Narrow passage identification using cell decomposition approximation and minimum spanning tree

Narrow passage problem is a problematic issue facing the sampling-based motion planner. In this paper, a new approach for narrow areas identification is proposed. The quad-tree cell-decomposition approximation is used to divide the free workspace into smaller cells, and build a graph of adjacency for these. The proposed method follows the graph edges and finds a sequence of cells, which have the same size, preceded and followed by a bigger cell size. The sequence, which has the pattern bigger-smaller-bigger cells size, is more likely to be located in a narrow area. The minimum spanning tree algorithm is used, to linearize adjacency graph. Many methods have been proposed to manipulate the edges cost in the graph, in order to make the generated spanning tree traverse through narrow passages in detectable ways. Five methods have been proposed, some of them give bad results, and the others give better on in simulationsNarrow passage problem is a problematic issue facing the sampling-based motion planner. In this paper, a new approach for narrow areas identification is proposed. The quad-tree cell-decomposition approximation is used to divide the free workspace into smaller cells, and build a graph of adjacency for these. The proposed method follows the graph edges and finds a sequence of cells, which have the same size, preceded and followed by a bigger cell size. The sequence, which has the pattern bigger-smaller-bigger cells size, is more likely to be located in a narrow area. The minimum spanning tree algorithm is used, to linearize adjacency graph. Many methods have been proposed to manipulate the edges cost in the graph, in order to make the generated spanning tree traverse through narrow passages in detectable ways. Five methods have been proposed, some of them give bad results, and the others give better on in simulation

Heuristic Approaches to Stochastic Quadratic Assignment Problem: VaR and CVar Cases

The goal of this paper is to continue our investigation of the heuristic approaches of solving thestochastic quadratic assignment problem (StoQAP) and provide additional insight into the behavior of di erentformulations that arise through the stochastic nature of the problem. The deterministic Quadratic AssignmentProblem (QAP) belongs to a class of well-known hard combinatorial optimization problems. Working with severalreal-world applications we have found that their QAP parameters can (and should) be considered as stochasticones. Thus, we review the StoQAP as a stochastic program and discuss its suitable deterministic reformulations.The two formulations we are going to investigate include two of the most used risk measures - Value at Risk(VaR) and Conditional Value at Risk (CVaR). The focus is on VaR and CVaR formulations and results of testcomputations for various instances of StoQAP solved by a genetic algorithm, which are presented and discussed

Two-Degree-of-Freedom Controller Tuning

Two-degree-of-freedom controllers have the ability to affect the dynamics of a system when the reference value changes. The answer to the question of parameter tuning for this additional lter still remains unclear we describe a new method for the design of said controller. We compare the behaviour of the controllers designed using the presented method versus the classic method on several instances

Design of Linear Quadratic Regulator (LQR) Based on Genetic Algorithm for Inverted Pendulum

One of the crucial problems in the dynamics and automatic control theory is balancing of an invertedpendulum robot by moving a cart along a horizontal path. This task is often used as a benchmark for di erentmethod comparison. In the practical use of the LQR method, the key problem is how to choose weight matricesQ and R correctly. To obtain satisfying results the experiments should be repeated many times with di erentparameters of weight matrices. These LQR parameters can be tuned by a Genetic Algorithm (GA) techniquefor getting better results. In our paper, the LQR parameters weight matrices Q and R which were tuned usingthe Genetic Algorithm. The simulations of the control problem are designed using MATLAB script code andMATLAB Simulink on an inverted pendulum model. The results show that the Genetic Algorithm is suitablefor tuning the parameters to give an optimal response. The control problem of the inverted pendulum was solvedsuccessfully

Comparison of Multiple Reinforcement Learning and Deep Reinforcement Learning Methods for the Task Aimed at Achieving the Goal

Reinforcement Learning (RL) and Deep Reinforcement Learning (DRL) methods are a promising approach to solving complex tasks in the real world with physical robots. In this paper, we compare several reinforcement  learning (Q-Learning, SARSA) and deep reinforcement learning (Deep Q-Network, Deep Sarsa) methods for a task aimed at achieving a specific goal using robotics arm UR3. The main optimization problem of this experiment is to find the best solution for each RL/DRL scenario and minimize the Euclidean distance accuracy error and smooth the resulting path by the Bézier spline method. The simulation and real word applications are controlled by the Robot Operating System (ROS). The learning environment is implemented using the OpenAI Gym library which uses the RVIZ simulation tool and the Gazebo 3D modeling tool for dynamics and kinematics

Stabilization of Higher Periodic Orbits of Chaotic maps using Permutation-selective Objective Function

International audienceThis paper deals with the design of an advanced objective function capable of proper evaluation of the solutions during the process of chaotic trajectory stabilisation into stable periodic motion by means of evolutionary metaheuristic optimization. The challenging problem of stabilisation of chaotic systems generates many unexpected difficulties. One of them is the evaluation of a sample stabilized run during optimization. Even more so, when the target state of the chaotic system is a stable cycle oscillating periodically between several target positions. In this study, a two-dimensional dynamical system, known as the Hénon map was used. The system is stabilized using Extended Time Delayed Auto Synchronization (ETDAS) method with Genetic Algorithm (GA) optimization. The solutions are evaluated by a permutation-selective objective function, which achieves significantly better results than conventional evaluation methods based on a common objective function

Rozpoznávání odrůd vinné révy na základě RGB obrázků pomocí hustě propojené konvoluční sítě

We present a pocket-size densely connected convolutional network (DenseNet) directed to classification of size-normalized colour images according to varieties of grapes captured in those images. We compare the DenseNet with three established small-size networks in terms of performance, inference time and model size. We propose a data augmentation that we use in training the networks. We train and evaluate the networks on in-field images. The trained networks distinguish between seven grapevine varieties and background, where four and three varieties, respectively, are of red and green grapes. Compared to the established networks, the DenseNet is characterized by near state-of-the-art performance, short inference time and minimal model size. All these aspects qualify the network for real-time, mobile and edge computing applications. The DenseNet opens possibilities for constructing affordable selective harvesters in accordance with agriculture 4.0.Představujeme hustě propojenou konvoluční síť kapesní velikosti (DenseNet) zaměřenou na klasifikaci barevných snímků normalizovaných podle velikosti podle odrůd hroznů zachycených na těchto snímcích. Srovnáváme síť DenseNet se třemi zavedenými sítěmi malé velikosti z hlediska výkonu, doby inference a velikosti modelu. Navrhujeme rozšíření dat, které používáme při trénování sítí. Sítě trénujeme a vyhodnocujeme na polních snímcích. Natrénované sítě rozlišují sedm odrůd vinné révy a pozadí, přičemž čtyři odrůdy jsou červené a tři zelené. V porovnání se zavedenými sítěmi se síť DenseNet vyznačuje výkonností blízkou nejmodernějším, krátkou dobou inference a minimální velikostí modelu. Všechny tyto aspekty kvalifikují síť pro aplikace v reálném čase, mobilní a okrajové výpočty. Síť DenseNet otevírá možnosti pro konstrukci cenově dostupných selektivních sklízečů v souladu se zemědělstvím 4.0