performed, with variations in the path followed by the robots and the parameters in the particle filter. Finally, the results obtained in simulation demonstrate that the approach is suitable for small robot teams.
Conference Paper
Designing algorithms for multi-robot systems can be a complex and difficult process: the cost of such systems can be very high, collecting experimental data can be time-consuming, and individual robots may malfunction, invalidating experiments. These constraints make it very tempting to work using high-level abstractions of the robots and their environment. While these high-level models can be useful for initial design, it is important to verify techniques in more realistic scenarios that include real-world effects that may have been ignored in the abstractions. In this paper, we take a simple, coordinated, multi-robot search algorithm and illustrate problems that it encounters in environments which incorporate real-world factors, such as probabilistic target detection and positional noise. We compare the performance to that of several simple randomized approaches, which are better able to deal with these constraints.
Article
This paper presents two new strategies for navigation of a swarm of robots for target/mission focused applications including landmine detection and firefighting. The first method presents an embedded fuzzy logic approach in the particle swarm optimization (PSO) algorithm robots and the second method presents a swarm of fuzzy logic controllers, one on each robot. The framework of both strategies has been inspired by natural swarms such as the school of fish or the flock of birds. In addition to the target search using the above methods, a hierarchy for the coordination of a swarm of robots has been proposed. The robustness of both strategies is evaluated for failures or loss in swarm members. Results are presented with both strategies and comparisons of their performance are carried out against a greedy search algorithm.
Article
A model of monarch bufferfly mating in overwinter aggregations is presented. Mating success each day is predicted to be a function of second-order kinetics. Therefore, increased density increases each individual's frequency of mating. Since multiple mating of females is critical for female energetics in overwinter populations (and in turn fecundity), and mating frequency is based on second-order kinetics, aggregations play an indispensable role in the reproduction dynamics of overwintering Danaus plexippus.
Article
Mobile robots are becoming more heavily used in environments where human involvement is limited, impossible, or dangerous. These robots perform some of the more laborious human tasks on Earth and throughout the solar system, simultaneously saving resources and offering automation. Higher levels of autonomy are also being sought in these applications, such as distributed exploration and mapping of unknown areas. Smaller, less expensive mobile robots are becoming more prevalent, which introduces unique challenges in terms of limited sensing accuracy and onboard computing resources. This paper presents a novel low-cost, limited-resource approach to autonomous multi-robot mapping and exploration in unstructured environments. Design and implementation details are presented, along with results from two planetary style environments. Results demonstrate that low-cost ($ 1250) mobile robots capable of simultaneous localization and mapping can be successfully constructed. The multi-robot system presented in this paper participated in the 2008 International Conference on Robotics and Automation (ICRA) Space Robotics Challenge, receiving two awards for successfully completing the ’Onto the Surface’ and ’Map the Environment’ events in a simulated planetary environment. This work demonstrates not only that such systems are possible, but also that this direction of research is important and needs attention.
Article
We present an integrated approach to multirobot exploration, mapping and searching suitable for large teams of robots operating in unknown areas lacking an existing supporting communications infrastructure. We present a set of algorithms that have been both implemented and experimentally verified on teams—of what we refer to as Centibots—consisting of as many as 100 robots. The results that we present involve search tasks that can be divided into a mapping stage in which robots must jointly explore a large unknown area with the goal of generating a consistent map from the fragment, a search stage in which robots are deployed within the environment in order to systematically search for an object of interest, and a protection phase in which robots are distributed to track any intruders in the search area. During the first stage, the robots actively seek to verify their relative locations in order to ensure consistency when combining data into shared maps; they must also coordinate their exploration strategies so as to maximize the efficiency of exploration. In the second and third stages, robots allocate search tasks among themselves; since tasks are not defined a priori, the robots first produce a topological graph of the area of interest and then generate a set of tasks that reflect spatial and communication constraints. Our system was evaluated under extremely realistic real-world conditions. An outside evaluation team found the system to be highly efficient and robust.
Article
This paper presents an experiment in collective robotics in which a group of autonomous robots searches for an infrared target beacon placed in a corner of the exploration area. This task is a more experimentally tractable version of the plume tracing problem, in which robots search for the source of an odor plume. Two different exploration strategies (collaborative and non-collaborative) are implemented and compared on the basis of several team performance metrics. The collaborative strategy uses a simple, binary signaling schema among robots. The experiment is implemented at three different levels: in a physical setup composed of groups of 1 to 8 Moorebot robots, in Webots, a 3D sensor-based, kinematic simulator, and with probabilistic simulations. Results show that the collaborative approach drastically improves the search across several metrics. Furthermore, the probabilistic model qualitatively and quantitatively reproduces the enhanced team performance via collaboration. Additional investigations using the probabilistic model indicate that the optimal number of robots is a function of the ratio between target and exploration areas.
Article
This paper presents an investigation of odor localization by groups of autonomous mobile robots using principles of Swarm Intelligence. First, we describe a distributed algorithm by which groups of agents can solve the full odor localization task more efficiently than a single agent. Next, we demonstrate that a group of real robots under fully distributed control can successfully traverse a real odor plume, and that an embodied simulator can faithfully reproduce these real robots experiments. Finally, we use the embodied simulator combined with a reinforcement learning algorithm to optimize performance across group size, showing that it can be useful not only for improving real world odor localization, but also for quantitatively characterizing the influence of group size on task performance.
Article
One of the most striking patterns in biology is the formation of animal aggregations. Classically, aggregation has been viewed as an evolutionarily advantageous state, in which members derive the benefits of protection, mate choice, and centralized information, balanced by the costs of limiting resources. Consisting of individual members, aggregations nevertheless function as an integrated whole, displaying a complex set of behaviors not possible at the level of the individual organism. Complexity theory indicates that large populations of units can self-organize into aggregations that generate pattern, store information, and engage in collective decision-making. This begs the question, are all emergent properties of animal aggregations functional or are some simply pattern? Solutions to this dilemma will necessitate a closer marriage of theoretical and modeling studies linked to empirical work addressing the choices, and trajectories, of individuals constrained by membership in the group.
Article
Social insect colonies have many of the properties of adaptive networks. The simple rules governing how local interactions among individuals translate into group behaviors are found across social groups, giving social insects the potential to have a profound impact on our understanding of the interplay between network dynamics and social evolution.
Robotics in practice: management and applications of industrial robots
  • J F Engelberger