18 research outputs found
An Evolutionary and Local Search Algorithm for Motion Planning of Two Manipulators
A method for obtaining coordinated motion plans of robot manipulators is presented.
A decoupled planning approach has been used; that is, the problem has been
decomposed into two subproblems: path planning, where a collision-free path is
found for each robot independently only considering fixed obstacles, and trajectory
planning, where the paths are timed and synchronized to avoid collisions with other
robots. This article focuses on the second problem. The proposed plan can easily be
implemented by programs written in most industrial robot programming languages.
The generated programs minimize the total motion time of the robots along their
paths. The method does not require accurate dynamic models of the robots and uses
an evolutionary algorithm followed by a local search which produces near optimal
solutions with a relatively small computational cost
Sampling-Based Trajectory (re)planning for Differentially Flat Systems: Application to a 3D Gantry Crane
In this paper, a sampling-based trajectory planning algorithm for a
laboratory-scale 3D gantry crane in an environment with static obstacles and
subject to bounds on the velocity and acceleration of the gantry crane system
is presented. The focus is on developing a fast motion planning algorithm for
differentially flat systems, where intermediate results can be stored and
reused for further tasks, such as replanning. The proposed approach is based on
the informed optimal rapidly exploring random tree algorithm (informed RRT*),
which is utilized to build trajectory trees that are reused for replanning when
the start and/or target states change. In contrast to state-of-the-art
approaches, the proposed motion planning algorithm incorporates a linear
quadratic minimum time (LQTM) local planner. Thus, dynamic properties such as
time optimality and the smoothness of the trajectory are directly considered in
the proposed algorithm. Moreover, by integrating the branch-and-bound method to
perform the pruning process on the trajectory tree, the proposed algorithm can
eliminate points in the tree that do not contribute to finding better
solutions. This helps to curb memory consumption and reduce the computational
complexity during motion (re)planning. Simulation results for a validated
mathematical model of a 3D gantry crane show the feasibility of the proposed
approach.Comment: Published at IFAC-PapersOnLine (13th IFAC Symposium on Robot Control
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Adaptive path planning: Algorithm and analysis
To address the need for a fast path planner, we present a learning algorithm that improves path planning by using past experience to enhance future performance. The algorithm relies on an existing path planner to provide solutions difficult tasks. From these solutions, an evolving sparse work of useful robot configurations is learned to support faster planning. More generally, the algorithm provides a framework in which a slow but effective planner may be improved both cost-wise and capability-wise by a faster but less effective planner coupled with experience. We analyze algorithm by formalizing the concept of improvability and deriving conditions under which a planner can be improved within the framework. The analysis is based on two stochastic models, one pessimistic (on task complexity), the other randomized (on experience utility). Using these models, we derive quantitative bounds to predict the learning behavior. We use these estimation tools to characterize the situations in which the algorithm is useful and to provide bounds on the training time. In particular, we show how to predict the maximum achievable speedup. Additionally, our analysis techniques are elementary and should be useful for studying other types of probabilistic learning as well
Parallele Bewegungsplanung in dynamischen Umgebungen
Dieser interne Bericht gibt einen Ueberblick ueber die aktuellen
Forschungsergebnisse aus dem gleichnamigen Projekt. Hierbei wird das
Problem der praktikablen Bewegungsplanung fuer Industrieroboter in
dynamischen Umgebungen angegangen. Der Grundalgorithmus ohne
wesentliche off-line Berechnungen basiert auf der A*-Suche und arbeitet
im impliziten, diskretisierten Konfigurationsraum. Die Kollisionen
werden im kartesischen Arbeitsraum durch hierarchische
Abstandsberechnung im gegebenen CAD-Modell erkannt. Eine zyklische
Aufteilung des Suchraums auf die einzelnen Prozessoren ermoeglicht eine
gut skalierbare Parallelverarbeitung fuer nachrichten-gekoppelte
Rechnersysteme. Die Leistungsfaehigkeit des Bewegungsplaners wird an
einem Satz von Benchmark-Problemen validiert. Unterstuetzt durch eine
optimale Diskretisierung zeigt der neuartige Ansatz einen linearen
Speedup. Fuer Umgebungen mit unbewegten Hindernissen liegen die
Laufzeiten im Sekundenbereich. Zur weiteren Beschleunigung der
Bewegungsplanung wird erstmalig eine heuristische hierarchische
Suche im impliziten Konfigurationsraum eingefuehrt. Fuer zweidimensionale
Benchmark-Probleme ergibt die Hierarchisierung eine starke Reduktion des
Suchaufwandes
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