Asymptotically Optimal Sampling-Based Motion Planning Methods

Motion planning is a fundamental problem in autonomous robotics that requires finding a path to a specified goal that avoids obstacles and takes into account a robot's limitations and constraints. It is often desirable for this path to also optimize a cost function, such as path length. Formal path-quality guarantees for continuously valued search spaces are an active area of research interest. Recent results have proven that some sampling-based planning methods probabilistically converge toward the optimal solution as computational effort approaches infinity. This survey summarizes the assumptions behind these popular asymptotically optimal techniques and provides an introduction to the significant ongoing research on this topic.Comment: Posted with permission from the Annual Review of Control, Robotics, and Autonomous Systems, Volume 4. Copyright 2021 by Annual Reviews, https://www.annualreviews.org/. 25 pages. 2 figure

Sampling-Based Motion Planning: A Comparative Review

Sampling-based motion planning is one of the fundamental paradigms to generate robot motions, and a cornerstone of robotics research. This comparative review provides an up-to-date guideline and reference manual for the use of sampling-based motion planning algorithms. This includes a history of motion planning, an overview about the most successful planners, and a discussion on their properties. It is also shown how planners can handle special cases and how extensions of motion planning can be accommodated. To put sampling-based motion planning into a larger context, a discussion of alternative motion generation frameworks is presented which highlights their respective differences to sampling-based motion planning. Finally, a set of sampling-based motion planners are compared on 24 challenging planning problems. This evaluation gives insights into which planners perform well in which situations and where future research would be required. This comparative review thereby provides not only a useful reference manual for researchers in the field, but also a guideline for practitioners to make informed algorithmic decisions.Comment: 25 pages, 7 figures, Accepted for Volume 7 (2024) of the Annual Review of Control, Robotics, and Autonomous System

Bidirectional Sampling Based Search Without Two Point Boundary Value Solution

Bidirectional motion planning approaches decrease planning time, on average, compared to their unidirectional counterparts. In single-query feasible motion planning, using bidirectional search to find a continuous motion plan requires an edge connection between the forward and reverse search trees. Such a tree-tree connection requires solving a two-point Boundary Value Problem (BVP). However, a two-point BVP solution can be difficult or impossible to calculate for many systems. We present a novel bidirectional search strategy that does not require solving the two-point BVP. Instead of connecting the forward and reverse trees directly, the reverse tree's cost information is used as a guiding heuristic for the forward search. This enables the forward search to quickly converge to a feasible solution without solving the two-point BVP. We propose two new algorithms (GBRRT and GABRRT) that use this strategy and run multiple software simulations using multiple dynamical systems and real-world hardware experiments to show that our algorithms perform on-par or better than existing state-of-the-art methods in quickly finding an initial feasible solution.Comment: Journal version (Video: https://youtu.be/Rumg66UHfyQ

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