Energy-aware Multi-UAV Coverage Mission Planning with Optimal Speed of Flight

This paper tackles the problem of planning minimum-energy coverage paths for multiple UAVs. The addressed Multi-UAV Coverage Path Planning (mCPP) is a crucial problem for many UAV applications such as inspection and aerial survey. However, the typical path-length objective of existing approaches does not directly minimize the energy consumption, nor allows for constraining energy of individual paths by the battery capacity. To this end, we propose a novel mCPP method that uses the optimal flight speed for minimizing energy consumption per traveled distance and a simple yet precise energy consumption estimation algorithm that is utilized during the mCPP planning phase. The method decomposes a given area with boustrophedon decomposition and represents the mCPP as an instance of Multiple Set Traveling Salesman Problem with a minimum energy objective and energy consumption constraint. The proposed method is shown to outperform state-of-the-art methods in terms of computational time and energy efficiency of produced paths. The experimental results show that the accuracy of the energy consumption estimation is on average 97% compared to real flight consumption. The feasibility of the proposed method was verified in a real-world coverage experiment with two UAVs.Comment: in IEEE Robotics and Automation Letter

Differentiable Boustrophedon Paths That Enable Optimization Via Gradient Descent

This paper introduces a differentiable representation for the optimization of boustrophedon path plans in convex polygons, explores an additional parameter of these path plans that can be optimized, discusses the properties of this representation that can be leveraged during the optimization process and shows that the previously published attempt at optimization of these path plans was too coarse to be practically useful. Experiments were conducted to show that this differentiable representation can reproduce scores from traditional discrete representations of boustrophedon path plans with high fidelity. Finally, optimization via gradient descent was attempted but found to fail because the search space is far more non-convex than was previously considered in the literature. The wide range of applications for boustrophedon path plans means that this work has the potential to improve path planning efficiency in numerous areas of robotics, including mapping and search tasks using uncrewed aerial systems, environmental sampling tasks using uncrewed marine vehicles, and agricultural tasks using ground vehicles, among numerous others applications.Comment: 6 pages, 5 figures, 1 tabl

Asymptotically optimized multi-surface coverage path planning for loco-manipulation in inspection and monitoring

Regular inspection and monitoring of aging assets are crucial to safe operation in industrial facilities, with remote robotic monitoring being a particularly promising approach for asset inspection. However, vessels, pipework, and surfaces to be monitored can follow complex 3D surfaces, and frequently no 3D as-built models exist. In this paper, we present an end-to-end solution that uses an optimization method for coverage path planning of multiple complex surfaces for mobile robot manipulators. The system includes a two-layer hierarchical structure of optimization: mission planning and motion planning. The surface sequence is optimized with a mixed-integer linear programming formulation while motion planning solves a whole-body optimal control problem considering the robot as a floating-base system. The loco-manipulation system automatically plans a full-coverage trajectory over multiple surfaces for contact-based non-destructive monitoring after unrolling the 3D-mesh region-of-interest selected from the user interface and projects it back to the surface. Our pipeline aims at offshore asset inspection and remote monitoring in industrial applications, and is also applicable in manufacturing and maintenance where area coverage is critical. We demonstrate the generality and scalability of our solution in a variety of robotic coverage path planning applications, including for multi-surface asset inspection using a quadrupedal manipulator

Anytime Replanning of Robot Coverage Paths for Partially Unknown Environments

In this paper, we propose a method to replan coverage paths for a robot operating in an environment with initially unknown static obstacles. Existing coverage approaches reduce coverage time by covering along the minimum number of coverage lines (straight-line paths). However, recomputing such paths online can be computationally expensive resulting in robot stoppages that increase coverage time. A naive alternative is greedy detour replanning, i.e., replanning with minimum deviation from the initial path, which is efficient to compute but may result in unnecessary detours. In this work, we propose an anytime coverage replanning approach named OARP-Replan that performs near-optimal replans to an interrupted coverage path within a given time budget. We do this by solving linear relaxations of mixed-integer linear programs (MILPs) to identify sections of the interrupted path that can be optimally replanned within the time budget. We validate our approach in simulation using maps of real-world environments and compare our approach against a greedy detour replanner and other state-of-the-art approaches.Comment: 14 pages, 15 figures, Paper submitted to T-R

Robot Area Coverage Path Planning in Aquatic Environments

This thesis is motivated by real world problems faced in aquatic environments. It addresses the problem of area coverage path planning with robots - the problem of moving an end-effector of a robot over all available space while avoiding existing obstacles. The problem is considered first in a 2D space with a single robot for specific environmental monitoring operations, and then with multi-robot systems — a known NP-complete problem. Next we tackle the coverage problem in 3D space - a step towards underwater mapping of shipwrecks or monitoring of coral reefs. The first part of this thesis leverages human expertise in river exploration and data collection strategies to automate and optimize environmental monitoring and surveying operations using autonomous surface vehicles (ASVs). In particular, four deterministic algorithms for both partial and complete coverage of a river segment are proposed, providing varying path length, coverage density, and turning patterns. These strategies result in increases in accuracy and efficiency compared to manual approaches taken by scientists. The proposed methods were extensively tested in simulation using maps of real rivers of different shapes and sizes. In addition, to verify their performance in real world operations, the ASVs were deployed successfully on several parts of the Congaree River in South Carolina, USA, resulting in a total of more than 35km of coverage trajectories in the field. In large scale coverage operations, such as marine exploration or aerial monitoring, single robot approaches are not ideal. Not only the coverage might take too long, but the robot might run out of battery charge before completing the task. In such scenarios, multi-robot approaches are preferable. Furthermore, several real world vehicles In large scale coverage operations, such as marine exploration or aerial monitoring, single robot approaches are not ideal. Not only the coverage might take too long, but the robot might run out of battery charge before completing the task. In such scenarios, multi-robot approaches are preferable. Furthermore, several real world vehicles are non-holonomic, but can be modeled using Dubins vehicle kinematics. The second part of this thesis focuses on environmental monitoring of aquatic domains using a team of Autonomous Surface Vehicles (ASVs) that have Dubins vehicle constraints. It is worth noting that both multi-robot coverage and Dubins vehicle coverage are NPcomplete problems. As such, we present two heuristic methods based on a variant of the traveling salesman problem—k-TSP—formulation and clustering algorithms that efficiently solve the problem. The proposed methods are tested both in simulations and with a team of ASVs operating on a 40 000m2 lake area to assess their ability to scale to the real world. Finally, in the third part, a step towards solving the coverage path planning problem in a 3D environment for surveying underwater structures, employing vision-only navigation strategies, is presented. Given the challenging conditions of the underwater domain, it is very complicated to obtain accurate state estimates reliably. Consequently, it is a great challenge to extend known path planning or coverage techniques developed for aerial or ground robot controls. In this work we are investigating a navigation strategy utilizing only vision to assist in covering a complex underwater structure. We propose to use a navigation strategy akin to what a human diver will execute when circumnavigating around a region of interest, in particular when collecting data from a shipwreck. The focus of this work is a step towards enabling the autonomous operation of lightweight agile robots near underwater wrecks in order to collect data for creating photo-realistic maps and volumetric 3D models while at the same time avoiding collisions. The proposed method uses convolutional neural networks (CNNs) to learn the control commands based on the visual input. We have demonstrated the feasibility of using a system based only on vision to learn specific strategies of navigation, with 80% accuracy on the prediction of control command changes. Experimental results and a detailed overview of the proposed method are discussed

Autonomous search of real-life environments combining dynamical system-based path planning and unsupervised learning

In recent years, advancements have been made towards the goal of using chaotic coverage path planners for autonomous search and traversal of spaces with limited environmental cues. However, the state of this field is still in its infancy as there has been little experimental work done. Current experimental work has not developed robust methods to satisfactorily address the immediate set of problems a chaotic coverage path planner needs to overcome in order to scan realistic environments within reasonable coverage times. These immediate problems are as follows: (1) an obstacle avoidance technique which generally maintains the kinematic efficiency of the robot's motion, (2) a means to spread chaotic trajectories across the environment (especially crucial for large and/or complex-shaped environments) that need to be covered, and (3) a real-time coverage calculation technique that is accurate and independent of cell size. This paper aims to progress the field by proposing algorithms that address all of these problems by providing techniques for obstacle avoidance, chaotic trajectory dispersal, and accurate coverage calculation. The algorithms produce generally smooth chaotic trajectories and provide high scanning coverage of environments. These algorithms were created within the ROS framework and make up a newly developed chaotic path planning application. The performance of this application was comparable to that of a conventional optimal path planner. The performance tests were carried out in environments of various sizes, shapes, and obstacle densities, both in real-life and Gazebo simulations

Interactive OAISYS: A photorealistic terrain simulation for robotics research

Photorealistic simulation pipelines are crucial for the development of novel robotic methods and modern machine vision approaches. Simulations have been particularly popular for generating labeled synthetic data sets, which otherwise would require vast efforts of manual annotation when using real data. However, these simulators are usually not interactive, and the data generation process cannot be interrupted. Therefore, these simulators are not suitable for evaluating active methods, such as active learning or perception aware path planning, which make decisions based on the observed perception data. In order to address this problem, we propose a modified version of the simulator OAISYS, a photorealistic scene simulator for unstructured outdoor environments. We extended the simulator in order to use it in an interactive way, and implemented a developer-friendly RPC interface so that it is easy for any environment to integrate into the simulator. In this paper, we demonstrate the functionality of the extension on 3D scene reconstruction to show its future research potential and provide an example of the implementation using the middleware ROS. The code is publicly available under https://github.com/DLR-RM/oaisy

Optimal Partitioning of a Surveillance Space for Persistent Coverage Using Multiple Autonomous Unmanned Aerial Vehicles: An Integer Programming Approach

Unmanned aerial vehicles (UAVs) are an essential tool for the battle eld commander in part because they represent an attractive intelligence gathering platform that can quickly identify targets and track movements of individuals within areas of interest. In order to provide meaningful intelligence in near-real time during a mission, it makes sense to operate multiple UAVs with some measure of autonomy to survey the entire area persistently over the mission timeline. This research considers a space where intelligence has identi ed a number of locations and their surroundings that need to be monitored for a period of time. An integer program is formulated and solved to partition this surveillance space into the minimum number of subregions such that these locations fall outside of each partitioned subregion for e cient, persistent surveillance of the locations and their surroundings. Partitioning is followed by a UAV-to-partitioned subspace matching algorithm so that each subregion of the partitioned surveillance space is assigned exactly one UAV. Because the size of the partition is minimized, the number of UAVs used is also minimized

Planejamento online de caminhos por cobertura através de meta-heurística

Different practical applications for Unmanned Aerial Vehicles (UAVs) have emerged in recent years, requiring periodic and detailed inspections to verify possible structural changes. Inspections using UAVs should minimize flight time due to battery time constraints and identify topographical features of terrain or structures. In this sense, coverage path planning (CPP) aims to find the best path to cover a given area while respecting the restrictions of the operation. Photometric terrain information is used to create routes or even refine already created paths, in addition to enabling different types of image analysis. Therefore, the main contribution of this research is the development of a methodology that uses a meta-heuristic algorithm to create optimized missions that seek to balance two conflicting objectives: mission time and image quality at 3D reconstructions. The technique was applied both in a simulated scenario and in a real environment to verify its effectiveness, seeking the application of several meta-heuristic techniques and a statistical analysis of the results found. In addition, the algorithm was applied to the most diverse structures, both on slopes and in regions to be investigated, through active sensors such as lasers and maps provided offline through point cloud and digital elevation models. The results showed that the algorithm was able to create optimized missions, equidistant from the surface and with all CPP criteria met. In addition, there is the possibility of controlling the two proposed objectives, aiming to increase the quality of the three-dimensional reconstructions and the mission time.Diferentes aplicações práticas com Veículos Aéreos Não Tripuláveis (VANTs) surgiram nos últimos anos, exigindo inspeções periódicas e detalhadas para verificar possíveis alterações estruturais. As inspeções usando veículos aéreos não tripulados VANTs devem minimizar o tempo de voo devido às restrições de tempo da bateria e identificar as características topográficas do terreno ou estruturas. Nesse sentido, o Planejamento do Caminho de Cobertura (CPP) visa encontrar o melhor caminho para a cobertura de uma determinada área respeitando as restrições da operação. As informações fotométricas do terreno são usadas para criar rotas ou mesmo refinar caminhos já criados, além de possibilitar diversos tipos de análises de imagens. Portanto, a principal contribuição desta pesquisa é o desenvolvimento de uma metodologia que utiliza um algoritmo meta-heurístico para criar missões otimizadas que buscam equilibrar dois objetivos conflitantes: tempo da missão e qualidade das imagens visando reconstruções 3D. A técnica foi aplicada tanto em um cenário simulado quanto em um ambiente real para verificar sua eficácia, buscando a aplicação de diversas técnicas meta-heurísticas e uma análise estatística sobre os resultados encontrados. Além disso o algoritmo foi aplicado nos mais diversas estruturas, tanto em taludes como em regiões a serem investigadas, através de sensores ativos como Lasers e mapas fornecidos de maneira offline através de point cloud e digital elevation model. Os resultados mostraram que o algoritmo foi capaz de criar missões otimizadas, equidistante a superfície e com todos os critérios de CPP sendo atendidos com equilíbrio dos objetivos de tempo e fotometria em comparação com outros algoritmos. Além disso, existe a possibilidade de controlar os dois objetivos propostos, visando aumentar a qualidade da reconstruções tridimensional e o tempo da missão.CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superio