A Survey on Swarm Robotics for Area Coverage Problem

We present a number of breakthroughs for coordinated motion planning, in which the objective is to reconfigure a swarm of labeled convex objects by a combination of parallel, continuous, collision-free translations into a given target arrangement. Problems of this type can be traced back to the classic work of Schwartz and Sharir (1983), who gave a method for deciding the existence of a coordinated motion for a set of disks between obstacles; their approach is polynomial in the complexity of the obstacles, but exponential in the number of disks. Despite a broad range of other non-trivial results for multi-object motion planning, previous work has largely focused on sequential schedules, in which one robot moves at a time, with objectives such as the number of moves; attempts to minimize the overall makespan of a coordinated parallel motion schedule (with many robots moving simultaneously) have defied all attempts at establishing the complexity in the absence of obstacles, as well as the existence of efficient approximation methods. We resolve these open problems by developing a framework that provides constant-factor approximation algorithms for minimizing the execution time of a coordinated, parallel motion plan for a swarm of robots in the absence of obstacles, provided their arrangement entails some amount of separability. In fact, our algorithm achieves constant stretch factor: If all robots want to move at most d units from their respective starting positions, then the total duration of the overall schedule (and hence the distance traveled by each robot) is O(d). Various extensions include unlabeled robots and different classes of robots. We also resolve the complexity of finding a reconfiguration plan with minimal execution time by proving that this is NP-hard, even for a grid arrangement without any stationary obstacles. On the other hand, we show that for densely packed disks that cannot be well separated, a stretch factor Omega(N^{1/4}) may be required. On the positive side, we establish a stretch factor of O(N^{1/2}) even in this case. The intricate difficulties of computing precise optimal solutions are demonstrated by the seemingly simple case of just two disks, which is shown to be excruciatingly difficult to solve to optimality.

This work proposes a control law for efficient area coverage and pop-up threat detection by a robot swarm inspired by the dynamical behavior of ant colonies foraging for food. In the first part, performance metrics that evaluate area coverage in terms of characteristics such as rate, completeness and frequency of coverage are developed. Next, the Keller–Segel model for chemotaxis is adapted to develop a virtual-pheromone-based method of area coverage. Sensitivity analyses with respect to the model parameters such as rate of pheromone diffusion, rate of pheromone evaporation, and white noise intensity then identify and establish noise intensity as the most influential parameter in the context of efficient area coverage and establish trends between these different parameters which can be generalized to other pheromone-based systems. In addition, the analyses yield optimal values for the model parameters with respect to the proposed performance metrics. A finite resolution of model parameter values were tested to determine the optimal one. In the second part of the work, the control framework is expanded to investigate the efficacy of non-Brownian search strategies characterized by Levy flight, a non-Brownian stochastic process which takes variable path lengths from a power-law distribution. It is shown that a control law that incorporates a combination of gradient following and Levy flight provides superior area coverage and pop-up threat detection by the swarm. The results highlight both the potential benefits of robot swarm design inspired by social insect behavior as well as the interesting possibilities suggested by considerations of non-Brownian noise.

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.

In recent years, research has been conducted on swarm robot systems in which multiple autonomous mobile robots cooperate to perform tasks. Swarm robot systems are expected to perform high functionality as a group by cooperating with each other, in spite of the limited capabilities of the individual robots. This paper explores a method of simplifying swarm robot controllers as much as possible for swarm robot navigation. If we can achieve autonomous navigation of swarm robots to a target area with minimal resource consumption, they only need to implement the task execution function in that area. This leads to lower costs for swarm robot design and more efficient system architecture design. To address the above aims, this study focuses on heterogeneity. Specifically, we introduce a navigator robot that indirectly guides swarm robots named the worker robots. Heterogeneity in this paper refers to the worker robots and the navigator robots. We design the interaction between the navigator robot and the worker robots to provide a system that guides the worker robots to the destination.

The area coverage problem is an important issue because it can minimize uncertainty in a complex and unknown environment. This paper adopts a distributed anti-flocking algorithm inspired by the social behavior of solitary animals, which enables self-organized collaboration of multiple unmanned aerial vehicles (multi-UAVs) to achieve effective coverage of the mission area. The designed area coverage map represents the historical coverage information of each unmanned aerial vehicle (UAV). According to the rules of collision avoidance, decentering, and selfishness in the distributed anti-flocking algorithm, the UAVs are guided to move towards the direction that maximizing the coverage area and try to reduce the overlap of coverage region as well. Simulations show that the algorithm can achieve approximate full coverage of the task area and has good scalability, adaptability, and robustness.

We consider the problem of dynamically adjusting the formation and size of robot teams performing distributed area coverage, when they encounter obstacles or occlusions along their path. Based on our earlier formulation of the robotic team formation problem as a coalitional game called a weighted voting game (WVG), we show that the robot team size can be dynamically adapted by adjusting the WVG’s quota parameter. We use a Q-learning algorithm to learn the value of the quota parameter and a policy reuse mechanism to adapt the learning process to changes in the underlying environment. Experimental results using simulated e-puck robots within the Webots simulator show that our Q-learning algorithm converges within a finite number of steps in different types of environments. Using the learning algorithm also improves the performance of an area coverage application where multiple robot teams move in formation to explore an initially unknown environment by 5 − 10%.

The ability of automated vehicles to avoid obstacles on the road is one of the most critical functionalities, as it directly impacts passenger safety. The trajectory planning functionality plays a key role in this task, as it uses data from sensors to identify obstacles and plan a free obstacle path. Thus, this paper proposes a trajectory planning method based on a model predictive control (MPC) strategy that takes into account both static and dynamic obstacles. Additionally, a trajectory tracking controller based on a linear quadratic regulator (LQR) algorithm is designed to ensure the vehicle follows the planned path. To evaluate the proposed control solution for the lateral dynamics of the vehicle, a traffic scenario involving multiple obstacles (both static and dynamic) is used. The results show that the proposed solution effectively controls the vehicle's lateral dynamics and successfully avoids collisions with obstacles.

We present a reactive base control method that enables high performance mobile manipulation on-the-move in real-world environments with static and dynamic obstacles. Performing manipulation tasks while the mobile base remains in motion can significantly decrease the time required to perform multi-step tasks, as well as improve the gracefulness of the robot's motion. Existing approaches to manipulation on-the-move either ignore the obstacle avoidance problem or rely on the execution of planned trajectories, which is not suitable in environments with dynamic objects and obstacles. The presented controller addresses both of these deficiencies and demonstrates robust performance of pick-and-place tasks in dynamic environments. The performance is evaluated on several simulated and real-world tasks. On a mobile manipulation task with static obstacles, we outperform an existing method by 41% in terms of total task time. Further, we present real-world examples of our robot performing manipulation tasks on-the-move while avoiding a second autonomous robot in the workspace.

During the mission of the USV (Unmanned Surface Vehicle), there may be some static or dynamic obstacles on the pre-planned track that are not displayed on the ground station. To avoid these obstacles in time, a model of DCPA (distance to the closest point of approach) between the USV and the obstacle is proposed, which is used to plan the speed of the USV, and adaptively accelerate and decelerate can safely avoid dynamic obstacles. We solved the problem of target unreachable and heading change optimization by planning the heading at IAPF (improved artificial potential field) in the process of sailing, which can effectively avoid static obstacles and respond to collision avoidance in emergencies. The collision avoidance planning outputs a set of behavior vectors (speed, heading), and hands it over to the USV controller to control the navigation of the USV. In this study, an adaptive speed and heading planning method is used to improve the collision avoidance ability of the USV in the mixed scene of dynamic and static obstacles. Through multiple launching experiments, the actual collision avoidance effect of the method is verified, and the safety and reliability of the USV application requirements are met.

COLREGs-abiding hybrid collision avoidance algorithm based on deep reinforcement learning for USVs

A current trend in robotics is fusing different types of sensors having different characteristics to improve the performance of a robot system and also benefit from the reduced cost of sensors. One type of robot that requires sensor fusion for its application is the service robot. To achieve better performance, several service robots are preferred to work together, and, hence, this paper concentrates on swarm service robots. Swarm service mobile robots operating within a fixed area need to cope with dynamic changes in the environment, and they must also be capable of avoiding dynamic and static obstacles. This study applies sensor fusion and swarm concept for service mobile robots in human services and rehabilitation environment. The swarm robots follow the human moving trajectory to provide support to human moving and perform several tasks required in their living environment. This study applies a reference control and proportional-integral (PI) control for the obstacle avoidance function. Various computer simulations are performed to verify the effectiveness of the proposed method.

This paper investigates how we can work towards building net-centric swarms of land, sea and air robots working together to accomplish a common goal. The goal is to bring together swarms of robots from all three sectors to safely benefit mankind without increasing dangers in the process. Several platforms are explored for simulation to investigate swarm robotics within heterogeneous environments.

Area coverage and Navigation are two fundamental requirements for robot applications. When multiple robots are fielded in a scene, coordination through communication becomes a natural pre-requisite. This paper focuses on the area coverage problem and proposes periodic exchange of state information related to location and coverage as a solution. The solution is based on pseudonet communication architecture that enables exchange of messages between the robots by setting up a Bluetooth piconet or scatternet and maintaining the same throughout the process of covering the area.

This article examines the experimental issues associated with the area coverage problem using a network of mobile robots. The developed schemes in the literature assume an unrealistic perfect knowledge of each point-robot's position. To account for the imprecise robots' localization, the covered space partitioning relies on the Guaranteed Voronoi (GV) principle, under the assumption of uniform radial sensing for each agent. The distributed nature of the developed algorithm assigns to each robot a GV-cell which is bounded by hyperbolic arcs. The only required information for the implementation of this scheme is the relative positioning from each robot to its GV-Delaunay neighbors. Simulation and experimental studies are offered to highlight the efficiency of the proposed distributed experimental-oriented control law.

Swarm robots are a group of macro robots which are controlled by an actual leader or virtual leader from that group. When the world is moving into the nano technology, its mandatory to upgrade our robotic system into the nano or mini robotic systems. But the nano robots are limited with performing the complex task. To overcome this limitations, group of nano robots are operated in such a way to perform the complex operations. The main discussion in this paper is a small overview on the swarm robotics. Present situations researchers are facing multiple problems and some of them are found some solutions but mostly swarm robotics are having more problems which are needs to be addressed. The main predicament in swarm robotics is that swarm control and unavailability of coordinated algorithm for a particular application. This paper helps the researchers to understand effectively about the swarm robotics.