Swarm Robotics for Area Coverage Problem

Version	Summary	Created by	Modification	Content Size	Created at	Operation
1		dena kadhim muhsen	--	6513	2024-01-17 08:31:53	\|
2	Reference format revised.	Lindsay Dong	Meta information modification	6513	2024-01-19 02:20:54	\|

This entry is adapted from the peer-reviewed paper 10.3390/a17010003

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.

swarm robotics area coverage hardware architecture swarm robotics algorithms

1. Introduction

Area coverage is one of the essential and attractive topics that has been studied in the last few years. This term is used in specific spatial area robotics to gain or update information in the robotic domain. Area coverage is a critical concept in different significant applications comprising the exploration of planetary, detection and demining of mines, wildfire fighting, and surveillance. Generally, the set of coverage behaviours required and the performance metrics used depend on the application ^[1]. Area coverage is a field of multi-robot systems called swarm robots. A swarm robot is one swarm intelligence application, including mobile robots responsible for completing a particular task ^[2]. Many distributed algorithms have been proposed and used for coordinating sets of robots to maximise the sensing coverage of a given environment area.

Swarm robotics is bio-inspired; the behaviour stems from observing common behaviours of animals, where each individual in the group acts autonomously similarly ^[3]. The basic idea for this swarm robotics is that each robot undergoing simple rules depends on local sensory inputs and communication with their neighbours locally ^[4]. Swarm robotics has possibilities to enable the use of practices in different problem solutions because of their characteristics: synergistic, distributed, robust, operating in real-time, universal, practical, optimality, reliability, and scalability. Swarm robotics research encompasses aggregation, area coverage, the search for a target, and cooperative handling ^[5].

2. Algorithms and Methods for Swarm Robotics

Most algorithms applied for swarm robotics in area coverage are inspired by coordination among biological entities ^[6]. The algorithms used in swarm robots are classified into stochastic and deterministic ^[7]. By definition, deterministic approaches use mathematical models that rely on local optimum and gradient to solve problems ^[8]. In contrast, stochastic techniques rely on mathematical properties to perform a given function, but with minimal dependency on the gradient and local optimums when solving specific problems ^[9]. Experts argue that the stochastic technique is more user-friendly than the latter in optimising robots to work independently or simultaneously in a swarm ^[10]. The type of algorithm applied in swarm robots is mainly dependent on the nature or type of problem being solved by the robot ^[11]. Researchers and developers of the optimisation domain have designed stochastic algorithms that mimic natural processes that are synonymous with various types of animals and flies, such as fish, birds, and bees. The notable algorithms developed from such observations include Particle Swarm Optimisation (PSO) and Honey Bee Optimization (MBO) ^[12]^[13]. And, lately, algorithms are based on bacteria colony behaviours ^[14]. The algorithms mentioned above are commonly used in swarm robots for area coverage.

2.1. Metaheuristic Algorithms–Swarm Intelligence

2.1.1. Ant Colony Optimization (ACO)

The ACO algorithm is one of the swarm intelligence algorithms ^[15]. The movement of ants is influenced by their need to search for food. When ants move from one point to another in search of food, they produce organic compounds called pheromones in the form of deposits in their footpath ^[16]^[17]. Pheromone paths enable ants to reach the food source and bring it to deposit in their colonies ^[18]. The ants will follow the trail with the highest quality and quantity of food ^[19]. Many researchers designed an ant-based algorithm applicable to swarm robots. The algorithm uses artificial intelligence robot-to-robot communication as a substitute for nature-based communication ^[20].

Ref. ^[21] introduced the design of control laws used by swarm robots to enable them to work cooperatively. Ant colonies are used for this purpose through pheromones to control individual robot behaviour. The decentralised control law is formed using many reaction-diffusion equations, leading to area coverage. Analysis was made in this work for the effect of pheromone diffusion and evaporation on the environment and measuring the performance of area coverage. Adding noise to the control law has the greatest effect on the algorithm for covering an area, and these parameters significantly influence the performance of the area coverage. The global concept is achieved by finding the most critical parameter to be the magnitude of the noise applied. Meanwhile, the local image is also achieved by adding noise depending on the importance of the other parameters: diffusion and evaporation.

2.1.2. Particle Swarm Optimisation (PSO)

Particle Swarm Optimisation is one of the swarm algorithms in artificial intelligence developed by Kennedy and Eberhart to simulate birds’ flocking behaviour ^[22] graphically. It is a stochastic technique that facilitates robot movement in a swarm ^[23]. The Particle Swarm Optimisation algorithm differs from others because its functionality solely depends on the objective function. It does not rely on differential objectives or gradients in flying formation ^[24]. A flock of birds that move in groups benefits from the experience of each flock member. For instance, when a flock of birds flies randomly in search of food, they increase the chances of each bird in the community getting the best hunt ^[25]^[26]. The Particle Swarm Optimisation algorithm was designed to assist in solving optimisation problems in both the local and global space ^[27]^[28]. However, each robot utilises the Particle Swarm Optimisation algorithm to navigate the terrain or area in a semi-autonomous manner that is distinct from other robots in the swarm. This allows the robot to solve the problem and navigate independently while functioning as a swarm ^[29]^[30].

Ref. ^[31] presented five robots with an XBee module for the problem of seeking electromagnetic sources by area coverage by modifying and implementing the PSO algorithm on mobile robots according to the physical constraints presented by both robots and the environment. Three different PSOs were evaluated by simulations encompassing PSO with Inertia Weight, PSO with Constriction Factor, and Standard Particle Swarm Optimization (SPSO). The area covered was 5 × 5 m², and applying the Vicon tracking system gave the information of each robot’s precise position via the support of an indoor GPS system to recognise markers on the robots. The type of robots used with an XBee module in experiments improved from the Parallax Shield-Bot, which Arduino controls. These robots search for the target source, a module of XBee hanging above a floor at a height of 20 cm in the middle of the area, to avoid a probability collision with the Robot. Also, the avoidance strategies for static and dynamic obstacles occurred in PSO in this experiment. The results proved that the best and most convenient diversity of PSO is the Inertia Weight PSO for this work.

2.1.3. Bacterial Foraging Optimisation (BFO) Algorithm

The BFO was proposed to mimic the E. coli bacteria’s foraging behaviours in the intestine ^[32]. The algorithm became popular due to its high ability to escape from the local optimum, and when compared with other heuristic methods, it has faster convergence ^[33]. Some researchers are working to improve the BFO.

Ref. ^[34] introduced a new proposal for a swarm robotic system called a bacterial chemotaxis-inspired coordination strategy (BCCS) for coverage and aggregation. Chaotic preprocessing is used to initialise the starting positions of the robots. Then, the area covered by the robot is computed as a fitness function value to compare with previous ones. Based on BCCS, every robot makes an action, running or rotating. The process continues until the maximum number of iterations is met or the number of covered cells satisfies the termination conditions. In addition, it has presented a random factor to guide robots to rendezvous at an undefined point to overcome aggregation. The simulation results showed the supremacy performance of the proposed strategy compared with other controllers in both success rate and iteration average number. BCCS has a high exploration ability for area coverage.

2.1.4. Bee Algorithm

The bee algorithm is inspired by observing how honey bees breed, mate, and forage. Their behaviours form the basis of bee optimisation algorithms ^[35]^[36]. The Honey Bee Optimization (MBO) is among the main algorithms based on the breeding and mating activities associated with honey bees and depends on swarm intelligence ^[37]^[38]. There are several algorithms based on the Honey Bee Optimization (MBO), including Fast Marriage in Honey Bee Optimization (FMHBO), Honey Bees Mating Optimization (HBMO), and Honey Bee Optimization (HBO) ^[39]^[40]. As mentioned earlier, the algorithm uses evolutionary behaviours, such as the random explorative behaviour of the bees, to solve problems or achieve set objectives. The principal functionality of the bee algorithm commences from a single source, typically referred to as the queen, and flows to other bees (or robots) in the colony (swarm) ^[41]. The principle is based on the forward pass concept, which implies that information is sent from a single source as it flows to other colony members ^[42]. Like the queen, a single robot in a swarm is used as the source of information to guide other robots as they move in the area ^[43]. The algorithm’s objective is to allow swarm robots to be attracted to a goal with the highest solution when deployed to survey a large expanse ^[44].

Ref. ^[45] proposed a novel concept of surveillance robotics that is based on honey bees and with the integration of an autonomous telepresence robot. The telepresence robot means considering human control in the loop, as doing so is essential to improve robotic swarm efficiency and speed up convergence. Experiments used Turtlebot robots for performing a foraging task, beginning at the hive location and randomly exploring in a specific area for a particular food location. The experiments assessing a proposed swarm coordination system in an unknown environment were performed via a simulation and on real robots. The simulation was performed for three settings: 5 × 5 m² shaped, 10 × 10 m² shaped, and 10 × 10 m² L-shaped. When Turtlebots find a food source, they take it and drive it to the hive, where they put food until they run out of food or detect another source. The telepresence represents a leader who sends related information about the location to the Turtlebots. The findings show the telepresence robot’s role in increasing the operation’s efficiency, chiefly in dynamic and complex scenarios where the sources of food change over time.

2.1.5. A Bio-Inspired Neural Network Approach

Artificial intelligence methods especially have more significance in swarm robots for area coverage. Several robots cooperate to complete coverage tasks efficiently. A neural dynamics method is proposed for this purpose and guides the group of robots in a dynamic environment for complete area coverage (CAC). Every robot considers other robots to be moving obstacles. The mobile robot’s path is generated from the landscape of the neural network and the previous position of the robot. Many cases used this method and showed effective results to enable the robots to cover an area. The computations of the model are easy, and the path of the robot is created without searching over the global free workspace or any global cost functions ^[46].

2.1.6. Cohort Intelligence (CI) Methodology

Cohort Intelligence (CI) methodology is used to model the behaviour of candidates depending on the interaction among them to perform a common goal. The behaviour of each candidate is improved by looking at all other candidates in the cohort. The CI is used for coverage area in the operation of search and rescue by swarm robots, the roulette wheel selection method, and the median method. Also, a perturbation technique is used to solve the problem of getting stuck in non-convex obstacles by robots. Many cases were achieved, such as the No Obstacle Case (NOC), Stationary Obstacles Case (SOC), Single Dynamic Obstacle Case (SDOC), Multiple Dynamic Obstacles Case with the Same Velocity (MDOC-SV), and Multiple Dynamic Obstacles Case with Different Velocities (MDOCDV) ^[47].

2.2. Classical Algorithms

2.2.1. Dynamic Voronoi-Based Algorithm

It is a mathematical model generally used in area division in some regions. These regions are based on the seeds given in the first. Every part has a corresponding region encompassing all points in space closer to itself than others. In this case, the region is called Voronoi cells ^[48]. The Voronoi algorithm controls swarm robots; each robot acts as a seed and divides the target area into Voronoi cells. Every robot must cover its own Voronoi cell, guaranteeing that each target point is closer to its corresponding robot ^[49].

Ref. ^[50] proposed a dynamic Voronoi-based algorithm, which is used for the area coverage problem; it divides the target area into Voronoi cells dynamically through the moving of robots rather than using a static algorithm and to improve the efficiency of modified bacterial foraging optimisation (MBFO) by using swarm robots in searching. The MBFO is also used to coordinate between robot positions and the swarm robot target area. The MBFO algorithm considers each robot to be a bacterium. Then, it applies the mechanism of bacteria chemotaxis to solve problems of decentralised controlling for swarm robots in experiments, using these two algorithms to the process of area coverage by 26 swarm robots in random locations on a 300 × 300 m² area by MBFO. The moving of robots depends on following the concentration gradient in the target area and using the sensor control the reaction between them. Also, after using 10, 20, and 30 robots to validate the efficiency of the new dynamic Voronoi bade on MBFO, the results showed the effectiveness of the dynamic Voronoi method.

2.2.2. The Decentralised Space-Based Potential Field (D-SBPF) Algorithm

D-SBPF is a simple decentralised method for dispersing a robot team to quickly explore and cover an area. The algorithm is considered a potential control method that supports knowledge of the area bounds to be explored. The basic idea is to solve the problem of deploying many robots in an unknown environment (buildings) to examine and collect information about the environment, using an effective method. The experiments were carried out using three maps, each 10 × 10 m², and a grid representation of 30 × 30. The approach uses an extended occupancy grid to represent the space where each cell can be attractive (if undiscovered) or repulsive (if discovered). A non-monotonic field scale factor proportional to coverage is also used to improve the searching of corners and niches and to assist in moving robots out of potential minima. The main characteristic of this method is that the robot, at any stage, can leave/join/rejoin the team. The simulation results show that using more robots at the beginning of exploration leads to more area coverage ^[51].

2.2.3. The Force Vector Algorithm

The general force vector algorithm was designed for most swarm robotics applications with few requirements to solve area coverage problems. This algorithm works efficiently and gets good results; it is considered an alternative to many complex algorithms for effectively covering an environment. Many simple robots are required for implementation with more capabilities, enabling the algorithm to cover specific areas ^[52].

2.2.4. Operative Distributed Asynchronous (CORDA) Model

Asynchronous CORDA is the primary computational model in the domain of swarm robots. It has a closer approximation to real-life situations when compared to other algorithms. The algorithms developed using the CORDA model view robots as consisting of four sequential cycles: wait, observe, compute, and move. All the cycles do not overlap. The primary objective of the research is to prove the CORDA model is popular and suitable compared to other available computational models for area coverage problems. Many solutions for area coverage have been discussed comprehensively by using this model. Two situations are applied. The first one is when covering the area without an obstacle; in this case, the robot starts to compute the boundaries of the strip that will be covered by itself depending on its closest horizontal neighbours. In the second case, when covering the area with obstacles, the robots start by being deployed randomly in the area and then collect on the left boundary. The robots divide the space into several blocks and draw the size of the blocks.

2.2.5. Deployment Entropy with Potential Fields Strategy

Deployment entropy was presented to cover persistent areas, using many sensing swarm robots, which depend on partitioning the area into many regions. Deployment means the uniformity of agents per region across all regions when covering an area. The study showed that a good spread of agents and growing sensor coverage resulted when compared with previous results, which did not use potential fields with deployment entropy. Fifty-agent deployment was used for the simulation. Two redeployments are global, which happens in partition regions, and local, which occurs in subregions. As a result, the robots cluster together more in corners at the end. The attractive and repulsive fields are applied receptively between robots, leading to the greater spread of robots to achieve area coverage.

2.2.6. A Self-Organising Area Coverage Based on Gradient and Grouping (GGC)

A new method depending on gradient and grouping was proposed for area coverage called shortly (GGC), using simple robots without computing or storage space. The rise of a robot led to the system of swarm robots with accessible functions that enable self-organisation to cover the area of the unknown task. In a grouping operation, each group can cover the task area in parallel, improving the coverage speed. The simulation results show superior gradient and grouping methods on the other techniques in coverage concepts, coverage completion time, robustness, and other sides. Simultaneously, this method is beneficial for the system of submillimetre swarm robots, which will be considered basically for micro-medicine ^[53].

2.2.7. Frontier-Led Swarming Algorithm

A Frontier-Led Swarming algorithm was proposed for the exploration and coverage of the area of unknown environments while controlling a formation that allows for short-range communication. The algorithm includes two components: swarm rules to save a closeknit appearance and frontier search for maintaining exploration and coverage. Three experiments were conducted in various environments, using heterogeneous and homogeneous groups of mobile robots to test the algorithm. The first experiment used real heterogeneous swarm robots, the TurtleBot Burger and Pioneer UGVs, and the second used real robots in an environment containing unknown static obstacles. The third experiment used a simulation in a 2D large-scale urban-like environment with obstacles through a virtual Gazebo.

3. Algorithms Analysis

The algorithms and methods discussed in this survey point to using swarm robotics to cover specific areas. These algorithms are either metaheuristic or classical algorithms. Each of these algorithms is adopted and integrated into swarm robots based on the need and the type of problem being solved in an extensive coverage. The successful usage of swarm robots is not solely associated with the algorithm used but with the existing hardware infrastructure. The area environment is an essential factor in achieving coverage; it may be known components or unknown. In unfamiliar territory, they face difficulties handling it due to needing more information about obstacles available and their type (static or dynamic).

Earlier algorithms were based on teams to mimic the swarm behaviours of biological entities. The primary challenge of team-based algorithms was encountering obstacles, and complete synchrony was required for effective communication among team members. Individual coverage is an emerging trend in recent surveys where a robot communicates with the rest of the swarm and updates the area covered. Regardless, there are gaps in adequate area coverage, although the current algorithms are relatively better than earlier algorithms. Another takeaway from this survey is that modern algorithms rely less on computations than earlier algorithms with elaborate mathematical models.

Table 1 provides a thorough summary of several metaheuristic algorithms, from those with their origins in swarm intelligence to the classical algorithms used in swarm robotics to cover areas. A wide range of tasks, environments, and applications can be handled by these algorithms. You can learn a lot about the algorithms’ strengths and weaknesses, as well as their possible difficulties, from this. Researchers and practitioners interested in the state of area coverage algorithms for swarm robotics will find the table to be an invaluable resource. The ant algorithm, which exemplifies swarm intelligence with its decentralised chemotactic control law, is a renowned example of a metaheuristic algorithm. Incorporating noise to enhance area coverage performance, this algorithm showcases both global and local ideas. However, there are problems with iterative modifications to the probability distribution and uncertainty in the convergence time. The Cellular Automata Ant Memory model with Tabu Search is another notable technique that deals with foraging activities and dynamic applications. While the algorithm’s usage of Tabu Search-inspired short-term memory is effective, it has computational hurdles when dealing with robot movement.

Table 1. The specifications of each algorithm or method.

Algorithm or Method	Application	Environment	Environment Complexity	Task	Obstacle	Type	Classification	Strength	Limitations	Ref.	Year
Ant algorithm + decentralised chemotactic control law	Area coverage	Known	Low	Simple	No obstacle	Stochastic alg.	Metaheuristic alg.–swarm intelligence	- Global and local concepts are achieved - Adding noise based on the magnitude to control law significantly influences and improves the performance of area coverage - Using noise is an important step to provide abilities to improve many algorithms, such as the Particle Swarm Optimisation (PSO), grey wolf algorithm, or cuckoo search algorithm	- Uncertainty in convergence time - Probability distribution changes by iteration	^[21]	2016
A Cellular Automata Ant Memory Model (CAAM) + Tabu Search	Area coverage to perform foraging tasks: - Dynamic applications (changes in terrain) - Travelling salesperson problem	Known	Low	Simple	Static	Stochastic alg.	Metaheuristic alg.–swarm intelligence	- Avoid unnecessary explorations by using a short-term memory inspired by Tabu Search - A better distribution for the robot team - Transition rules of C.A. used that provide local control for obstacles, which leads to non-use obstacle avoidance algorithms - Efficient solution	- Sequences of random decisions. - Consuming for computation time through the moving robots	^[54]	2017
Ant foraging + adaptive Brownian Levy flight transitions + control law	Area coverage to perform foraging tasks in 2D domain	Known	Mid	Moderate	No obstacle	Stochastic alg.	Metaheuristic alg.–swarm intelligence + classical algorithm (random walk)	- Improve area coverage performance by using this method - Using Levy led to lowering the constraints of communication and sensing of robots - Increase area coverage up to a specific value of threshold by transitions from Brownian motion to Levy flights	- There is no detailed analysis for parameter variations such as pheromone diffusion coefficient, evaporation rates, Levy index, and noise intensity and does not determine which is better	^[55]	2017
Genetic Shared Tabu Inverted Ant Cellular Automata (GSTIACA)	Area coverage for surveillance tasks	Known	Mid	Simple	Static	Stochastic alg.	Metaheuristic alg.–swarm intelligence	- Provide advanced surrogate techniques for swarm robotic surveillance tasks, especially in science and engineering - Transition rules of C.A. used that provide local control for obstacles, which leads to non-use obstacle avoidance algorithms - GA optimises the control parameters of a robotic - The approach of integrating the various techniques of artificial intelligence with natural computing, which was not used in the previous research	- Not be applied to real robots yet	^[56]	2022
- Particle Swarm Optimisation with Inertia Weight - Particle Swarm Optimisation with Constriction Factor - Standard Particle Swarm Optimisation (SPSO)	area coverage for Source-seeking	Unknown	High	Hard	Static and dynamic	Stochastic alg.	Metaheuristic alg.–swarm intelligence	- Relatively simple to implement - Few parameters to vary - Fast and inexpensive computations - Robust - Escape from local optimal solutions - Work well without a centralised unit if the robots can reach their positions	- More powerful robots are required for areas with obstacles - Increased the swarm size based on each environment/area	^[31]	2015
Robotic Darwinian Particle Swarm Optimization (RDPSO) + Probabilistic Finite Sate Machine (PFSM) + Depth First Search (DFS)	Area coverage through robots’ exploration and navigation	Unknown	Mid	Moderate	Static	Stochastic alg.	Metaheuristic alg.–swarm intelligence	- The proposed method proved to have good navigation in optimal time, about a 40% higher success range, with a speed of 1.4× for exploration compared to other methods. - The consumed time decreases when the size of the swarm increases - The swarm of simple robots is faster than that of a single complex robot	- Increasing the size of the hive above a particular level leads to the saturation of the RDPSO algorithm and not obtaining the optimal time and cost for each task	^[57]	2017
Robotic Darwinian Particle Swarm Optimisation (RDPSO)	Area coverage for search and rescue	Unknown	High	Moderate	Static	Stochastic alg.	Metaheuristic alg.–swarm intelligence	- Reduce the computational cost - Improve the efficiency of navigation - RDPSO permits the robot not to get suboptimal solutions - The ability to determine the positions of multiple targets and collisions - The distribution of the actual target positions does not influence the work of the algorithm	- Increasing the size of the swarm above a particular level leads to the saturation of the RDPSO algorithm and not obtaining the optimal time and cost for each task	^[25]	2017
Particle Swarm Optimisation-Based Algorithm	Area coverage and swarm robot coordination	Unknown	High	Moderate	Static	Stochastic alg.	Metaheuristic alg.–swarm intelligence	- Increasing area coverage and the ratio of detecting more targets by storing the information for one robot when locating a target and starting a search for another - Produces many flexible fitness functions which can be used in various maps and affect the performance of swarm robots	- The algorithm does not focus on robot aggregation because the objective is to explore the area - The positions of robots not known; a particular function was used which returns these locations (like GPS work)	^[58]	2018
Exploration-enhanced RPSO (E2RPSO)	Area coverage to find multiple targets	Unknown	High	Moderate	Static and dynamic	Stochastic alg.	Metaheuristic alg.–swarm intelligence	- Avoid falling into local optimums - Comprehensive search area coverage - It is vital in applications of the search for multitarget due to making a good balance between exploration and exploitation	-Does not detect all targets in the search area	^[59]	2020
Particle Swarm Optimisation algorithm+ Inverse Perspective Map (IPM) transformation	Area coverage for positioning of soccer robots	Known	Low	Simple	No obstacle	Stochastic alg.	Metaheuristic alg.–swarm intelligence	- Implementation simplicity - Reduced computational and memory consumption of its design - Increases speed and decreases size of input information - Eliminate perspective effects - High accuracy in determining the robot location	- The possibility that it might get stuck at local optima, and robots will never be aware that other solutions might exist	^[60]	2021
An adaptive exploration robotic PSO (AERPSO)	Area coverage to find multiple targets	Unknown	High	Moderate	Static and dynamic	Stochastic alg.	Metaheuristic alg.–swarm intelligence	- Avoids local minima - Detecting all targets in the search area - Explores unexplored regions and helps with obstacle avoidance, using evolutionary speed and aggregation degree - Improves the search time - It balances between exploration and exploitation	- Not be applied to real robots yet	^[61]	2022
Bacterial chemotaxis optimisation (B.C.) + Voronoi-based algorithm	- Search for target and trapping within area coverage distributed control for swarm robots in the area	Unknown	High	Hard	Static and dynamic	Stochastic alg. + deterministic	Metaheuristic alg.–swarm intelligence + classical algorithm (motion planning)	- Less vulnerability to a local optimum - Robustness to unexpected failure for a robot - Effectiveness	- Time consumption is based on randomly initialising the population of swarm robots and the target - Does not depend on physical robots to verify the performance	^[33]	2015
Bacterial chemotaxis-inspired coordination strategy (BCCS)	Swarm robotic systems for coverage and aggregation	Known	Low	Simple	No obstacle	Stochastic alg.	Metaheuristic alg.–swarm intelligence	- Better coverage through preprocessing - Better exploration capability - In most cases, BCCS has fewer iterations and a higher success rate - Distributed system	- Uncertainty in irregular environment	^[34]	2021
Honey bee algorithm	Area coverage to perform foraging tasks, robot coordination and surveillance robotics by using a human telepresence robot in the system	Unknown	Low	Simple	No obstacle	Stochastic alg.	Metaheuristic alg.–swarm intelligence	- Low computation - Robustness - Scalability - Adaptability - Simple and flexible - Broad applications, even in complex functions - Popular - Ease of implementations - The human operator controlling the telepresence robot speeds up the convergence of the swarm	- New algorithms require new fitness tests - Slow in sequential processing - Large objective function evaluation	^[45]	2016
Labour division phenomenon approach for the bee colony algorithm and ant colony algorithm	Complex area coverage of swarm robots (- Coverage monitoring for forest fire - Task allocation for UAV - Detection for nuclear and biochemical disaster - Search and rescue in an area - Searching for anti-terrorism explosives)	Unknown	High	Hard	Static and dynamic	Stochastic alg.	Metaheuristic alg.–swarm intelligence	- High ability to solve area coverage and dynamic environment - The algorithm can respond effectively to the sudden threat - Low computation - Robustness - scalability	- Lack of global communication. Between robots - May not apply to some situations	^[62]	2020
Bee search algorithm + local formation-building approach	- Area coverage for cleaning robots - Harvesting - Deactivating the area from radioactive substances - Disinfecting the area from viruses	Unknown	High	Hard	Static and dynamic	Stochastic alg.	Metaheuristic alg.–swarm intelligence	- Global solutions - A high ability to solve area coverage - Scalability - Simple and flexible - Low computation - Robustness - Use for orientation by leading robot and its neighbours	- It requires several strategies to provide controllers for the motion of robots	^[63]	2021
A bio-inspired neural network approach	Area coverage by swarm robot	Unknown	High	Simple	Dynamic	Stochastic alg.	Heuristic alg. and bioinspired alg.	- Reducing completion time - Robustness - Fault-tolerant	Not perfectly accurate	^[46]	2018
Cohort Intelligence (CI) methodology + perturbation technique	Area coverage for search and rescue by swarm robots	Unknown	High	Hard	Static and dynamic	Stochastic alg.	Nature-inspired Swarm Intelligence	- Robots will not get stuck in the non-convex region by using the perturbation technique	- In some situations, it needs many techniques to support it	^[47]	2020
Dynamic Voronoi-based algorithm + modified bacterial foraging optimisation (MBFO)	Area coverage searching problem in decentralised control of sensors of swarm robots	Known	Low	Simple	No obstacle	Deterministic + stochastic alg. (MBFO)	Classical algorithm (motion planning) + metaheuristic alg.–swarm intelligence	- Escape from local optimum - Quick search and saves energy for robots - Robots motion by following the gradient in the target area and the sensor range control on reactions between robots	- Consuming for computation time through the moving robots - Does not depend on physical robots to verify the performance	^[50]	2015
Decentralised Space-Based Potential Field (D-SBPF) algorithm	Area coverage for exploration by swarm robots - Motion planning for swarm robot	Unknown	Moderate	Simple	Static	Deterministic	Classical algorithm + (motion planning)	- Simple - Uniform - Decentralised - Disperse the group of robots to perform a quick search, using an effective method - The area was represented by a grid that was either attractive (if unexplored) or repulsive (if discovered), which led to enhancing the searching - The robots can leave/join/rejoin the group at any stage	- Decrease in the efficiency of coverage and speed when a few robots are used - Lower exploration performance for maps with complex geometry	^[51]	2015
The force vector algorithm	Area coverage by swarm robot	Known	Low	Simple	No obstacle	Deterministic	Classical algorithm	- Applies well to robot swarms with few requirements. - Effective area coverage - General solution - Simple to implement	- There are some constraints on used robots - It is not reliable like other algorithms - A secondary solution	^[52]	2016
The Cooperative Distributed Asynchronous (CORDA) model	Area coverage by swarm robot	Known	Low	Simple	Static/ no obstacle	Deterministic	Classical algorithm	- Famous and suitable compared to other available computational models for area coverage - Reduces system cost - Fault-tolerant	- Robot velocities affect this model under limited visibility - More powerful robots are required for areas with obstacles	^[64]	2017
Deployment Entropy with Potential Fields Strategy	Covers persistent areas by swarm robots for surveillance applications	Known	Low	Simple	Static	Deterministic	Classical algorithm	- A good spread of agents - Growing sensor coverage - Scalability - Decentralized system (more security) - More effective at generating a uniform group of distributed robots - Low computational complexity	- Lack of persistence results - The robot knows its position but does not know other robots’ positions in the group	^[65]	2020
ASelf-Organizing Area Coverage Method + Gradient and Grouping	Area coverage By swarm robot	Unknown	High	Hard	No obstacle	Deterministic	Classical algorithm	- Less completion time for coverage - Low computational cost - Robustness - Its parallel coverage led to speed covering an area - Very useful for the system of submillimetre swarm robots, which will be considered basically for micro-medicine	- The number of teams must be a manageable size - The robot coverage distance must be a reasonable value	^[53]	2021
Frontier-Led Swarming algorithm	Area coverage by swarm robot for exploration	Unknown	High	Hard	Static	Deterministic	Classical algorithm	- High performance for area coverage - Re-tuning of parameters of algorithm not needed to move the system to another environment - Covering an area, even if in cluttered environments and including unknown obstacles	- Not able to track changes in the environment (avoiding moving obstacles) - Does not search for optimal parameters of swarm robots	^[66]	2022

4. Mobile Robots Hardware Used for Swarm Robotics

The type of hardware used in swarm robots varies based on the coverage needs. The various types of hardware include cameras, controllers, actuators, and sensors ^[67]^[68]. Each component in the hardware is used to perform a specific task that assists in the functionality of each robot in the swarm ^[69]. Sensors are essential in facilitating information or data about the surrounding environment; mapping is called mapping. Swarm robots use sensors to analyse the topography of a domain by detecting key features, like land, roads, paths, and obstacles, among other feasible features ^[70]. The I.R. Proximity Sensor is the standard type of sensor that detects obstacles in swarm robots ^[71]. Controllers are essential to the swarm robot hardware ^[72]. Two approaches are used in controlling swarm robots, which include a centralised and decentralised control system ^[73].

In contrast, a distributed approach is used where each robot in the swarm plans and dictates its movement ^[74]. The controllers are fused with communication devices to enhance efficiency in covering a large area. Wireless devices like the Internet, Bluetooth, infrared, and LED lights are examples of communication devices that improve the functionality and movement of swarm robots ^[75].

Moreover, power is essential hardware in the swarm robot. Ideally, swarm robots are small; hence, ample power is needed to ensure that the device functions appropriately ^[76]. It is the role of the power supply unit to ensure that there is an optimal supply of power for the swarm robots. Most swarm robots use lithium batteries with a power voltage capacity of between five and twenty-five of direct current ^[77]. The batteries provide consistent and high-density voltage in small batteries that can be fitted in small swarm robots.

5. Applications for Swarm Robotics to Perform Area Coverage Tasks

There is a comprehensive application of swarm robots due to the increased reliability in adopting new technology in research and analysis over large open spaces and terrains to achieve area coverage. Swarm robots are increasingly used in various fields, such as the military, archaeology, and oil sectors. Today, geologists can visually represent the earth’s surface using three-dimensional imaging, using swarm robots. The result is the reduced cost of operations because it can take several hours to map a large area, which initially requires several days or weeks. In the military sector, the swarm application is used in intelligence collection. The military deploys a swarm of UAVs in various parts of the world to conduct reconnaissance simultaneously. Such capability provides the military with updated data and information that enhances readiness and the ability to respond to threats ^[78]. In the oil sector, swarm robots estimate the level and depth of oil spillage. Initially, it would take two-to-three weeks for oil companies to assess the scope of damage when oil spilt in the open seas. Today, oil companies are deploying swarm drones to assess damage from oil spills and suitable effective methods to be used to control the damage. Overall, there is continuous development and research in swarm robots that seek to enhance the integration and application of robots to enhance the quality of life ^[79].

Table 2 provides a thorough synopsis of swarm robotics’ many uses, down to the necessary algorithms, facets of coordination, centralization of the system, obstacles to be considered, hardware platforms, simulation environments, strengths, limits, and environmental settings. A multi-robot planner with poor coordination and no centralization, employing tiny, inexpensive cooperative robots, is the application’s focus within the framework of sustainable broad-acre farming. In spite of the system’s scalability and resilience, it cannot fix issues when robots fail because the environment is unpredictable ^[80]. Particle Swarm Optimization variations, when applied to small robots built from Parallax Shield-Bot hardware, show great coordination without centralization, making them ideal for source-seeking applications. While the method has limits due to physical constraints given by robots and the environment in a defined setting, its merits lie in the quick and affordable calculations and the fact that it escapes local optimal solutions.

Table 2. The specifications of each application.

Application	Algorithm	Application Domain	Coordination between Robots	Centralised System	Obstacle	Hardware	Simulation Platform/Controller	Strength	Limitation	Environment	Ref.	Year
Area coverage by swarm robots for sustainable broad-acre agriculture	The multi-robot planner	Task allocation and coordination	Weak	Y	Static	Small, low-cost cooperative robots (Gator T.E. (vehicle)	/	- Robustness - Scalability - Minimising the overlapping of areas - Increasing broad-acre agricultural productivity by low-cost robots	Does not adaptive to the problem if one robot failure	Unknown	^[80]	2015
Area coverage for source seeking	- Particle Swarm Optimisation with Inertia Weight - Particle Swarm Optimisation with Constriction Factor - Standard Particle Swarm Optimisation (SPSO)	Exploration	Strong	Y	Static and dynamic	Small robots modified from the Parallax Shield-Bot	Arduino	- The best and most convenient diversity of PSO is inertia weight PSO - Fast and inexpensive computations - Escape from local optimal solutions	- Physical constraints presented by both robots and the environment	Known	^[31]	2015
Area coverage for spraying a large field	Cooperative strategy by swarm robot	Task allocation and coordination	Strong	N	No obstacle	Many robots on a team	/	- Depends on their local information to produce a decision - Real robots can be applied successfully - Few computational needs	- All robots are working and participating at once - The distance between locations of two consecutive checkpoints must not exceed more than the discovery range of robots	Known	^[81]	2016
Area coverage of the testbed in an R-shape	Cooperative strategy by swarm robot	Aggregation	Moderate	Y	No obstacle	GRITSBot robots	Robotarium	- Low-cost - Safe - Flexible - Collision-avoidant - Fault-tolerant	Not applicable to some situations	Known	^[82]	2016
Area coverage to perform a collective map of the environment	Random walk algorithm (Brownian motion and Levy walk	Exploration And Mapping	Strong	Y	Static	e-puck	Arena	- Better for mapping in closed environments	- Not applicable in open environments - Not applicable in many actual robot experiments	Unknown	^[83]	2019
Area coverage for drawing a painting	A robotic painting system + Voronoi method	Task allocation	Strong	Y	No obstacle	Team of mobile robots	Robotarium	- The novelty of this method is represented by an external factor through the user (artist) affects the robot’s motion to paint specific colours - The end integration of the colours presents a result close to the user’s density specification	- Painting resources are limited, and this influences the resulting painting	known	^[84]	2019
Cleaning industrial environment	Multi-robotic dirt-cleaning algorithm+ Grid Divide Algorithm + A* Path-Planning Algorithm	Task allocation	Weak	Y	Static	iRobot	/	- Cleaning is enhanced by a swarm of robots rather than a single one, in both time-consuming and battery usage	-Does not handle the case of dynamic obstacles and replanning for path	Un known	^[85]	2020
Solving the non-convex area coverage problem	Visibility-based approach	Exploration	Strong	N	Static and dynamic	AmigoBot	/	- Determines the optimal direction motion for each robot, which influences efficiently solving the homing problem - Requires only local knowledge	- Does not work well for vast areas	Known	^[86]	2021
The management system of swarm robots in hospitals to decrease the risk to the doctors and medical staff, especially during the period of the COVID-19 pandemic	Management system by swarm robots	Exploration and task allocation	Weak	Y	Static and dynamic	Mobile bot		- Decreases the risk to the doctors and medical staff, especially during the period of the COVID-19 pandemic	- To perform extra functions, one must attach more equipment, such as an arm-like structure for medicine delivery	Unknown	^[87]	2021
Area coverage problems to find targets	Lévy flight strategy	Exploration and task allocation	Moderate	Y	Static and dynamic	Swarm crawler robots	Arduino	- Detecting the targets with a 100% success rate is significant indoors	Not exact/accurate results for the position of targets	Unknown	^[88]	2022

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