Enhancing the Transition-based RRT to deal with complex cost spaces

The Transition-based RRT (T-RRT) algorithm enables to solve motion planning problems involving configuration spaces over which cost functions are defined, or cost spaces for short. T-RRT has been successfully applied to diverse problems in robotics and structural biology. In this paper, we aim at enhancing T-RRT to solve ever more difficult problems involving larger and more complex cost spaces. We compare several variants of T-RRT by evaluating them on various motion planning problems involving different types of cost functions and different levels of geometrical complexity. First, we explain why applying as such classical extensions of RRT to T-RRT is not helpful, both in a mono-directional and in a bidirectional context. Then, we propose an efficient Bidirectional T-RRT, based on a bidirectional scheme tailored to cost spaces. Finally, we illustrate the new possibilities offered by the Bidirectional T-RRT on an industrial inspection problem

Path Planning Framework for Unmanned Ground Vehicles on Uneven Terrain

In this thesis, I address the problem of long-range path planning on uneven terrain for non-holonomic wheeled mobile robots (WMR). Uneven terrain path planning is essential for search-and-rescue, surveillance, military, humanitarian, agricultural, constructing missions, etc. These missions necessitate the generation of a feasible sequence of waypoints, or reference states, to navigate a WMR from the initial location to the final target location through the uneven terrain. The feasibility of navigating through a given path over uneven terrain can be undermined by various terrain features. Examples of such features are loose soil, vegetation, boulders, steeply sloped terrain, or a combination of all of these elements. I propose a three-stage framework to solve the problem of rapid long-range path planning. In the first stage, RRT-Connect provides a rapid discovery of the feasible solution. Afterward, Informed RRT* improves the feasible solution. Finally, Shortcut heuristics improves the solution locally. To improve the computational speed of path planning algorithms, we developed an accelerated version of the traversability estimation on point clouds based on Principal Component Analysis. The benchmarks demonstrate the efficacy of the path planning approach

Robust Localization and Efficient Path Planning for Mobile Sensor Networks

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2016. 2. 오성회.The area of wireless sensor networks has flourished over the past decade due to advances in micro-electro-mechanical sensors, low power communication and computing protocols, and embedded microprocessors. Recently, there has been a growing interest in mobile sensor networks, along with the development of robotics, and mobile sensor networks have enabled networked sensing system to solve the challenging issues of wireless sensor networks by adding mobility into many different applications of wireless sensor networks. Nonetheless, there are many challenges to be addressed in mobile sensor networks. Among these, the estimation for the exact location is perhaps the most important to obtain high fidelity of the sensory information. Moreover, planning should be required to send the mobile sensors to sensing location considering the region of interest, prior to sensor placements. These are the fundamental problems in realizing mobile sensor networks which is capable of performing monitoring mission in unstructured and dynamic environment. In this dissertation, we take an advantage of mobility which mobile sensor networks possess and develop localization and path planning algorithms suitable for mobile sensor networks. We also design coverage control strategy using resource-constrained mobile sensors by taking advantages of the proposed path planning method. The dissertation starts with the localization problem, one of the fundamental issue in mobile sensor networks. Although global positioning system (GPS) can perform relatively accurate localization, it is not feasible in many situations, especially indoor environment and costs a tremendous amount in deploying all robots equipped with GPS sensors. Thus we develop the indoor localization system suitable for mobile sensor networks using inexpensive robot platform. We focus on the technique that relies primarily on the camera sensor. Since it costs less than other sensors, all mobile robots can be easily equipped with cameras. In this dissertation, we demonstrate that the proposed method is suitable for mobile sensor networks requiring an inexpensive off-the-shelf robotic platform, by showing that it provides consistently robust location information for low-cost noisy sensors. We also focus on another fundamental issue of mobile sensor networks which is a path planning problem in order to deploy mobile sensors in specific locations. Unlike the traditional planning methods, we present an efficient cost-aware planning method suitable for mobile sensor networks by considering the given environment, where it has environmental parameters such as temperature, humidity, chemical concentration, stealthiness and elevation. A global stochastic optimization method is used to improve the efficiency of the sampling based planning algorithm. This dissertation presents the first approach of sampling based planning using global tree extension. Based on the proposed planning method, we also presents a general framework for modeling a coverage control system consisting of multiple robots with resource constraints suitable for mobile sensor networks. We describe the optimal informative planning methods which deal with maximization problem with constraints using global stochastic optimization method. In addition, we describe how to find trajectories for multiple robots efficiently to estimate the environmental field using information obtained from all robots.Chapter 1 Introduction 1 1.1 Mobile Sensor networks 1 1.1.1 Challenges 3 1.2 Overview of the Dissertation 4 Chapter 2 Background 7 2.1 Localization in MSNs 7 2.2 Path planning in MSNs 10 2.3 Informative path planning in MSNs 12 Chapter 3 Robust Indoor Localization 15 3.1 An Overview of Coordinated Multi-Robot Localization 16 3.2 Multi-Robot Localization using Multi-View Geometry 19 3.2.1 Planar Homography for Robot Localization 20 3.2.2 Image Based Robot Control 21 3.3 Multi-Robot Navigation System 25 3.3.1 Multi-Robot System 26 3.3.2 Multi-Robot Navigation 30 3.4 Experimental Results 32 3.4.1 Coordinated Multi-Robot Localization: Single-Step 32 3.4.2 Coordinated Multi-Robot Localization: Multi-Step 36 3.5 Discussions and Comparison to Leap-Frog 42 3.5.1 Discussions 42 3.5.2 Comparison to Leap-Frog 45 3.6 Summary 51 Chapter 4 Preliminaries to Cost-Aware Path Planning 53 4.1 Related works 54 4.2 Sampling based path planning 56 4.3 Cross entropy method 59 4.3.1 Cross entropy based path planning 63 Chapter 5 Fast Cost-Aware Path Planning using Stochastic Optimization 65 5.1 Problem formulation 66 5.2 Issues with sampling-based path planning for complex terrains or high dimensional spaces 68 5.3 Cost-Aware path planning (CAPP) 73 5.3.1 CE Extend 75 5.4 Analysis of CAPP 81 5.4.1 Probabilistic Completeness 81 5.4.2 Asymptotic optimality 83 5.5 Simulation and experimental results 84 5.5.1 (P1) Cost-Aware Navigation in 2D 85 5.5.2 (P2) Complex Terrain Navigation 88 5.5.3 (P3) Humanoid Motion Planning 96 5.6 Summary 103 Chapter 6 Effcient Informative Path Planning 105 6.1 Problem formulation 106 6.2 Cost-Aware informative path planning (CAIPP) 109 6.2.1 Overall procedure 110 6.2.2 Update Bound 112 6.2.3 CE Estimate 115 6.3 Analysis of CAIPP 118 6.4 Simulation and experimental results 120 6.4.1 Single robot informative path planning 120 6.4.2 Multi robot informative path planning 122 6.5 Summary 125 Chapter 7 Conclusion and Future Work 129 Appendices 131 Appendix A Proof of Theorem 1 133 Appendix B Proof of Theorem 2 135 Appendix C Proof of Theorem 3 137 Appendix D Proof of Theorem 4 139 Appendix E Dubins' curve 141 Bibliography 147 초 록 163Docto

Human-Aware Motion Planning for Safe Human-Robot Collaboration

With the rapid adoption of robotic systems in our daily lives, robots must operate in the presence of humans in ways that improve safety and productivity. Currently, in industrial settings, human safety is ensured through physically separating the robotic system from the human. However, this greatly decreases the set of shared human-robot tasks that can be accomplished and also reduces human-robot team fluency. In recent years, robots with improved sensing capabilities have been introduced and the feasibility of humans and robots co-existing in shared spaces has become a topic of interest. This thesis proposes a human-aware motion planning approach building on RRT-Connect, dubbed Human-Aware RRT-Connect, that plans in the presence of humans. The planner considers a composite cost function that includes human separation distance and visibility costs to ensure the robot maintains a safety distance during motion while being as visible as possible to the human. A danger criterion cost considering two mutually dependent factors, human-robot center of mass distance and robot inertia, is also introduced into the cost formulation to ensure human safety during planning. A simulation study is conducted to demonstrate the planner performance. For the simulation study, the proposed Human-Aware RRT-Connect planner is evaluated against RRT-Connect through a set of problem scenarios that vary in environment and task complexity. Several human-robot configurations are tested in a shared workspace involving a simulated Franka Emika Panda arm and human model. Through the problem scenarios, it is shown that the Human-Aware RRT-Connect planner, paired with the developed HRI costs, performs better than the baseline RRT-Connect planner with respect to a set of quantitative metrics. The paths generated by the Human-Aware RRT-Connect planner maintain larger separation distances from the human, are more visible and also safer due to the minimization of the danger criterion. It is also shown that the proposed HRI cost formulation outperforms formulations from previous work when tested with the Human-Aware RRT-Connect planner

Learning Reach-to-Grasp Motions From Human Demonstrations

Reaching over to grasp an item is arguably the most commonly used motor skill by humans. Even under sudden perturbations, humans seem to react rapidly and adapt their motion to guarantee success. Despite the apparent ease and frequency with which we use this ability, a complete understanding of the underlying mechanisms cannot be claimed. It is partly due to such incomplete knowledge that adaptive robot motion for reaching and grasping under perturbations is not perfectly achieved. In this thesis, we take the discriminative approach for modelling trajectories of reach-to-grasp motion from expert demonstrations. Throughout this thesis, we will employ time-independent (autonomous) flow based representations to learn reactive motion controllers which can then be ported onto robots. This thesis is divided into three main parts. The first part is dedicated to biologically inspired modelling of reach-to-grasp motions with respect to the hand-arm coupling. We build upon previous work in motion modelling using autonomous dynamical systems (DS) and present a coupled dynamical system (CDS) model of these two subsystems. The coupled model ensures satisfaction of the constraints between the hand and the arm subsystems which are critical to the success of a reach-to-grasp task. Moreover, it reduces the complexity of the overall motion planning problem as compared to considering a combined problem for the hand and the arm motion. In the second part we extend the CDS approach to incorporate multiple grasping points. Such a model is beneficial due to the fact that many daily life objects afford multiple grasping locations on their surface. We combine a DS based approach with energy-function learning to learn a multiple attractor dynamical system where the attractors are mapped to the desired grasping points. We present the Augmented-SVM (ASVM) model that combines the classical SVM formulation with gradient constraints arising from the energy function to learn the desired dynamical function for motion generation. In the last part of this thesis, we address the problem of inverse-kinematics and obstacle avoidance by combining our flow-based motion generator with global configuration-space planners. We claim that the two techniques complement each other. On one hand, the fast reactive nature of our flow based motion generator can used to guide the search of a randomly exploring random tree (RRT) based global planner. On the other hand, global planners can efficiently handle arbitrary obstacles and avoid local minima present in the dynamical function learned from demonstrations. We show that combining the information from demonstrations with global planning in the form of a energy-map considerably decreases the computational complexity of state-of-the-art sampling based planners. We believe that this thesis has the following contributions to Robotics and Machine Learning. First, we have developed algorithms for fast and adaptive motion generation for reach-grasp motions. Second, we formulated an extension to the classical SVM formulation that takes into account the gradient information from data. We showed that instead of being limited as a classifier or a regressor, the SVM framework can be used as a more general function approximation technique. Lastly, we have combined our local methods with global approaches for planning to achieve arbitrary obstacle avoidance and considerable reduction in the computation complexity of the global planners

Kinodynamic motion planning for quadrotor-like aerial robots

Motion planning is the field of computer science that aims at developing algorithmic techniques allowing the automatic computation of trajecto- ries for a mechanical system. The nature of such a system vary according to the fields of application. In computer animation it could be a humanoid avatar. In molecular biology it could be a protein. The field of application of this work being aerial robotics, the system is here a four-rotor UAV (Unmanned Aerial Vehicle) called quadrotor. The motion planning problem consists in computing a series of motions that brings the system from a given initial configuration to a desired final configuration without generating collisions with its environment, most of the time known in advance. Usual methods explore the system’s configuration space regardless of its dynamics. By construction the thrust force that allows a quadrotor to fly is tangential to its attitude which implies that not every motion can be performed. Furthermore, the magnitude of this thrust force and hence the linear acceleration of the center of mass are limited by the physical capabilities of the robot. For all these reasons, not only position and orientation must be planned, higher derivatives must be planned also if the motion is to be executed. When this is the case we talk of kinodynamic motion planning. A distinction is made between the local planner and the global planner. The former is in charge of producing a valid trajectory between two states of the system without necessarily taking collisions into account. The later is the overall algorithmic process that is in charge of solving the motion planning problem by exploring the state space of the system. It relies on multiple calls to the local planner. We present a local planner that interpolates two states consisting of an arbitrary number of degrees of freedom (dof) and their first and second derivatives. Given a set of bounds on the dof derivatives up to the fourth order (snap), it quickly produces a near-optimal minimum time trajectory that respects those bounds. In most of modern global motion planning algorithms, the exploration is guided by a distance function (or metric). The best choice is the cost-to-go, i.e. the cost associated to the local method. In the context of kinodynamic motion planning, it is the duration of the minimal-time trajectory. The problem in this case is that computing the cost-to-go is as hard (and thus as costly) as computing the optimal trajectory itself. We present a metric that is a good approximation of the cost-to-go but which computation is far less time consuming. The dominant paradigm nowadays is sampling-based motion planning. This class of algorithms relies on random sampling of the state space in order to quickly explore it. A common strategy is uniform sampling. It however appears that, in our context, it is a rather poor choice. Indeed, a great majority of uniformly sampled states cannot be interpolated. We present an incremental sampling strategy that significantly decreases the probability of this happening

Extensions of sampling-based approaches to path planning in complex cost spaces: applications to robotics and structural biology

Planning a path for a robot in a complex environment is a crucial issue in robotics. So-called probabilistic algorithms for path planning are very successful at solving difficult problems and are applied in various domains, such as aerospace, computer animation, and structural biology. However, these methods have traditionally focused on finding paths avoiding collisions, without considering the quality of these paths. In recent years, new approaches have been developed to generate high-quality paths: in robotics, this can mean finding paths maximizing safety or control; in biology, this means finding motions minimizing the energy variation of a molecule. In this thesis, we propose several extensions of these methods to improve their performance and allow them to solve ever more difficult problems. The applications we present stem from robotics (industrial inspection and aerial manipulation) and structural biology (simulation of molecular motions and exploration of energy landscapes)

Extensions of sampling-based approaches to path planning in complex cost spaces: applications to robotics and structural biology

Planifier le chemin d’un robot dans un environnement complexe est un problème crucial en robotique. Les méthodes de planification probabilistes peuvent résoudre des problèmes complexes aussi bien en robotique, qu’en animation graphique, ou en biologie structurale. En général, ces méthodes produisent un chemin évitant les collisions, sans considérer sa qualité. Récemment, de nouvelles approches ont été créées pour générer des chemins de bonne qualité : en robotique, cela peut être le chemin le plus court ou qui maximise la sécurité ; en biologie, il s’agit du mouvement minimisant la variation énergétique moléculaire. Dans cette thèse, nous proposons plusieurs extensions de ces méthodes, pour améliorer leurs performances et leur permettre de résoudre des problèmes toujours plus difficiles. Les applications que nous présentons viennent de la robotique (inspection industrielle et manipulation aérienne) et de la biologie structurale (mouvement moléculaire et conformations stables). ABSTRACT : Planning a path for a robot in a complex environment is a crucial issue in robotics. So-called probabilistic algorithms for path planning are very successful at solving difficult problems and are applied in various domains, such as aerospace, computer animation, and structural biology. However, these methods have traditionally focused on finding paths avoiding collisions, without considering the quality of these paths. In recent years, new approaches have been developed to generate high-quality paths: in robotics, this can mean finding paths maximizing safety or control; in biology, this means finding motions minimizing the energy variation of a molecule. In this thesis, we propose several extensions of these methods to improve their performance and allow them to solve ever more difficult problems. The applications we present stem from robotics (industrial inspection and aerial manipulation) and structural biology (simulation of molecular motions and exploration of energy landscapes)