In concrete troweling for building construction, robots can significantly reduce workload and improve automation level. However, as a primary task of coverage path planning (CPP) for troweling, delimitating area of interest (AOI) in complex scenes is still challenging, especially for swing-arm robots with more complex working modes. Thus, this research proposes an algorithm to delimitate AOI for swing-arm troweling robot (SatAOI algorithm). By analyzing characteristics of the robot and obstacle maps, mathematical models and collision principles are established. On this basis, SatAOI algorithm achieves AOI delimitation by global search and collision detection. Experiments on different obstacle maps indicate that AOI can be effectively delimitated in scenes under different complexity, and the algorithm can fully consider the connectivity of obstacle maps. This research serves as a foundation for CPP algorithm and full process simulation of swing-arm troweling robots.

Multi-robot Coverage Path Planning (MCPP) addresses the problem of computing paths for multiple robots to effectively cover a large area of interest. Conventional approaches to MCPP typically assume that robots move at fixed velocities, which is often unrealistic in real-world applications where robots must adapt their speeds based on the specific coverage tasks assigned to them.Consequently, conventional approaches often lead to imbalanced workload distribution among robots and increased completion time for coverage tasks. To address this, we introduce a novel Multi-robot Dynamic Coverage Path Planning (MDCPP) algorithm for complete coverage in two-dimensional environments. MDCPP dynamically estimates each robot's remaining workload by approximating the target distribution with Gaussian mixture models, and assigns coverage regions using a capacity-constrained Voronoi diagram. We further develop a distributed implementation of MDCPP for range-constrained robotic networks. Simulation results validate the efficacy of MDCPP, showing qualitative improvements and superior performance compared to an existing sweeping algorithm, and a quantifiable impact of communication range on coverage efficiency.

Efficient coordination of multiple robots for coverage of large, unknown environments is a significant challenge that involves minimizing the total coverage path length while reducing inter-robot conflicts. In this paper, we introduce a Multi-robot Connectivity-Aware Planner (Multi-CAP), a hierarchical coverage path planning algorithm that facilitates multi-robot coordination through a novel connectivity-aware approach. The algorithm constructs and dynamically maintains an adjacency graph that represents the environment as a set of connected subareas. Critically, we make the assumption that the environment, while unknown, is bounded. This allows for incremental refinement of the adjacency graph online to ensure its structure represents the physical layout of the space, both in observed and unobserved areas of the map as robots explore the environment. We frame the task of assigning subareas to robots as a Vehicle Routing Problem (VRP), a well-studied problem for finding optimal routes for a fleet of vehicles. This is used to compute disjoint tours that minimize redundant travel, assigning each robot a unique, non-conflicting set of subareas. Each robot then executes its assigned tour, independently adapting its coverage strategy within each subarea to minimize path length based on real-time sensor observations of the subarea. We demonstrate through simulations and multi-robot hardware experiments that Multi-CAP significantly outperforms state-of-the-art methods in key metrics, including coverage time, total path length, and path overlap ratio. Ablation studies further validate the critical role of our connectivity-aware graph and the global tour planner in achieving these performance gains.

Abstract-- Modern coverage path planning (CPP) for holo-nomic UA Vs in emergency response must contend with diverse environments where regions of interest (ROIs) often take the form of highly irregular polygons, characterized by asymmetric shapes, dense clusters of concavities, and multiple internal holes. Modern CPP pipelines typically rely on decomposition strategies that overfragment such polygons into numerous subregions. This increases the number of sweep segments and connectors, which in turn adds inter-region travel and forces more frequent reorientation. These effects ultimately result in longer completion times and degraded trajectory quality. We address this with a decomposition strategy that applies a recursive dual-axis monotonicity criterion with cuts guided by a cumulative gap severity metric. This approach distributes clusters of concavities more evenly across subregions and produces a minimal set of partitions that remain sweepable under a parallel-track maneuver . We pair this with a global optimizer that jointly selects sweep paths and inter-partition transitions to minimize total path length, transition overhead, and turn count. We demonstrate that our proposed approach achieves the lowest mean overhead in path length and completion time across 13 notable CPP pipelines. Coverage Path Planning (CPP) generates trajectories that fully observe a Region of Interest (ROI) with minimal cost, typically measured in path length, turns, execution time, or energy use. A dominant strategy in aerial and ground robotics is parallel-track coverage, where back-and-forth sweeps (e.g., boustrophedon) tile the region. For convex ROIs, selecting an optimal sweep direction is straightforward.

Automatic Section Control (ASC) is a long-standing trend for spraying in agriculture. It promises to minimise spray overlap areas. The core idea is to (i) switch off spray nozzles on areas that have already been sprayed, and (ii) to dynamically adjust nozzle flow rates along the boom bar that holds the spray nozzles when velocities of boom sections vary during turn maneuvers. ASC is not possible without sensors for accurate positioning data. Spraying and the movement of modern wide boom bars are highly dynamic processes. In addition, many uncertainty factors have an effect such as cross wind drift, nozzle clogging in open-field conditions, etc. In view of this complexity, the natural question arises if a simpler alternative exist. Therefore, ASC is compared to a proposed simpler one- or two-sections alternative that uses predictive spray switching. The comparison is provided under nominal conditions. Agricultural spraying is intrinsically linked to area coverage path planning and spray switching logic. Combinations of two area coverage path planning and switching logics as well as 3 sections-setups are compared. The three sections-setups differ by controlling 48 sections, 2 sections or controlling all nozzles uniformly with the same control signal as one single section. Methods are evaluated on 10 diverse real-world field examples, including non-convex field contours, freeform mainfield lanes and multiple obstacle areas. An economic cost analysis is provided to compare the methods. A preferred method is suggested that (i) minimises area coverage pathlength, (ii) offers intermediate overlap, (iii) is suitable for manual driving by following a pre-planned predictive spray switching logic for an area coverage path plan, and (iv) and in contrast to ASC can be implemented sensor-free and at low cost. Surprisingly strong economic arguments are found to not recommend ASC for small farms.

-- Multi-UA V Coverage Path Planning (mCPP) algorithms in popular commercial software typically treat a Region of Interest (RoI) only as a 2D plane, ignoring important 3D structure characteristics. This leads to incomplete 3D reconstructions, especially around occluded or vertical surfaces. In this paper, we propose a modular algorithm that can extend commercial two-dimensional path planners to facilitate terrain-aware planning by adjusting altitude and camera orientations. T o demonstrate it, we extend the well-known DARP (Divide Areas for Optimal Multi-Robot Coverage Path Planning) algorithm and produce DARP-3D. Compared to baseline, our approach consistently captures improved 3D reconstructions, particularly in areas with significant vertical features. An open-source implementation of the algorithm is available here: https://github.com/konskara/T

In many applications, including underwater robotics, the coverage problem requires an autonomous vehicle to systematically explore a defined area while minimizing redundancy and avoiding obstacles. This paper investigates coverage path planning strategies to enhance the efficiency of underwater gliders, particularly in maximizing the probability of detecting a radioactive source while ensuring safe navigation. We evaluate three path-planning approaches: the Traveling Salesman Problem (TSP), Minimum Spanning Tree (MST), and Optimal Control Problem (OCP). Simulations were conducted in MATLAB, comparing processing time, uncovered areas, path length, and traversal time. Results indicate that OCP is preferable when traversal time is constrained, although it incurs significantly higher computational costs. Conversely, MST-based approaches provide faster but less optimal solutions. These findings offer insights into selecting appropriate algorithms based on mission priorities, balancing efficiency and computational feasibility.

Efficient Coverage Path Planning (CPP) is necessary for autonomous robotic lawnmowers to effectively navigate and maintain lawns with diverse and irregular shapes. This paper introduces a comprehensive end-to-end pipeline for CPP, designed to convert user-defined boundaries on an aerial map into optimized coverage paths seamlessly. The pipeline includes user input extraction, coordinate transformation, area decomposition and path generation using our novel AdaptiveDecompositionCPP algorithm, preview and customization through an interactive coverage path visualizer, and conversion to actionable GPS waypoints. The AdaptiveDecompositionCPP algorithm combines cellular decomposition with an adaptive merging strategy to reduce non-mowing travel thereby enhancing operational efficiency. Experimental evaluations, encompassing both simulations and real-world lawnmower tests, demonstrate the effectiveness of the framework in coverage completeness and mowing efficiency.

-- Unmanned Aerial V ehicle (UA V) Coverage Path Planning (CPP) is critical for applications such as precision agriculture and search and rescue. While traditional methods rely on discrete grid-based representations, real-world UA V operations require power-efficient continuous motion planning. We formulate the UA V CPP problem in a continuous environment, minimizing power consumption while ensuring complete coverage. We train a reinforcement learning agent using an action-mapping-based Soft Actor-Critic (AM-SAC) algorithm employing a self-adaptive curriculum. Experiments on both procedurally generated and hand-crafted scenarios demonstrate the effectiveness of our method in learning energy-efficient coverage strategies. Unmanned Aerial V ehicle (UA V) Coverage Path Planning (CPP) is a challenging problem with numerous real-world applications.

Coverage Path Planning of Thermal Interface Materials (TIM) plays a crucial role in the design of power electronics and electronic control units. Up to now, this is done manually by experts or by using optimization approaches with a high computational effort. We propose a novel AI-based approach to generate dispense paths for TIM and similar dispensing applications. It is a drop-in replacement for optimization-based approaches. An Artificial Neural Network (ANN) receives the target cooling area as input and directly outputs the dispense path. Our proposed setup does not require labels and we show its feasibility on multiple target areas. The resulting dispense paths can be directly transferred to automated manufacturing equipment and do not exhibit air entrapments. The approach of using an ANN to predict process parameters for a desired target state in real-time could potentially be transferred to other manufacturing processes.