Energy-Aware Path Planning for Fixed-Wing Seaplane UAVs

Fixed-wing unmanned aerial vehicles (UAVs) are commonly used for remote sensing applications over water bodies, such as monitoring water quality or tracking harmful algal blooms. However, there are some types of measurements that are difficult to accurately obtain from the air. In existing work, water samples have been collected in situ either by hand, with an unmanned surface vehicle (USV), or with a vertical takeoff and landing (VTOL) UAV such as a multirotor. We propose a path planner, landing control algorithm, and energy estimator that will allow a low-cost and energy efficient fixed-wing UAV to carry out a combined remote sensing and direct water sampling mission without requiring sophisticated sensors and using limited onboard computation. Finally, we demonstrate a fully autonomous mission on a modified off-the-shelf RC aircraft. The aircraft flies a survey pattern, lands at a series of sampling points and then returns to the starting location while respecting the available energy budget. In our experiments, we completed multiple sampling missions in the real world with no aborted landings or crashes and an overall energy estimation error of approximately 5%

협소하고 복잡한 환경에서 자율 주행을 위한 샘플링 기반 모션 계획 방법

학위논문 (박사)-- 서울대학교 융합과학기술대학원 융합과학부, 2017. 8. 박재흥.Autonomous vehicles are being actively developed for fully autonomous driving without driver intervention. Motion planning is one of the most key technologies in terms of driving safety and efficiency. In particular, the motion planning in constrained narrow space such as a parking lot is very challenging because it requires many changes in forward and backward directions and adjustments of position and orientation of the vehicle. In this thesis, a sampling-based motion planning algorithm is proposed based on Rapidly-exploring Random Trees (RRT, RRT*) by specifying desired orientation during the tree expansion and the rewiring step. The contribution is as follows. First, efficient sampling method is proposed for narrow-cluttered area. In this area, the probability of obtaining a sample to pass through the area due to the obstacle area is relatively low than an open area. It may also fail to extend the path if sampled position is difficult to extend from near nodes. To solve this problem, a constraint model on the tangential direction of the random sample is proposed. Second, we propose an extension method based on tangential direction constraint. In the process of expanding the tree to random samples, a large number of nodes in narrow-cluttered regions cannot pass the collision test. This increases unnecessary iteration numbers and increases memory usage. To solve this problem, we propose a node extension method based on gradient descent. The proposed algorithm has been tested in various situations and its results demonstrated much faster target path search and convergence to the optimal path than the existing nonholonomic RRT*.I. Introduction 1 1.1 Autonomous Vehicles 1 1.2 Planning System of Autonomous Vehicles 2 1.3 Contribution of Thesis 4 II. Related Works 6 2.1 Motion Planning for Aunomous Vehicles 6 2.2 Sampling-based Motion Planning Algorithms 9 III. Sampling-based Kinodynamic Motion Planning Algorithm for Narrow Cluttered Environments 14 3.1 Overview 14 3.2 Preliminary Definition 15 3.2.1 Problem Statements 15 3.2.2 Autonomous Vehicle Model 16 3.3 Kinodynamic RRT and Limitations 16 3.3.1 Overview of DO-RRT Algorithm 20 3.4 Magnetic-like Field based Desired Orientation Model 20 3.4.1 Magnet-like Field Model 22 3.4.2 Pfaffian Constraints 24 3.4.3 DO(Desired Orientation) Model 26 3.5 Sampling Fuction of DO-RRT 28 3.6 Extend Function of DO-RRT 30 3.7 Experimental Results 31 3.7.1 Experimental Condition 31 3.7.2 Simulation Test Results 32 3.7.3 Vehicle Test Results 34 IV. Sampling-based Geometric Motion Planning Algorithm for Narrow Cluttered Environments 38 4.1 Overview 38 4.2 Backgrounds 39 4.2.1 Algorithm Description and Limitations 39 4.2.2 Overview of Proposed Algorithm 42 4.3 Desired Orientation based Random Sampling Method 44 4.4 Desired Orientation based Extend Method 47 4.5 Analysis 49 4.5.1 Probabilistic Completeness 49 4.5.2 Asymptotic Optimality 51 4.5.3 Configuration Space Analysis 53 4.6 Experimental Results 58 4.6.1 Experimental Condition 59 4.6.2 The Result of Desired Orientation-RRT Planner 59 4.6.3 Result of Desired Orientation-RRT 65 V. Experimental Platform Development 71 5.1 Hardware Architecture 71 5.2 Software Architecture 73 5.3 Valet Parking System 74 5.3.1 Perception System 75 5.3.2 Localization System 76 5.3.3 Planning System 77 5.3.4 Control System 79 5.4 Experimental Validation 81 VI. Conclusion 85 Bibliography 86Docto

A COLLISION AVOIDANCE SYSTEM FOR AUTONOMOUS UNDERWATER VEHICLES

The work in this thesis is concerned with the development of a novel and practical collision avoidance system for autonomous underwater vehicles (AUVs). Synergistically, advanced stochastic motion planning methods, dynamics quantisation approaches, multivariable tracking controller designs, sonar data processing and workspace representation, are combined to enhance significantly the survivability of modern AUVs. The recent proliferation of autonomous AUV deployments for various missions such as seafloor surveying, scientific data gathering and mine hunting has demanded a substantial increase in vehicle autonomy. One matching requirement of such missions is to allow all the AUV to navigate safely in a dynamic and unstructured environment. Therefore, it is vital that a robust and effective collision avoidance system should be forthcoming in order to preserve the structural integrity of the vehicle whilst simultaneously increasing its autonomy. This thesis not only provides a holistic framework but also an arsenal of computational techniques in the design of a collision avoidance system for AUVs. The design of an obstacle avoidance system is first addressed. The core paradigm is the application of the Rapidly-exploring Random Tree (RRT) algorithm and the newly developed version for use as a motion planning tool. Later, this technique is merged with the Manoeuvre Automaton (MA) representation to address the inherent disadvantages of the RRT. A novel multi-node version which can also address time varying final state is suggested. Clearly, the reference trajectory generated by the aforementioned embedded planner must be tracked. Hence, the feasibility of employing the linear quadratic regulator (LQG) and the nonlinear kinematic based state-dependent Ricatti equation (SDRE) controller as trajectory trackers are explored. The obstacle detection module, which comprises of sonar processing and workspace representation submodules, is developed and tested on actual sonar data acquired in a sea-trial via a prototype forward looking sonar (AT500). The sonar processing techniques applied are fundamentally derived from the image processing perspective. Likewise, a novel occupancy grid using nonlinear function is proposed for the workspace representation of the AUV. Results are presented that demonstrate the ability of an AUV to navigate a complex environment. To the author's knowledge, it is the first time the above newly developed methodologies have been applied to an A UV collision avoidance system, and, therefore, it is considered that the work constitutes a contribution of knowledge in this area of work.J&S MARINE LT

Underwater Vehicles

For the latest twenty to thirty years, a significant number of AUVs has been created for the solving of wide spectrum of scientific and applied tasks of ocean development and research. For the short time period the AUVs have shown the efficiency at performance of complex search and inspection works and opened a number of new important applications. Initially the information about AUVs had mainly review-advertising character but now more attention is paid to practical achievements, problems and systems technologies. AUVs are losing their prototype status and have become a fully operational, reliable and effective tool and modern multi-purpose AUVs represent the new class of underwater robotic objects with inherent tasks and practical applications, particular features of technology, systems structure and functional properties