A Topological Approach to Globally-Optimal Redundancy Resolution with Dynamic Programming

Ferrentino, Enrico; Chiacchio, Pasquale

doi:10.1007/978-3-319-78963-7_11

Cited by 8 publications

(4 citation statements)

References 11 publications

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“…Dynamic programming improves the original search algorithm with exponential time complexity into an algorithm with polynomial time complexity. The key is to solve the redundancy, which is the fundamental purpose of the dynamic programming algorithm [7]. That is, the sub-problems are not independent, and a sub-problem may be used multiple times in the next stage of decision-making.…”

Section: Overlap Of Sub-problemsmentioning

confidence: 99%

Optimizing Operations Management and Business Analytics Strategies under Uncertainty: Dynamic Programming

2023

AEMPS

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Dynamic programming is a method used in mathematics, management science, computer science, economics, and bioinformatics to solve complex problems by decomposing the original problem into relatively simple sub-problems. Dynamic programming is often applicable to problems with overlapping subproblems and optimal substructure properties. This paper employs a comprehensive research approach of literature review, as well as empirical analysis and case studies to investigate the topic and demonstrate the practicality and effectiveness of dynamic programming in solving complex decision-making problems. However, the curse of dimensionality poses challenges when dealing with high-dimensional decision spaces, requiring approximate dynamic programming methods, including reinforcement learning algorithms. Therefore, when dealing with more complex dynamic programming problems, it is also essential to use program construction tools such as Python to help design a program that can optimize the problem. Therefore, in the learning of dynamic programming, in addition to the correct understanding of basic concepts and methods, specific problems must be analyzed and dealt with in detail, models should be built with rich imagination, and solutions should be solved with creative skills.

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Section: Overlap Of Sub-problemsmentioning

confidence: 99%

Optimizing Operations Management and Business Analytics Strategies under Uncertainty: Dynamic Programming

2023

AEMPS

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“…The algorithm in [8,18] has been modified to include the stiffness optimization on the last waypoint, as well as to consider the specific objective function and constraints that characterize the LABOR use case. Its pseudo-code is provided in Algorithm 1.…”

Section: Algorithmic Implementationmentioning

confidence: 99%

Planning of efficient trajectories in robotized assembly of aerostructures exploiting kinematic redundancy

2021

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Aerospace production volumes have increased over time and robotic solutions have been progressively introduced in the aeronautic assembly lines to achieve high-quality standards, high production rates, flexibility and cost reduction. Robotic workcells are sometimes characterized by robots mounted on slides to increase the robot workspace. The slide introduces an additional degree of freedom, making the system kinematically redundant, but this feature is rarely used to enhance performances. The paper proposes a new concept in trajectory planning, that exploits the redundancy to satisfy additional requirements. A dynamic programming technique is adopted, which computes optimized trajectories, minimizing or maximizing the performance indices of interest. The use case is defined on the LABOR (Lean robotized AssemBly and cOntrol of composite aeRostructures) project which adopts two cooperating six-axis robots mounted on linear axes to perform assembly operations on fuselage panels. Considering the needs of this workcell, unnecessary robot movements are minimized to increase safety, the mechanical stiffness is maximized to increase stability during the drilling operations, collisions are avoided, while joint limits and the available planning time are respected. Experiments are performed in a simulation environment, where the optimal trajectories are executed, highlighting the resulting performances and improvements with respect to non-optimized solutions.

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“…Sin embargo, la trayectoria no se planea de forma completamente autónoma, ya que se requiere que un operador seleccione una serie de puntos intermedios. En (Ferrentino and Chiacchio, 2019), se realiza una búsqueda exhaustiva en mapas de factibilidad empleando programación dinámica para encontrar la trayectoria óptima global.…”

Section: Introductionunclassified

Planificación de trayectorias en robots redundantes con mapas de factibilidad y RRT

Fabregat Jaen,

Peidró Vidal,

Soler

et al. 2023

XLIV Jornadas De Automática: Libro De Actas: Universidad De Zaragoza, Escuela De Ingeniería Y Arquitectura, 6, 7 Y 8 De Septiem

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Los manipuladores redundantes ofrecen múltiples ventajas, tales como manipulabilidad mejorada, o evasión de singularidades u obstáculos. Sin embargo, la redundancia cinemática también introduce retos adicionales, como la necesidad de resolver un problema de cinemática inversa indeterminado. Este artículo presenta un método novedoso para la planificación de trayectorias de manipuladores redundantes, basado en la exploración de mapas de factibilidad. El método propuesto es una extensión del algoritmo Rapidly-exploring Random Trees (RRT), modificado para explorar el espacio redundante de la tarea con la finalidad de encontrar un camino factible subóptimo en el espacio articular, sacrificando optimalidad por escalabilidad a mayor número de grados de redundancia. El método es capaz de seguir una trayectoria dada en el espacio de la tarea, mientras considera otras restricciones, como la evasión de obstáculos o límites articulares. El método se ha validado en simulación.

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A Topological Approach to Globally-Optimal Redundancy Resolution with Dynamic Programming

Cited by 8 publications

References 11 publications

Optimizing Operations Management and Business Analytics Strategies under Uncertainty: Dynamic Programming

Optimizing Operations Management and Business Analytics Strategies under Uncertainty: Dynamic Programming

Planning of efficient trajectories in robotized assembly of aerostructures exploiting kinematic redundancy

Planificación de trayectorias en robots redundantes con mapas de factibilidad y RRT

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