Area Coverage Task Research Articles

Multi-agent cooperative area coverage: A two-stage planning approach based on reinforcement learning

Multi-agent area coverage aims to accomplish the complete traversal of the target area through cooperation between agents. Focusing on the problems of low coverage efficiency and weak practicability in the existing methods, we propose a two-stage area coverage method based on multi-agent deep reinforcement learning. In the first stage, we convert the coverage path planning problem into an optimal grid selection problem, and according to the equivalence of agents in cooperative tasks, we propose a distributed coverage path planning algorithm based on QMIX and a grid coverage map. The second stage is to realize the cooperative navigation control in a constrained environment with obstacles and non-ideal communication conditions. To implement the stage, we design a hybrid attention mechanism to adaptively aggregate important feature information of adjacent agents and obstacles, which efficiently exploits the limited local perception and communication capabilities of agents to perform cooperative control. The experimental results show that the proposed two-stage multi-agent area coverage method can accomplish the area coverage task in the environment with random obstacles, and the area coverage efficiency and robustness are significantly better than other reinforcement learning based or traditional coverage algorithms. In addition, the results also verify that the proposed method has the advantage of adapting to the dynamic changes in the number of agents and the communication range.

A deep reinforcement learning based distributed multi-UAV dynamic area coverage algorithm for complex environment

Dynamic area coverage algorithm is often used in multiple unmanned aerial vehicle (multi-UAV) systems to realize searching or monitoring an area of interest (AOI). Improving coverage efficiency is one of the important research issues of dynamic area coverage. In this paper, we propose a distributed dynamic area coverage algorithm based on a reinforcement learning (RL) algorithm to enhance the coverage efficiency in complex application environments. A coverage information fusion based discrete soft actor–critic algorithm (herein FDSAC) is proposed to solve the non-stationary problem of RL algorithm in area coverage task under the communication constraint environment. Considering the equivalence of UAVs in the area coverage tasks, we introduce a multi-UAV cooperative learning method for FDSAC by sharing the strategy and experience of the UAVs to accelerate the learning speed of FDSAC in multi-UAV system. With the FDSAC algorithm, the UAVs can intelligently select the best coverage points and achieve almost optimal coverage point planning for the entire coverage process. In addition, we also provide a coverage point adjustment method based on the distribution of obstacles to enable the proposed algorithm adapt to obstacle environments. The experimental results demonstrate that the proposed algorithm can efficiently complete the area coverage task in complex communication and stochastic control noise environment. The good scalability, environment adaptability, and practicality of our algorithm have been also confirmed in different complex environments of different experimental platforms.

Inter-Reconfigurable Robot Path Planner for Double-Pass Complete Coverage Problem

Recent advancements in autonomous mobile robots have led to significant progress in area coverage tasks. However, challenges persist in optimizing the efficiency and computational complexity of complete coverage path planner (CCPP) algorithms for multi-robot systems, particularly in scenarios requiring revisiting or a double pass in specific locations, such as cleaning robots addressing spilled consumables. This paper presents an innovative approach to tackling the double-pass complete coverage problem using an autonomous inter-reconfigurable robot path planner. Our solution leverages a modified Glasius bio-inspired neural network (GBNN) to facilitate double-pass coverage through inter-reconfiguration between two robots. We compare our proposed algorithm with traditional multi-robot path planning in a centralized system, demonstrating a reduction in algorithm iterations and computation time. Our experimental results underscore the efficacy of the proposed solution in enhancing the efficiency of area coverage tasks. Furthermore, we discuss the implementation details and limitations of our study, providing insights for future research directions in autonomous robotics.

ПОТЕНЦИАЛ ГЕНЕТИЧЕСКИХ АЛГОРИТМОВ В ЗАДАЧАХ ПОКРЫТИЯ ТЕРРИТОРИИ ГРУППОЙ БЛА

Unmanned aerial vehicles (UAVs) have become key tools in automated area coverage tasks due to the fact that they are not limited by obstacles on the ground and are able to quickly complete tasks. However, when planning routes for UAVs covering an area, difficulties arise in finding optimal solutions due to the computational complexity of the problem. Therefore, when solving problems with a group of UAVs over large areas, heuristic algorithms are used to find coverage paths that are close to optimal. one popular algorithm for that is the genetic algorithm. The article studies the potential of using genetic algorithms in the context of solving the problem of covering an area using a group of UAVs. The article provides a review of methods for developing and optimizing modified genetic algorithms that take into account the unique features of the coverage problem. The characteristics of the genetic algorithm and the representation of chromosomes when solving the coverage problem are analyzed, depending on the different types of representation of territories. The article considers features of applying genetic operations to chromosomes that reflect the trajectory of the UAV, as well as the features of the task when working with a group of UAVs. In addition, it also discusses collision avoidance techniques in UAV swarm missions, their advantages and limitations.

Multi-UAV Airborne UV Area Coverage and Task Assignment Method

To address the area coverage problem in large-scale reconnaissance and detection tasks, Unmanned Aerial Vehicles(UAVs) utilize Ultraviolet(UV) light for flight guidance. Additionally, a UV light power control method is implemented to assist UAVs in adapting to various terrains. Based on the flight characteristics of quadrotor UAVs, the target area is divided into a grid-like format. This transformation helps convert the area coverage task into a multi-UAV path planning problem on a raster map. To address this, constraints are established for assigning tasks to multiple UAVs. An algorithm is proposed to assign the target area uniformly based on the initial position of the UAV, ensuring full coverage. Additionally, the algorithm considers the differences in endurance among multiple UAVs. This improved algorithm is highly suitable for real-life application scenarios. The simulation results demonstrate the power control changes in wireless UV-guided UAV flight. Moreover, the assignment results of the improved algorithm align closely with the theoretical assignment results, considering different parameters like the number of UAVs, initial position, and endurance capacity.

UAV Cluster Mission Planning Strategy for Area Coverage Tasks.

In the context of area coverage tasks in three-dimensional space, unmanned aerial vehicle (UAV) clusters face challenges such as uneven task assignment, low task efficiency, and high energy consumption. This paper proposes an efficient mission planning strategy for UAV clusters in area coverage tasks. First, the area coverage search task is analyzed, and the coverage scheme of the task area is determined. Based on this, the cluster task area is divided into subareas. Then, for the UAV cluster task allocation problem, a step-by-step solution is proposed. Afterward, an improved fuzzy C-clustering algorithm is used to determine the UAV task area. Furthermore, an optimized particle swarm hybrid ant colony (PSOHAC) algorithm is proposed to plan the UAV cluster task path. Finally, the feasibility and superiority of the proposed scheme and improved algorithm are verified by simulation experiments. The simulation results show that the proposed method achieves full coverage of the task area and efficiently completes the task allocation of the UAV cluster. Compared with related comparison algorithms, the method proposed in this paper can achieve a maximum improvement of 21.9% in balanced energy consumption efficiency for UAV cluster task search planning, and the energy efficiency of the UAV cluster can be improved by up to 7.9%.

Optimal Morphologies of n-Omino-Based Reconfigurable Robot for Area Coverage Task Using Metaheuristic Optimization

Reconfigurable robots design based on polyominos or n-Omino is increasingly being explored in cleaning and maintenance (CnM) tasks due to their ability to change shape using intra- and inter-reconfiguration, resulting in various footprints of the robot. On one hand, reconfiguration during a CnM task in a given environment or map results in enhanced area coverage over fixed-form robots. However, it also consumes more energy due to the additional effort required to continuously change shape while covering a given map, leading to a deterioration in overall performance. This paper proposes a new strategy for n-Omino-based robots to select a range of optimal morphologies that maximizes area coverage and minimizes energy consumption. The optimal “morphology” is based on two factors: the shape or footprint obtained by varying the angles between the n-Omino blocks and the number of n-Omino blocks, i.e., “n”. The proposed approach combines a Footprint-Based Complete coverage Path planner (FBCP) with a metaheuristic optimization algorithm to identify an n-Omino-based reconfigurable robot’s optimal configuration, assuming energy consumption is proportional to the path length taken by the robot. The proposed approach is demonstrated using an n-Omino-based robot named Smorphi, which has square-shaped omino blocks with holonomic locomotion and the ability to change from monomino to tetromino. Three different simulated environments are used to find the optimal morphologies of Smorphi using three metaheuristic optimization techniques, namely, MOEA/D, OMOPSO, and HypE. The results of the study show that the morphology produced by this approach is energy efficient, minimizing energy consumption and maximizing area coverage. Furthermore, the HypE algorithm is identified as more efficient for generating optimal morphology as it took less time to converge than the other two algorithms.

Fixed-Time Coverage Control of Mobile Robot Networks Considering the Time Cost Metric.

In this work, we studied the area coverage control problem (ACCP) based on the time cost metric of a robot network with an input disturbance in a dynamic environment, which was modeled by a time-varying risk density function. A coverage control method based on the time cost metric was proposed. The area coverage task that considers the time cost consists of two phases: the robot network is driven to cover the task area with a time-optimal effect in the first phase; the second phase is when the accident occurs and the robot is driven to the accident site at maximum speed. Considering that there were movable objects in the task area, a time-varying risk density function was used to describe the risk degree at different locations in the task area. In the presence of the input disturbance, a robust controller was designed to drive each robot, with different maximum control input values, to the position that locally minimized the time cost metric function in a fixed time, and the conditions for maximum control input were obtained. Finally, simulation results and comparison result are presented in this paper.

A Static Area Coverage Algorithm for Heterogeneous AUV Group Based on Biological Competition Mechanism.

Static area coverage of the heterogeneous autonomous underwater vehicle (AUV) group is widely used in many fields. With the use of the centroidal Voronoi tessellation (CVT) algorithm, the coverage problem can be resolved. However, the CVT method, which is optimized with the location evaluation function, cannot consider the heterogeneity of AUVs when the group performs the static area coverage task and will cause a waste of resources. In this paper, considering different AUVs’ task requirements and detection capabilities comprehensively, we propose a coverage control optimization algorithm based on a biological competition mechanism (BCM). By using BCM, the task load of each AUV can be distributed consistently. In addition, we provide strict proof of the consistency of the algorithm based on the Lyapunov method. Simulation results demonstrate that with the proposed algorithm, the location distribution of the heterogeneous AUV group for area coverage is close to the balanced value, and the performance is better than the CVT algorithm for static area coverage.

Global Optimization of UAV Area Coverage Path Planning Based on Good Point Set and Genetic Algorithm

When performing area coverage tasks in some special scenarios, fixed-wing aircraft conventionally adopt the scan-type of path planning, where the distance between two adjacent tracks is usually less than the minimum turning radius of the aircraft. This results in increased energy consumption during turning between adjacent tracks, which means a reduced task execution efficiency. To address this problem, the current paper proposes an area coverage path planning method for a fixed-wing unmanned aerial vehicle (UAV) based on an improved genetic algorithm. The algorithm improves the primary population generation of the traditional genetic algorithm, with the help of better crossover operator and mutation operator for the genetic operation. More specifically, the good point set algorithm (GPSA) is first used to generate a primary population that has a more uniform distribution than that of the random algorithm. Then, the heuristic crossover operator and the random interval inverse mutation operator are employed to reduce the risk of local optimization. The proposed algorithm is verified in tasks with different numbers of paths. A comparison with the conventional genetic algorithm (GA) shows that our algorithm can converge to a better solution.

Multi-UAV Area Coverage Based on Relative Localization: Algorithms and Optimal UAV Placement.

This paper is concerned with relative localization-based optimal area coverage placement using multiple unmanned aerial vehicles (UAVs). It is assumed that only one of the UAVs has its global position information before performing the area coverage task and that ranging measurements can be obtained among the UAVs by using ultra-wide band (UWB) sensors. In this case, multi-UAV relative localization and cooperative coverage control have to be run simultaneously, which is a quite challenging task. In this paper, we propose a single-landmark-based relative localization algorithm, combined with a distributed coverage control law. At the same time, the optimal multi-UAV placement problem was formulated as a quadratic programming problem by compromising between optimal relative localization and optimal coverage control and was solved by using Sequential Quadratic Programming (SQP) algorithms. Simulation results show that our proposed method can guarantee that a team of UAVs can efficiently localize themselves in a cooperative manner and, at the same time, complete the area coverage task.

Active SLAM for Mobile Robots With Area Coverage and Obstacle Avoidance

In this article, we present an active simultaneous localization and mapping (SLAM) framework for a mobile robot to obtain a collision-free trajectory with good performance in SLAM uncertainty reduction and in an area coverage task. Based on a model predictive control framework, these two tasks are solved by the introduction of a control switching mechanism. For SLAM uncertainty reduction, graph topology is used to approximate the original problem as a constrained nonlinear least squares problem. A convex half-space representation is applied to relax nonconvex spatial constraints that represent obstacle avoidance. Using convex relaxation, the problem is solved by a convex optimization method and a rounding procedure based on singular value decomposition. The area coverage task is addressed with a sequential quadratic programming method. A submap joining approach, called linear SLAM, is used to address the associated challenges of avoiding local minima, minimizing control switching, and potentially high computational cost. Finally, various simulations and experiments using an aerial robot are presented that verify the effectiveness of the proposed method, showing that our method produces a more accurate SLAM result and is more computationally efficient compared with multiple existing methods.

Complete coverage path planning using reinforcement learning for Tetromino based cleaning and maintenance robot

Tiling robotics have been deployed in autonomous complete area coverage tasks such as floor cleaning, building inspection, and maintenance, surface painting. One class of tiling robotics, polyomino-based reconfigurable robots, overcome the limitation of fixed-form robots in achieving high-efficiency area coverage by adopting different morphologies to suit the needs of the current environment. Since the reconfigurable actions of these robots are produced by real-time intelligent decisions during operations, an optimal path planning algorithm is paramount to maximize the area coverage while minimizing the energy consumed by these robots. This paper proposes a complete coverage path planning (CCPP) model trained using deep blackreinforcement learning (RL) for the tetromino based reconfigurable robot platform called hTetro to simultaneously generate the optimal set of shapes for any pretrained arbitrary environment shape with a trajectory that has the least overall cost. To this end, a Convolutional Neural Network (CNN) with Long Short Term Memory (LSTM) layers is trained using Actor Critic Experience Replay (ACER) reinforcement learning algorithm. The results are compared with existing approaches which are based on the traditional tiling theory model, including zigzag, spiral, and greedy search schemes. The model is also compared with the Travelling salesman problem (TSP) based Genetic Algorithm (GA) and Ant Colony Optimization (ACO) schemes. The proposed scheme generates a path with lower cost while also requiring lesser time to generate it. The model is also highly robust and can generate a path in any pretrained arbitrary environments.

Area Coverage by a Group of UAVs using the Broadcast Control Framework

Abstract This paper proposes a scheme for distributing a group of Unmanned Aerial Vehicles (UAVs) in an environment using Broadcast Control (BC) algorithm. BC is a recent technique that uses stochastic optimization to control a multi-agent system for accomplishing a global task. BC’s variant, the Pseudo-perturbation based BC (PBC), uses model based predicted decision and shows faster convergence than BC. In this paper, we have modified the existing BC schemes by incorporating relevant constraints to consider physical limitations of the agents. The proposed scheme is applied to a group of UAVs having kinematic constraints for an area coverage task. Results from numerical simulation show that the performance of BC methods are maintained, and more importantly, the designed path constraints for the UAVs produces a less erratic, continuous and realistic travel path as the UAVs fly to reach their goal.

Swarm Robotic Systems Based on Collective Behavior of Chloroplasts

[abstFig src='/00290003/15.jpg' width='300' text='Swarming chloroplastic robots around light source' ] In this paper, distributed autonomous robots are used to perform area coverage tasks. In order for robots to cover the ground surface of environments, the coordination of a team of robots is a challenge. For this challenge, we present bio-inspired swarm robotic systems. We focus on the collective behavior of chloroplasts toward a light source. On the basis of the mechanism of the chloroplast, we propose robot behavior models that do not use local communication. The emergence of cooperative behavior through the interaction among the swarming robots is a main contribution of this paper. Based on simulation results, the effectiveness of the chloroplastic robots for the coverage task is discussed in terms of flexibility and scalability. Furthermore, the behavioral models are applied to actual mobile robots. Based on the results of experiments, the applicability of the chloroplastic robots to real environments is discussed. As an application of the swarm robotic system, a specific task, sweeping, is given to actual chloroplastic robots.

Symmetry and reduction in collectives: low-dimensional cyclic pursuit.

We investigate low-dimensional examples of cyclic pursuit in a collective, wherein each agent employs a constant bearing (CB) steering law relative to exactly one other agent. For the case of three agents in the plane, we characterize relative equilibria and pure shape equilibria of associated closed-loop dynamics. Re-scaling time yields a reduction of phase space to two dimensions and effective tools for stability analysis. Study of bifurcation of a family of collinear equilibria dependent on a single CB control parameter reveals the presence of a rich collection of trajectories that are periodic in shape and undergo precession in physical space. For collectives in three dimensions, with an appropriate notion of CB pursuit strategy and corresponding steering law, the two-agent case proves to be explicitly integrable. These results suggest control schemes for small teams of mobile robotic agents engaged in area coverage tasks such as search and rescue, and raise interesting possibilities for behaviour in biological contexts.