SUMMARY: In this paper we address the problem of coverage path planning (CPP) for multiple cooperating mobile robots. We use a ‘partition and cover’ approach using Voronoi partition to achieve natural passive cooperation between robots to avoid task duplicity. We combine two generalizations of Voronoi partition, namely geodesic-distance-based Voronoi partition and Manhattan-distance-based Voronoi partition, to address contiguity of partition in the presence of obstacles and to avoid partition-boundary-induced coverage gap. The region is divided into 2D×2Dgrids, whereDis the size of the robot footprint. Individual robots can use any of the single-robot CPP algorithms. We show that with the proposed Geodesic-Manhattan Voronoi-partition-based coverage (GM-VPC), a complete and non-overlapping coverage can be achieved at grid level provided that the underlying single-robot CPP algorithm has similar property.We demonstrated using two representative single-robot coverage strategies, namely Boustrophedon-decomposition-based coverage and Spanning Tree coverage, first based on so-called exact cellular decomposition and second based on approximate cellular decomposition, that the proposed partitioning scheme completely eliminates coverage gaps and coverage overlaps. Simulation experiments using Matlab and V-rep robot simulator and experiments with Fire Bird V mobile robot are carried out to validate the proposed coverage strategy.

Coverage Path Planning (CPP) is a fundamental aspect of mobile robotics, enabling robots to navigate dynamic environments efficiently while ensuring thorough coverage of all areas of interest and avoiding obstacles. CPP is pivotal in diverse applications, from everyday tasks like vacuum cleaning, lawn mowing, and window cleaning to specialized operations such as demining hazardous areas, autonomous underwater imaging, and inspecting complex structures. This survey reviews recent advancements in CPP, focusing on algorithms and methodologies tailored for dynamic environments. It highlights the challenges posed by environmental variability, obstacle dynamics, and real-time computational demands. By synthesizing insights from existing research, this survey aims to guide future developments in CPP, paving the way for smarter and more adaptive robotic systems capable of handling the complexities of real-world scenarios.

The ascent phase trajectory optimization of a single stage liquid propellant hypersonic launch vehicle is considered in this paper. Trajectory optimization is done to achieve desired terminal conditions using angle of attack as a control variable. The formulation entails nonlinear 2-dimensional launch vehicle flight dynamics with mixed boundary conditions and multi-constraints. The burning time of the liquid rocket engine is fixed, leading to fixed time problem. By studying the behavior of the problem with boundary constraints, the non-linear problem is solved using the PSO method and the optimal trajectory is obtained. The effect of two types of dynamic inertia weight and constant inertia weight in PSO algorithm is examined. The influence of controlling parameters is also investigated

In this paper, a robust online Multi-robot Simultaneous exploration and coverage path planning problem is presented. The entire workspace is initially partitioned using a variant of Voronoi partitioning,Manhattan Voronoi, and the robots execute simultaneous exploration and coverage using Spanning Tree Coverage algorithm and cover the workspace. Once the robot(s) failure is detected the uncovered portions of the Voronoi cell of the failed robot will be shared between other eligible robots or a replacement strategy,if available, is performed. Simulation experiments within the V-rep environment is used to demonstrate and validate the performance of the proposed algorithm. Though the authors used the Spanning Tree Coverage algorithm for path planning for the purpose of demonstration, any suitable coverage algorithm may be used.

The growing demands of next-generation electric and hybrid electric vehicles and high-power electronic devices necessitate higher power density, longer cycle life, and enhanced safety at a reduced cost. To address these challenges, supercapacitors have emerged as a potential technology offering several advantages such as higher power density, excellent cycle stability, environmental friendliness, and wide temperature-range performance. Recently, research has focused on developing nanomaterials that would improve the capacitive performance of supercapacitors. Graphitic carbon nitride (g-CN or g-C₃N₄) exhibits distinct chemical and physical characteristics that are advantageous for diverse applications including energy conversion and storage. g-CN integrates the benefits of nitrogen doping, such as increased surface polarity and better surface wettability, with the advantages of carbon compounds, such as ease of availability, abundance in nature, and cost efficiency. The considerable advance in research on g-CN has inspired the development of various g-CN nanocomposites to achieve high efficiency by eliminating certain limitations. To overcome the issues related to conductivity and specific surface area, g-CN can be composited with conducting polymers (CP) as one of the modification strategies. Recently researchers have experimented with various g-CN-conducting polymer nanocomposites as electrode materials for supercapacitors. Based on the studies conducted, g-CN-conducting polymer nanocomposites have achieved good stability, adequate conductivity, and better specific capacitance. This review provides an overview of g-CN/conducting polymer nanocomposites as supercapacitor electrode materials. It covers synthetic strategies, discusses factors affecting their electrochemical performance, and outlines future research directions for high-performance supercapacitors.