Minimum-Energy Exploration and Coverage for Robotic Systems

This dissertation is concerned with the question of autonomously and efficiently exploring three-dimensional environments. Hence, three robotics problems are studied in this work: the motion planning problem, the coverage problem and the exploration problem. The work provides a better understanding of motion and exploration problems with regard to their mathematical formulation and computational complexity, and proposes solutions in the form of algorithms capable of being implemented on a wide range of robotic systems.Because robots generally operate on a limited power source, the primary focus is on minimizing energy while moving or navigating in the environment. Many approaches address motion planning in the literature, however few attempt to provide a motion that aims at reducing the amount of energy expended during that process. We present a new approach, we call integral-squared torque approximation, that can be integrated with existing motion planners to find low-energy and collision-free paths in the robot\u27s configuration space.The robotics coverage problem has many real-world applications such as removing landmines or surveilling an area. We prove that this problem is inherently difficult to solve in its general case, and we provide an approach that is shown to be probabilistically complete, and that aims at minimizing a cost function (such as energy.) The remainder of the dissertation focuses on minimum-energy exploration, and offers a novel formulation for the problem. The formulation can be directly applied to compare exploration algorithms. In addition, an approach that aims at reducing energy during the exploration process is presented, and is shown through simulation to perform better than existing algorithms

Toward Asymptotically-Optimal Inspection Planning via Efficient Near-Optimal Graph Search

Inspection planning, the task of planning motions that allow a robot to inspect a set of points of interest, has applications in domains such as industrial, field, and medical robotics. Inspection planning can be computationally challenging, as the search space over motion plans that inspect the points of interest grows exponentially with the number of inspected points. We propose a novel method, Incremental Random Inspection-roadmap Search (IRIS), that computes inspection plans whose length and set of inspected points asymptotically converge to those of an optimal inspection plan. IRIS incrementally densifies a motion planning roadmap using sampling-based algorithms, and performs efficient near-optimal graph search over the resulting roadmap as it is generated. We demonstrate IRIS's efficacy on a simulated planar 5DOF manipulator inspection task and on a medical endoscopic inspection task for a continuum parallel surgical robot in anatomy segmented from patient CT data. We show that IRIS computes higher-quality inspection paths orders of magnitudes faster than a prior state-of-the-art method.Comment: RSS 201

Geometry-Aware Coverage Path Planning for Depowdering on Complex 3D Surfaces

This paper presents a new approach to obtaining nearly complete coverage paths (CP) with low overlapping on 3D general surfaces using mesh models. The CP is obtained by segmenting the mesh model into a given number of clusters using constrained centroidal Voronoi tessellation (CCVT) and finding the shortest path from cluster centroids using the geodesic metric efficiently. We introduce a new cost function to harmoniously achieve uniform areas of the obtained clusters and a restriction on the variation of triangle normals during the construction of CCVTs. Here, we utilize the planned VPs as cleaning configurations to perform residual powder removal in additive manufacturing using manipulator robots. The self-occlusion of VPs and ensuring collision-free robot configurations are addressed by integrating a proposed optimization-based strategy to find a set of candidate rays for each VP into the motion planning phase. CP planning benchmarks and physical experiments are conducted to demonstrate the effectiveness of the proposed approach. We show that our approach can compute the CPs and VPs of various mesh models with a massive number of triangles within a reasonable time.Comment: 8 pages, 8 figure

Heterogeneous Multi-Robot Collaboration for Coverage Path Planning in Partially Known Dynamic Environments

This research presents a cooperation strategy for a heterogeneous group of robots that comprises two Unmanned Aerial Vehicles (UAVs) and one Unmanned Ground Vehicles (UGVs) to perform tasks in dynamic scenarios. This paper defines specific roles for the UAVs and UGV within the framework to address challenges like partially known terrains and dynamic obstacles. The UAVs are focused on aerial inspections and mapping, while UGV conducts ground-level inspections. In addition, the UAVs can return and land at the UGV base, in case of a low battery level, to perform hot swapping so as not to interrupt the inspection process. This research mainly emphasizes developing a robust Coverage Path Planning (CPP) algorithm that dynamically adapts paths to avoid collisions and ensure efficient coverage. The Wavefront algorithm was selected for the two-dimensional offline CPP. All robots must follow a predefined path generated by the offline CPP. The study also integrates advanced technologies like Neural Networks (NN) and Deep Reinforcement Learning (DRL) for adaptive path planning for both robots to enable real-time responses to dynamic obstacles. Extensive simulations using a Robot Operating System (ROS) and Gazebo platforms were conducted to validate the approach considering specific real-world situations, that is, an electrical substation, in order to demonstrate its functionality in addressing challenges in dynamic environments and advancing the field of autonomous robots.The authors also would like to thank their home Institute, CEFET/RJ, the federal Brazilian research agencies CAPES (code 001) and CNPq, and the Rio de Janeiro research agency, FAPERJ, for supporting this work.info:eu-repo/semantics/publishedVersio

2차원 균일 커버리지 경로 계획을 위한 효율적 알고리즘

학위논문 (석사) -- 서울대학교 대학원 : 공과대학 기계공학부, 2020. 8. 박종우.Coverage path planning (CPP) is widely used in numerous robotic applications. With progressively complex and extensive applications of CPP, automating the planning process has become increasingly important. This thesis proposes an efficient CPP algorithm based on a random sampling scheme for spray painting applications. We have improved on the conventional CPP algorithm by alternately iterating the path generation and node sampling steps. This method can reduce the computational time by reducing the number of sampled nodes. We also suggest a new distance metric called upstream distance to generate reasonable path following given vector field. This induces the path to be aligned with a desired direction. Additionally, one of the machine learning techniques, support vector regression (SVR) is utilized to identify the paint distribution model. This method accurately predict the paint distribution model as a function of the painting parameters. We demonstrate our algorithm on several types of analytic surfaces and compare the results with those of conventional methods. Experiments are conducted to assess the performance of our approach compared to the traditional method.본 논문에서는 2차원 표면의 균일 커버리지 경로 계획을 설명하고 이를 효율적으로 푸는 알고리즘을 제시한다. 우리는 경로 계획 문제를 두 개의 하위 문제로 분리하여 각각 푸는 기존의 방식을 보완하여 두 개의 하위문제를 한 번에 풀면서 계산시간을 줄이는 방법을 제시하였다. 또한 경우에 따라 주어진 벡터 필드와 나란한 방향으로 경로가 생성될 필요가 있는데 이를 위해 거스름 거리(upstream distance)의 개념을 제시하였으며 여행 외판원 문제(Traveling Salesman Problem)를 풀 때 이를 적용하였다. 우리는 차량 도장 응용분야에 균일 커버리지 경로 계획법을 적용하였으며 도장 시스템을 고려하여 균일한 페인트 두께를 보장하는 방법을 같이 제시하였다. 네 가지 타입의 2차원 곡면에 대해 시뮬레이션을 진행하였으며 기존의 방법에 비해 더 적은 계산시간을 요구하면서도 합리적인 수준의 페인트 균일도를 달성함을 검증하였다.1 Introduction 1 1.1 Related Work 3 1.2 Contribution of Our Work 7 1.3 Organization of This Thesis 8 2 Preliminary Background 9 2.1 Elementary Differential Geometry of Surfaces in R3 10 2.1.1 Representation of Surfaces 10 2.1.2 Normal Curvature 10 2.1.3 Shape Operator 12 2.2 Traveling Salesman Problem 15 2.2.1 Definition 15 2.2.2 Variations of the TSP 17 2.2.3 Approximation Algorithm for TSP 19 2.3 Path Planning on Vector Fields 20 2.3.1 Randomized Path Planning 20 2.3.2 Upstream Criterion 20 2.4 Support Vector Regression 21 2.4.1 Single-Output SVR 21 2.4.2 Dual Problem of SVR 23 2.4.3 Kernel for Nonlinear System 25 2.4.4 Multi-Output SVR 26 3 Methods 29 3.1 Efficient Coverage Path Planning on Vector Fields 29 3.1.1 Efficient Node Sampling 31 3.1.2 Divide and Conquer Strategy 32 3.1.3 Upstream Distance 34 3.2 Uniform Coverage Path Planning in Spray Painting Applications 35 3.2.1 Minimum Curvature Direction 35 3.2.2 Learning Paint Deposition Model 36 4 Results 38 4.1 Experimental Setup 38 4.2 Simulation Result 41 4.3 Discussion 41 5 Conclusion 45 Bibliography 47 국문초록 52Maste

A Novel Clustering-Based Algorithm for Solving Spatially Constrained Robotic Task Sequencing Problems

The robotic task sequencing problem (RTSP) appears in various forms across many industrial applications and consists of developing an optimal sequence of motions to visit a set of target points defined in a task space. Developing solutions to problems involving complex spatial constraints remains challenging due to the existence of multiple inverse kinematic solutions and the requirements for collision avoidance. So far existing studies have been limited to relaxed RTSPs involving a small number of target points and relatively uncluttered environments. When extending existing methods to problems involving greater spatial constraints and large sets of target points, they either require substantially long planning times or are unable to obtain high-quality solutions. To this end, this article presents a clustering-based algorithm to efficiently address spatially constrained RTSPs involving several hundred to thousands of points. Through a series of benchmarks, we show that the proposed algorithm outperforms the state-of-the-art in terms of solution quality and planning efficiency for large, complex problems, achieving up to 60% reduction in task execution time and 91% reduction in computation time