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.

This paper addresses the problem of establishing efficient intralogistic systems, focusing on the generation of roadmaps on a given layout and the coordination of multiple Automated Guided Vehicles (AGVs). A simulation-based approach for automatic roadmap design is proposed. An event-based simulator is developed that uses ant-colony inspired optimization to generate roadmaps tailored to the specific characteristics of a given intralogistic problem, i.e., the plant layout, fleet size, statistical description of tasks, dispatching algorithm, etc. The generated solutions are evaluated with a Multi-Agent Path Finding (MAPF) simulator that uses a Safe Interval Path Planning (SIPP) algorithm. By analysing the system throughput, the optimal fleet size for the system is proposed. The approach is validated through various examples and benchmarked against existing methods in the literature. Note to Practitioners —Creating efficient roadmaps plays a key role in improving the efficiency of multi-AGV systems. Usually, roadmaps are designed by experts, which is time consuming and may lead to suboptimal solutions due to the complexity of the problem. The main objective of this work is to develop a methodology that allows the automatic generation of efficient roadmaps taking into account the specific characteristics of an intralogistic problem. The main advantage is the fast generation of solutions and their validation in terms of throughput. At the same time, the presented methodology allows a quick correction of the solution if one of the problem characteristics changes, without the need to change the layout itself. This provides industry practitioners with an effective tool that facilitates their work in setting up multi-AGV systems, thus saving time and costs.

In this paper we present a visual-control algorithm for driving a mobile robot along the reference trajectory. The configuration of the system consists of a two-wheeled differentially driven mobile robot that is observed by an overhead camera, which can be placed at arbitrary, but reasonable, inclination with respect to the ground plane. The controller must be capable of generating appropriate tangential and angular control velocities for the trajectory-tracking problem, based on the information received about the robot position obtained in the image. To be able to track the position of the robot through a sequence of images in real-time, the robot is marked with an artificial marker that can be distinguishably recognized by the image recognition subsystem. Using the property of differential flatness, a dynamic feedback compensator can be designed for the system, thereby extending the system into a linear form. The presented control algorithm for reference tracking combines a feedforward and a feedback loop, the structure also known as a two DOF control scheme. The feedforward part should drive the system to the vicinity of the reference trajectory and the feedback part should eliminate any errors that occur due to noise and other disturbances etc. The feedforward control can never achieve accurate reference following, but this deficiency can be eliminated with the introduction of the feedback loop. The design of the model predictive control is based on the linear error model. The model predictive control is given in analytical form, so the computational burden is kept at a reasonable level for real-time implementation. The control algorithm requires that a reference trajectory is at least twice differentiable function. A suitable approach to design such a trajectory is by exploiting some useful properties of the Bernstein-Bézier parametric curves. The simulation experiments as well as real system experiments on a robot normally used in the robot soccer small league prove the applicability of the presented control approach.

This paper presents an approach of depth image segmentation based on the Evolving Principal Component Clustering (EPCC) method, which exploits data locality in an ordered data stream. The parameters of linear prototypes, which are used to describe different clusters, are estimated in a recursive manner. The main contribution of this work is the extension and application of the EPCC to 3D space for recursive and real-time detection of flat connected surfaces based on linear segments, which are all detected in an evolving way. To obtain optimal results when processing homogeneous surfaces, we introduced two-step filtering for outlier detection within a clustering framework and considered the noise model, which allowed for the compensation of characteristic uncertainties that are introduced into the measurements of depth sensors. The developed algorithm was compared with well-known methods for point cloud segmentation. The proposed approach achieves better segmentation results over longer distances for which the signal-to-noise ratio is low, without prior filtering of the data. On the given database, an average rate higher than 90% was obtained for successfully detected flat surfaces, which indicates high performance when processing huge point clouds in a non-iterative manner.