A Sampling-Based Tree Planner for Robot Navigation Among Movable Obstacles

This thesis proposes a planner that solves Navigation Among Movable Obstacles problems giving robots the ability to reason about the environment and choose when manipulating obstacles. The planner combines the A*-Search and the exploration strategy of the Kinodynamic Motion Planning by Interior-Exterior Cell Exploration algorithm. It is locally optimal and independent from the size of the map and from the number, shape, and position of obstacles. It assumes full world knowledgeope

Conditional Task and Motion Planning through an Effort-based Approach

This paper proposes a preliminary work on a Conditional Task and Motion Planning algorithm able to find a plan that minimizes robot efforts while solving assigned tasks. Unlike most of the existing approaches that replan a path only when it becomes unfeasible (e.g., no collision-free paths exist), the proposed algorithm takes into consideration a replanning procedure whenever an effort-saving is possible. The effort is here considered as the execution time, but it is extensible to the robot energy consumption. The computed plan is both conditional and dynamically adaptable to the unexpected environmental changes. Based on the theoretical analysis of the algorithm, authors expect their proposal to be complete and scalable. In progress experiments aim to prove this investigation

Task planning using physics-based heuristics on manipulation actions

© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.In order to solve mobile manipulation problems, the efficient combination of task and motion planning is usually required. Moreover, the incorporation of physics-based information has recently been taken into account in order to plan the tasks in a more realistic way. In the present paper, a task and motion planning framework is proposed based on a modified version of the Fast-Forward task planner that is guided by physics-based knowledge. The proposal uses manipulation knowledge for reasoning on symbolic literals (both in offline and online modes) taking into account geometric information in order to evaluate the applicability as well as feasibility of actions while evaluating the heuristic cost. It results in an efficient search of the state space and in the obtention of low-cost physically-feasible plans. The proposal has been implemented and is illustrated with a manipulation problem consisting of a mobile robot and some fixed and manipulatable objects.Peer ReviewedPostprint (author's final draft

Physics-based motion planning for grasping and manipulation

This thesis develops a series of knowledge-oriented physics-based motion planning algorithms for grasping and manipulation in cluttered an uncertain environments. The main idea is to use high-level knowledge-based reasoning to define the manipulation constraints that define the way how robot should interact with the objects in the environment. These interactions are modeled by incorporating the physics-based model of rigid body dynamics in planning. The first part of the thesis is focused on the techniques to integrate the knowledge with physics-based motion planning. The knowledge is represented in terms of ontologies, a prologbased knowledge inference process is introduced that defines the manipulation constraints. These constraints are used in the state validation procedure of sampling-based kinodynamic motion planners. The state propagator of the motion planner is replaced by a physics-engine that takes care of the kinodynamic and physics-based constraints. To make the interaction humanlike, a low-level physics-based reasoning process is introduced that dynamically varies the control bounds by evaluating the physical properties of the objects. As a result, power efficient motion plans are obtained. Furthermore, a framework has been presented to incorporate linear temporal logic within physics-based motion planning to handle complex temporal goals. The second part of this thesis develops physics-based motion planning approaches to plan in cluttered and uncertain environments. The uncertainty is considered in 1) objects’ poses due to sensing and due to complex robot-object or object-object interactions; 2) uncertainty in the contact dynamics (such as friction coefficient); 3) uncertainty in robot controls. The solution is framed with sampling-based kinodynamic motion planners that solve the problem in open-loop, i.e., it considers uncertainty while planning and computes the solution in such a way that it successfully moves the robot from the start to the goal configuration even if there is uncertainty in the system. To implement the above stated approaches, a knowledge-oriented physics-based motion planning tool is presented. It is developed by extending The Kautham Project, a C++ based tool for sampling-based motion planning. Finally, the current research challenges and future research directions to extend the above stated approaches are discussedEsta tesis desarrolla una serie de algoritmos de planificación del movimientos para la aprehensión y la manipulación de objetos en entornos desordenados e inciertos, basados en la física y el conocimiento. La idea principal es utilizar el razonamiento de alto nivel basado en el conocimiento para definir las restricciones de manipulación que definen la forma en que el robot debería interactuar con los objetos en el entorno. Estas interacciones se modelan incorporando en la planificación el modelo dinámico de los sólidos rígidos. La primera parte de la tesis se centra en las técnicas para integrar el conocimiento con la planificación del movimientos basada en la física. Para ello, se representa el conocimiento mediante ontologías y se introduce un proceso de razonamiento basado en Prolog para definir las restricciones de manipulación. Estas restricciones se usan en los procedimientos de validación del estado de los algoritmos de planificación basados en muestreo, cuyo propagador de estado se susituye por un motor basado en la física que tiene en cuenta las restricciones físicas y kinodinámicas. Además se ha implementado un proceso de razonamiento de bajo nivel que permite adaptar los límites de los controles aplicados a las propiedades físicas de los objetos. Complementariamente, se introduce un marco de desarrollo para la inclusión de la lógica temporal lineal en la planificación de movimientos basada en la física. La segunda parte de esta tesis extiende el enfoque a planificación del movimiento basados en la física en entornos desordenados e inciertos. La incertidumbre se considera en 1) las poses de los objetos debido a la medición y a las interacciones complejas robot-objeto y objeto-objeto; 2) incertidumbre en la dinámica de los contactos (como el coeficiente de fricción); 3) incertidumbre en los controles del robot. La solución se enmarca en planificadores kinodinámicos basados en muestro que solucionan el problema en lazo abierto, es decir que consideran la incertidumbre en la planificación para calcular una solución robusta que permita mover al robot de la configuración inicial a la final a pesar de la incertidumbre. Para implementar los enfoques mencionados anteriormente, se presenta una herramienta de planificación del movimientos basada en la física y guiada por el conocimiento, desarrollada extendiendo The Kautham Project, una herramienta implementada en C++ para la planificación de movimientos basada en muestreo. Finalmente, de discute los retos actuales y las futuras lineas de investigación a seguir para extender los enfoques presentados

Physics-based Motion Planning with Temporal Logic Specifications

One of the main foci of robotics is nowadays centered in providing a great degree of autonomy to robots. A fundamental step in this direction is to give them the ability to plan in discrete and continuous spaces to find the required motions to complete a complex task. In this line, some recent approaches describe tasks with Linear Temporal Logic (LTL) and reason on discrete actions to guide sampling-based motion planning, with the aim of finding dynamically-feasible motions that satisfy the temporal-logic task specifications. The present paper proposes an LTL planning approach enhanced with the use of ontologies to describe and reason about the task, on the one hand, and that includes physics-based motion planning to allow the purposeful manipulation of objects, on the other hand. The proposal has been implemented and is illustrated with didactic examples with a mobile robot in simple scenarios where some of the goals are occupied with objects that must be removed in order to fulfill the task.Comment: The 20th World Congress of the International Federation of Automatic Control, 9-14 July 201