On the performance of sampling-based optimal motion planners

Sampling based algorithms provide efficient methods of solving robot motion planning problem. The advantage of these approaches is the ease of their implementation and their computational efficiency. These algorithms are probabilistically complete i.e. they will find a solution if one exists, given a suitable run time. The drawback of sampling based planners is that there is no guarantee of the quality of their solutions. In fact, it was proven that their probability of reaching an optimal solution approaches zero. A breakthrough in sampling planning was the proposal of optimal based sampling planners. Current optimal planners are characterized with asymptotic optimality i.e. they reach an optimal solutions as time approaches infinity. Motivated by the slow convergence of optimal planners, post-processing and heuristic approach have been suggested. Due to the nature of the sampling based planners, their implementation requires tuning and selection of a large number of parameters that are often overlooked. This paper presents the performance study of an optimal planner under different parameters and heuristics. We also propose a modification in the algorithm to improve the convergence rate towards an optimal solution

Path planning for robotic truss assembly

A new Potential Fields approach to the robotic path planning problem is proposed and implemented. Our approach, which is based on one originally proposed by Munger, computes an incremental joint vector based upon attraction to a goal and repulsion from obstacles. By repetitively adding and computing these 'steps', it is hoped (but not guaranteed) that the robot will reach its goal. An attractive force exerted by the goal is found by solving for the the minimum norm solution to the linear Jacobian equation. A repulsive force between obstacles and the robot's links is used to avoid collisions. Its magnitude is inversely proportional to the distance. Together, these forces make the goal the global minimum potential point, but local minima can stop the robot from ever reaching that point. Our approach improves on a basic, potential field paradigm developed by Munger by using an active, adaptive field - what we will call a 'flexible' potential field. Active fields are stronger when objects move towards one another and weaker when they move apart. An adaptive field's strength is individually tailored to be just strong enough to avoid any collision. In addition to the local planner, a global planning algorithm helps the planner to avoid local field minima by providing subgoals. These subgoals are based on the obstacles which caused the local planner to fail. A best-first search algorithm A* is used for graph search

A Heuristic and complete planner for the classical mover`s problem

We present a motion planner for the classical mover`s problem in three dimensions that is both resolution-complete and efficient in that it has performance commensurate with task difficulty. It is based on the SANDROS search strategy, which uses a hierarchical, multi-resolution representation of the configuration space along with a generate-and-test paradigm for solution paths. This planner can control the trade-offs between the computation resource and algorithmic completeness/solution path quality, and thus can fully utilize the available computing power. It is useful for navigation of mobile robots, submarines and spacecraft, or part motion feasibility in assembly planning

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

Trajectory planning for industrial robot using genetic algorithms

En las últimas décadas, debido la importancia de sus aplicaciones, se han propuesto muchas investigaciones sobre la planificación de caminos y trayectorias para los manipuladores, algunos de los ámbitos en los que pueden encontrarse ejemplos de aplicación son; la robótica industrial, sistemas autónomos, creación de prototipos virtuales y diseño de fármacos asistido por ordenador. Por otro lado, los algoritmos evolutivos se han aplicado en muchos campos, lo que motiva el interés del autor por investigar sobre su aplicación a la planificación de caminos y trayectorias en robots industriales. En este trabajo se ha llevado a cabo una búsqueda exhaustiva de la literatura existente relacionada con la tesis, que ha servido para crear una completa base de datos utilizada para realizar un examen detallado de la evolución histórica desde sus orígenes al estado actual de la técnica y las últimas tendencias. Esta tesis presenta una nueva metodología que utiliza algoritmos genéticos para desarrollar y evaluar técnicas para la planificación de caminos y trayectorias. El conocimiento de problemas específicos y el conocimiento heurístico se incorporan a la codificación, la evaluación y los operadores genéticos del algoritmo. Esta metodología introduce nuevos enfoques con el objetivo de resolver el problema de la planificación de caminos y la planificación de trayectorias para sistemas robóticos industriales que operan en entornos 3D con obstáculos estáticos, y que ha llevado a la creación de dos algoritmos (de alguna manera similares, con algunas variaciones), que son capaces de resolver los problemas de planificación mencionados. El modelado de los obstáculos se ha realizado mediante el uso de combinaciones de objetos geométricos simples (esferas, cilindros, y los planos), de modo que se obtiene un algoritmo eficiente para la prevención de colisiones. El algoritmo de planificación de caminos se basa en técnicas de optimización globales, usando algoritmos genéticos para minimizar una función objetivo considerando restricciones para evitar las colisiones con los obstáculos. El camino está compuesto de configuraciones adyacentes obtenidas mediante una técnica de optimización construida con algoritmos genéticos, buscando minimizar una función multiobjetivo donde intervienen la distancia entre los puntos significativos de las dos configuraciones adyacentes, así como la distancia desde los puntos de la configuración actual a la final. El planteamiento del problema mediante algoritmos genéticos requiere de una modelización acorde al procedimiento, definiendo los individuos y operadores capaces de proporcionar soluciones eficientes para el problema.Abu-Dakka, FJM. (2011). Trajectory planning for industrial robot using genetic algorithms [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/10294Palanci

A Motion Planner For Robot Manipulators Based on Support Vector Machines

ABSTRACT Moving a robot between two configurations without making a collision is of high importance in planning problems. Sampling-based planners have gained popularity due to their acceptable performance in practical situations. This body of work introduces the notion of a risk function that is provided using the Support Vector Machine (SVM) algorithm to find safe configurations in a sampled configuration space. A configuration is called safe if it is placed at maximum dis­tance from surrounding obstacle samples. Compared to previous solutions, this function is less sensitive to a selected sampling method and resolution. The proposed function is first used as a repulsive potential field in a local SVM-based planner. Afterwards, a global planner using the notion of the risk function is suggested to address some of the shortcomings of the suggested local planner. The proposed global planner is able to solve a problem with fewer number of milestones and less number of referrals to the collision detection module in comparison to the classical Probabilistic Roadmap Planner (PRM). The two proposed methods are evaluated in both simulated and experimental environments and the results are reported

Mathematical Modelling for Load Balancing and Minimization of Coordination Losses in Multirobot Stations

The automotive industry is moving from mass production towards an individualized production, in order to improve product quality and reduce costs and material waste. This thesis concerns aspects of load balancing of industrial robots in the automotive manufacturing industry, considering efficient algorithms required by an individualized production. The goal of the load balancing problem is to improve the equipment utilization. Several approaches for solving the load balancing problem are presented along with details on mathematical tools and subroutines employed.Our contributions to the solution of the load balancing problem are manifold. First, to circumvent robot coordination we have constructed disjoint robot programs, which require no coordination schemes, are more flexible, admit competitive cycle times for some industrial instances, and may be preferred in an individualized production. Second, since solving the task assignment problem for generating the disjoint robot programs was found to be unreasonably time-consuming, we modelled it as a generalized unrelated parallel machine problem with set packing constraints and suggested a tighter model formulation, which was proven to be much more tractable for a branch--and--cut solver. Third, within continuous collision detection it needs to be determined whether the sweeps of multiple moving robots are disjoint. Our solution uses the maximum velocity of each robot along with distance computations at certain robot configurations to derive a function that provides lower bounds on the minimum distance between the sweeps. The lower bounding function is iteratively minimized and updated with new distance information; our method is substantially faster than previously developed methods

General techniques for constrained motion planning

This report presents automatic motion planning algorithms for robotic manipulators performing a variety of tasks. Given a task and a robot manipulator equipped with a tool in its hand, the motion planners compute robot motions to complete the task while respecting manipulator kinematic constraints and avoiding collisions with objects in the robot`s work space. To handle the high complexity of the motion planning problem, a sophisticated search strategy called SANDROS is developed and used to solve many variations of the motion planning problem. To facilitate systematic development of motion planning algorithms, robotic tasks are classified into three categories according to the dimension of the manifold the robot tool has to travel: visit-point (0 dimensional), trace-curve (1 dimensional) and cover-surface (2 dimensional) tasks. The motion planner for a particular dimension is used as a sub-module by the motion planner for the next-higher dimension. This hierarchy of motion planners has led to a set of compact and systematic algorithms that can plan robot motions for many types of robotic operations. In addition, an algorithm is developed that determines the optimal robot-base configuration for minimum cycle time. The SANDROS search paradigm is complete in that it finds a solution path if one exists, up to a user specified resolution. Although its worst-case time complexity is exponential in the degrees of freedom of the manipulator, its average performance is commensurate with the complexity of the solution path. Since solution paths for most of motion planning problems consist of a few monotone segments, the motion planners based on SANDROS search strategy show approximately two-orders of magnitude improvements over existing complete algorithms

Motion planning in 2D and 3D with rotation

Imperial Users onl