Praćenje mobilnih robota korištenjem računalnog vida

In this paper a global vision scheme applied to a fast dynamic game – robot soccer is presented. The process of robots positions and orientations estimation is divided into two steps. In the first step, the Bayer format image is acquired from camera, then the RGB image is interpolated and pixels are classified into a finite number of classes. At the same time, a segmentation algorithm is used to find corresponding regions belonging to one of the classes. In the second step, all the regions are examined. Selection of the ones that are parts of the observed object is made by means of simple logic procedures. A data filtering is used to improve identified noisy data. The novelty is focused on the optimization of the image acquisition algorithm as well as the processing time needed to finish the estimation of possible object positions.Dan je opis algoritma globalnog računalnog vida primijenjenog na brzu dinamičku igru – robotski nogomet. Proces određivanja pozicija i orijentacija robota sastoji se od dva koraka. U prvom koraku, iz kamere prenosi se slika u Bayer formatu, iz koje se potom interpolira RGB slika, a pikseli se klasificiraju u konačan broj klasa. Istovremeno, primjenjuje se algoritam segmentacije kako bi se izdvojilo odgovarajuće regije slike koje odgovaraju jednoj od klasi boja. U drugom koraku ispituju se sve pronađene regije, a odabir onih koje odgovaraju traženim objektima provodi se jednostavnom logičkom procedurom. Koristi se filtriranje kako bi se umanjio šum u izmjerenim vrijednostima. Doprinos ovog rada sastoji se u optimizaciji algoritma interpolacije slike i algoritma obrade slike za mjerenje pozicija i orijentacija objekata

Coordinated Multi-Robotic Vehicles Navigation and Control in Shop Floor Automation

In this paper, we propose a global navigation function applied to model predictive control (MPC) for autonomous mobile robots, with application to warehouse automation. The approach considers static and dynamic obstacles and generates smooth, collision-free trajectories. The navigation function is based on a potential field derived from an E* graph search algorithm on a discrete occupancy grid and by bicubic interpolation. It has convergent behavior from anywhere to the target and is computed in advance to increase computational efficiency. The novel optimization strategy used in MPC combines a discrete set of velocity candidates with randomly perturbed candidates from particle swarm optimization. Adaptive horizon length is used to improve performance. The efficiency of the proposed approaches is validated using simulations and experimental results

Tracking-error

model-based predictive control for mobile robots in real tim

Fast and reliable alternative to encoder-based measurements of multiple 2-DOF rotary-linear transformable objects using a network of image sensors with application to table football

Simultaneous determination of linear and angular positions of rotating objects is a challenging task for traditional sensor applications and a very limited set of solutions is available. The paper presents a novel approach of replacing a set of traditional linear and rotational sensors by a small set of image sensors. While the camera’s angle of view can be a limiting factor in the tracking of multiple objects, the presented approach allows for a network of image sensors to extend the covered area. Furthermore, rich image data allows for the application of different data processing algorithms to effectively and accurately determine the object’s position. The proposed solution thus provides a set of smart visual encoders emulated by an image sensor or a network of image sensors for more demanding spatially distributed tasks. As a proof of concept, we present the results of the experiment in the target application, where a 1.6 MP image sensor was used to obtain sub-degree angular resolution at 600 rpm and thus exceeding the design parameters and requirements. The solution allows for a compact, cost-effective, and robust integration into the final product

Coordinated multi-robotic vehicles navigation and control in shop floor automation

In this paper, we propose a global navigation function applied to model predictive control (MPC) for autonomous mobile robots, with application to warehouse automation. The approach considers static and dynamic obstacles and generates smooth, collision-free trajectories. The navigation function is based on a potential field derived from an E* graph search algorithm on a discrete occupancy grid and by bicubic interpolation. It has convergent behavior from anywhere to the target and is computed in advance to increase computational efficiency. The novel optimization strategy used in MPC combines a discrete set of velocity candidates with randomly perturbed candidates from particle swarm optimization. Adaptive horizon length is used to improve performance. The efficiency of the proposed approaches is validated using simulations and experimental results

Effective parametrization of low order Bézier motion primitives for continuous-curvature path-planning applications

We propose a new parametrization of motion primitives based on Bézier curves that suits perfectly path-planning applications (and environment exploration) of wheeled mobile robots. The individual motion primitives can simply be calculated taking into account the requirements of path planning and the constraints of a vehicle, given in the form of the starting and ending orientations, velocities, turning rates, and curvatures. The proposed parametrization provides a natural geometric interpretation of the curve. The solution of the problem does not require optimization and is obtained by solving a system of simple polynomial equations. The resulting planar path composed of the primitives is guaranteed to be C2 continuous (the curvature is therefore continuous). The proposed primitives feature low order Bézier (third order polynomial) curves. This not only provides the final path with minimal required turns or unwanted oscillations that typically appear when using higher-order polynomial primitives due to Runge’s phenomenon but also makes the approach extremely computationally efficient. When used in path planning optimizers, the proposed primitives enable better convergence and conditionality of the optimization problem due to a low number of required parameters and a low order of the polynomials. The main contribution of the paper therefore lies in the analytic solution for the third-order Bézier motion primitive under given boundary conditions that guarantee continuous curvature of the composed spline path. The proposed approach is illustrated on some typical scenarios of path planning for wheeled mobile robots

Robot Navigation Based on Potential Field and Gradient Obtained by Bilinear Interpolation and a Grid-Based Search

The original concept of the artificial potential field in robot path planning has spawned a variety of extensions to address its main weakness, namely the formation of local minima in which the robot may be trapped. In this paper, a smooth navigation function combining the Dijkstra-based discrete static potential field evaluation with bilinear interpolation is proposed. The necessary modifications of the bilinear interpolation method are developed to make it applicable to the path-planning application. The effect is that the strategy makes it possible to solve the problem of the local minima, to generate smooth paths with moderate computational complexity, and at the same time, to largely preserve the product of the computationally intensive static plan. To cope with detected changes in the environment, a simple planning strategy is applied, bypassing the static plan with the solution of the A* algorithm to cope with dynamic discoveries. Results from several test environments are presented to illustrate the advantages of the developed navigation model

Potential Field Control of a Redundant Nonholonomic Mobile Manipulator with Corridor-Constrained Base Motion

This work proposes a solution for redundant nonholonomic mobile manipulator control with corridor constraints on base motion. The proposed control strategy applies an artificial potential field for base navigation to achieve joint control with desired trajectory tracking of the end effector. The overall kinematic model is created by describing the nonholonomic mobile platform and the kinematics of the manipulator. The objective function used consists of a primary control task that optimizes the joint variables to achieve the desired pose or trajectory of the end effector and a secondary control task that optimizes the joint variables for the base to support the arm and stay within the corridor. As a last priority, an additional optimization is introduced to optimize the maneuverability index. The proposed baseline navigation has global convergence without local minima and is computationally efficient. This is achieved by an optimal grid-based search on a coarse discrete grid and a bilinear interpolation to obtain a continuous potential function and its gradient. The performance of the proposed control algorithm is illustrated by several simulations of a mobile manipulator model derived for a Pal Tiago mobile base and an Emiko Franka Panda robotic manipulator