Medial-Axis Biased Rapidly-Exploring Random Trees

RRTs (Rapidly-Exploring Random Trees) have shown wide applications in robotics. RRTs are a type of sampling-based motion planners that expand to fill the space starting from one or more root configurations. RRTs are excellent at rapidly exploring open space in an environment, as well as finding configurations close to obstacles. PRMs (Probabilistic RoadMap methods) are another class of sampling-based motion planners. One particular planner, Medial Axis PRM (MAPRM), constructs roadmaps on the medial axis, leading to paths with high clearance. This work introduces a novel RRT variant, namely the Medial Axis RRT (MARRT) that constructs trees whose nodes and edges lie on (or near) the medial axis of the free configuration space. This is achieved through the use of MAPRM-like techniques to retract sampled configurations to the medial axis of the free space. We show MARRT successfully increases clearance along RRT paths for a broad spectrum of motion planning problems

Medial Axis Local Planner: Local Planning for Medial Axis Roadmaps

In motion planning, high clearance paths are favorable due to their increased visibility and reduction of collision risk such as the safety of problems involving: human- robot cooperation. One popular approach to solving motion planning problems is the Probabilistic Roadman Method (PRM), which generates a graph of the free space of an environment referred to as a roadmap. In this work we describe a new approach to making high clearance paths when using PRM The medial axis is useful for this since it represents the set of points with maximal clearance and is well defined in higher dimensions. However it can only be computed exactly in workspace. Our goal is to generate roadmaps with paths following the medial axis of an environment without explicitly computing the medial axis. One of the major steps of PRM is local planning: the planning of motion between two nearby nodes PRMs have been used to build roadmaps that have nodes on the medial axis but so far there has been no local planner method proposed for connecting these nodes on the medial axis. These types of high clearance motions are desirable and needed in many robotics applications. This work proposes Medial Axis Local Planner (MALP), a local planner which attempts to connect medial axis configurations via the medial axis. The recursive method takes a simple path between two medial axis configurations and attempts to deform the path to fit the medial axis. This deformation creates paths with high clearance and visibility properties. We have implemented this local planner and have tested it in 2D and 3D rigid body and 8D and 16D fixed base articulated linkage environments. We compare MALP with a straight-line local planner (SL), a typical local planer used in motion planning that interpolated along a line in the planning space. Our results indicate that MALP generated higher clearance paths than SL local planning. As a result, MALP found more connections and generated fewer connected components as compared to connecting the same nodes using SL connections. Using MALP connects noes on the medial axis, increasing the overall clearance of the roadmap generated

Visualization tools for moving objects

In this work we describe the design and implementation of a general framework for visualizing and editing motion planning environments, problem instances, and their solutions. The motion planning problem consists of finding a valid path between a start and a goal configuration for a movable object. The workspace is, in traditional robotics and animation applications, composed of one or more objects (called obstacles) that cannot overlap with the robot. As even the simplest motion planning problems have been shown to be in- tractable, most practical approaches to motion planning use randomization and/or compute approximate solutions. While the tool we present allows the manipulation and evaluation of planner solutions and the animation of any path found by any plan- ner, it is specialized for a class of randomized planners called probabilistic roadmap methods (PRMs). PRMs are roadmap-based methods that generate a graph or roadmap where the nodes represent collision-free configurations and the edges represent feasible paths between those configurations. PRMs typically consist of two phases: roadmap con- struction, where a roadmap is built, and query, where the start and goal configura- tions are connected to the roadmap and then a path is extracted using graph search techniques

Motion planning for a rigid body using random networks on the medial axis of the free space

Several motion planning methods using networks of randomly generated nodes in the free space have been shown to perform well in a number of cases, however their performance degrades when paths are required topass through narrow passages in the free space. In [16] weproposed MAPRM, a method of sampling the con guration space in which randomly generated con gurations, free or not, are retracted onto the medial axis of the free space without having to rst compute the medial axis; this was shown to increase sampling in narrow passages. In this paper we give details of the MAPRM algorithm for the case of a free- ying rigid body moving in three dimensions, and show that the retraction may be carried out without explicitly computing the C-obstacles or the medial axis. We give theoretical arguments to show that this improves sampling in narrow corridors, and present preliminary experimental results comparing the performance to uniform random sampling from the free space.

ADAPTIVE PROBABILISTIC ROADMAP CONSTRUCTION WITH MULTI-HEURISTIC LOCAL PLANNING

The motion planning problem means the computation of a collision-free motion for a movable object among obstacles from the given initial placement to the given end placement. Efficient motion planning methods have many applications in many fields, such as robotics, computer aided design, and pharmacology. The problem is known to be PSPACE-hard. Because of the computational complexity, practical applications often use heuristic or incomplete algorithms. Probabilistic roadmap is a probabilistically complete motion planning method that has been an object of intensive study over the past years. The method is known to be susceptible to the problem of “narrow passages”: Finding a motion that passes a narrow, winding tunnel can be very expensive. This thesis presents a probabilistic roadmap method that addresses the narrow passage problem with a local planner based on heuristic search. The algorithm is suitable for planning motions for rigid bodies and articulated robots including multirobot systems with many degrees-of-freedom. Variants of the algorithm are describe

A framework for roadmap-based navigation and sector-based localization of mobile robots

Personal robotics applications require autonomous mobile robot navigation methods that are safe, robust, and inexpensive. Two requirements for autonomous use of robots for such applications are an automatic motion planner to select paths and a robust way of ensuring that the robot can follow the selected path given the unavoidable odometer and control errors that must be dealt with for any inexpensive robot. Additional difficulties are faced when there is more than one robot involved. In this dissertation, we describe a new roadmapbased method for mobile robot navigation. It is suitable for partially known indoor environments and requires only inexpensive range sensors. The navigator selects paths from the roadmap and designates localization points on those paths. In particular, the navigator selects feasible paths that are sensitive to the needs of the application (e.g., no sharp turns) and of the localization algorithm (e.g., within sensing range of two features). We present a new sectorbased localizer that is robust in the presence of sensor limitations and unknown obstacles while still maintaining computational efficiency. We extend our approach to teams of robots focusing on quickly sensing ranges from all robots while avoiding sensor crosstalk, and reducing the pose uncertainties of all robots while using a minimal number of sensing rounds. We present experimental results for mobile robots and describe a webbased route planner for the Texas A&M campus that utilizes our navigator

Méthode interactive et par l'apprentissage pour la generation de trajectoire en conception du produit

The accessibility is an important factor considered in the validation and verification phase of the product design and usually dominates the time and costs in this phase. Defining the accessibility verification as the motion planning problem, the sampling based motion planners gained success in the past fifteen years. However, the performances of them are usually shackled by the narrow passage problem arising when complex assemblies are composed of large number of parts, which often leads to scenes with high obstacle densities. Unfortunately, humans’ manual manipulations in the narrow passage always show much more difficulties due to the limitations of the interactive devices or the cognitive ability. Meanwhile, the challenges of analyzing the end users’ response in the design process promote the integration with the direct participation of designers.In order to accelerate the path planning in the narrow passage and find the path complying with user’s preferences, a novel interactive motion planning method is proposed. In this method, the integration with a random retraction process helps reduce the difficulty of manual manipulations in the complex assembly/disassembly tasks and provide local guidance to the sampling based planners. Then a hypothesis is proposed about the correlation between the topological structure of the scenario and the motion path in the narrow passage. The topological structure refers to the medial axis (2D) and curve skeleton (3D) with branches pruned. The correlation runs in an opposite manner to the sampling based method and provide a new perspective to solve the narrow passage problem. The curve matching method is used to explore this correlation and an interactive motion planning framework that can learn from experience is constructed in this thesis. We highlight the performance of our framework on a challenging problem in 2D, in which a non-convex object passes through a cluttered environment filled with randomly shaped and located non-convex obstacles.L'accessibilitéest un facteur important pris en compte dans la validation et la vérification en phase de conception du produit et augmente généralement le temps et les coûts de cette phase. Ce domaine de recherche a eu un regain d’intérêt ces quinze dernières années avec notamment de nouveaux planificateurs de mouvement. Cependant, les performances de ces méthodes sont généralement très faibles lorsque le problème se caractérise par des passages étroits des assemblages complexes composées d'un grand nombre de pièces. Cela conduit souvent àdes scènes àforte densitéd'obstacles. Malheureusement, les manipulations manuelles des humains dans le passage étroit montrent toujours beaucoup de difficultés en raison des limitations des dispositifs interactifs ou la capacitécognitive. Pendant ce temps, les défis de l'analyse de la réponse finale des utilisateurs dans le processus de conception promeut l'intégration avec la participation directe des concepteurs.Afin d'accélérer la planification dans le passage étroit et trouver le chemin le plus conforme aux préférences de l'utilisateur, une nouvelle méthode de planification de mouvement interactif est proposée. Nous avons soulignéla performance de notre algorithme dans certains scénarios difficiles en 2D et 3D environnement.Ensuite, une hypothèse est proposésur la corrélation entre la structure topologique du scénario et la trajectoire dans le passage étroit. La méthode basée sur les courbures est utilisée pour explorer cette corrélation et un cadre de planification de mouvement interactif qui peut apprendre de l'expérience est construit dans cette thèse. Nous soulignons la performance de notre cadre sur un problème difficile en 2D, dans lequel un objet non-convexe passe à travers un environnement encombrérempli d'obstacles non-convexes de forme aléatoire et situés