Libor Přeučil

David Woller, Viktor Kozák, Miroslav Kulich et al.

The Electric Vehicle Routing Problem (EVRP) extends the classical Vehicle Routing Problem (VRP) to reflect the growing use of electric and hybrid vehicles in logistics. Due to the variety of constraints considered in the literature, comparing approaches across different problem variants remains challenging. A minimalistic variant of the EVRP, known as the Capacitated Green Vehicle Routing Problem (CGVRP), was the focus of the CEC-12 competition held during the 2020 IEEE World Congress on Computational Intelligence. This paper presents the competition-winning approach, based on the Variable Neighborhood Search (VNS) metaheuristic. The method achieves the best results on the full competition dataset and also outperforms a more recent algorithm published afterward.

David Zahrádka, David Woller, Denisa Mužíková et al.

During the execution of Multi-Agent Path Finding (MAPF) plans in real-life applications, the MAPF assumption that the fleet's movement is perfectly synchronized does not apply. Since one or more of the agents may become delayed due to internal or external factors, it is often necessary to use a robust execution method to avoid collisions caused by desynchronization. Robust execution methods - such as the Action Dependency Graph (ADG) - synchronize the execution of risky actions, but often at the expense of increased plan execution cost, because it may require some agents to wait for the delayed agents. In such cases, the execution's cost can be reduced while still preserving safety by finding a new plan either by rescheduling (reordering the agents at crossroads) or the more general replanning capable of finding new paths. However, these operations may be costly, and the new plan may not even lead to lower execution cost than the original plan: for example, the two plans may be the exact same. Therefore, we estimate the benefit that can be achieved by single replanning in scenarios with delayed agents given an immediate state of the execution with a fully connected feed-forward neural network. The input to the neural network is a set of newly designed ADG-based features describing the robust execution's state and the impact of potential delays, and the output is an estimated benefit achievable by replanning. We train and test the network on a new labeled dataset containing 12,000 experiments, and we show that our proposed method is capable of reducing the impact of delays by up to 94.6% of the achievable reduction.

Tomáš Pivoňka, Libor Přeučil

Re-ranking is the second stage of a visual place recognition task, in which the system chooses the best-matching images from a pre-selected subset of candidates. Model-free approaches compute the image pair similarity based on a spatial comparison of corresponding local visual features, eliminating the need for computationally expensive estimation of a model describing transformation between images. The article focuses on model-free re-ranking based on standard local visual features and their applicability in long-term autonomy systems. It introduces three new model-free re-ranking methods that were designed primarily for deep-learned local visual features. These features evince high robustness to various appearance changes, which stands as a crucial property for use with long-term autonomy systems. All the introduced methods were employed in a new visual place recognition system together with the D2-net feature detector (Dusmanu, 2019) and experimentally tested with diverse, challenging public datasets. The obtained results are on par with current state-of-the-art methods, affirming that model-free approaches are a viable and worthwhile path for long-term visual place recognition.

Václav Truhlařík, Tomáš Pivoňka, Michal Kasarda et al.

Uniform and variable environments still remain a challenge for stable visual localization and mapping in mobile robot navigation. One of the possible approaches suitable for such environments is appearance-based teach-and-repeat navigation, relying on simplified localization and reactive robot motion control - all without a need for standard mapping. This work brings an innovative solution to such a system based on visual place recognition techniques. Here, the major contributions stand in the employment of a new visual place recognition technique, a novel horizontal shift computation approach, and a multi-platform system design for applications across various types of mobile robots. Secondly, a new public dataset for experimental testing of appearance-based navigation methods is introduced. Moreover, the work also provides real-world experimental testing and performance comparison of the introduced navigation system against other state-of-the-art methods. The results confirm that the new system outperforms existing methods in several testing scenarios, is capable of operation indoors and outdoors, and exhibits robustness to day and night scene variations.

Viktor Kozák, Karel Košnar, Jan Chudoba et al.

Inspection systems utilizing unmanned aerial vehicles (UAVs) equipped with thermal cameras are increasingly popular for the maintenance of photovoltaic (PV) power plants. However, automation of the inspection task is a challenging problem as it requires precise navigation to capture images from optimal distances and viewing angles. This paper presents a novel localization pipeline that directly integrates PV module detection with UAV navigation, allowing precise positioning during inspection. Detections are used to identify the power plant structures in the image and associate these with the power plant model. We define visually recognizable anchor points for the initial association and use object tracking to discern global associations. We present three distinct methods for visual segmentation of PV modules based on traditional computer vision, deep learning, and their fusion, and we evaluate their performance in relation to the proposed localization pipeline. The presented methods were verified and evaluated using custom aerial inspection data sets, demonstrating their robustness and applicability for real-time navigation. Additionally, we evaluate the influence of the power plant model's precision on the localization methods.

Viktor Kozák, Jan Chudoba, Libor Přeučil

An accurate and up-to-date model of a photovoltaic (PV) power plant is essential for its optimal operation and maintenance. However, such a model may not be easily available. This work introduces a novel approach for PV power plant mapping based on aerial overview images. It enables the automation of the mapping process while removing the reliance on third-party data. The presented mapping method takes advantage of the structural layout of the power plants to achieve detailed modeling down to the level of individual PV modules. The approach relies on visual segmentation of PV modules in overview images and the inference of structural information in each image, assigning modules to individual benches, rows, and columns. We identify visual keypoints related to the layout and use these to merge detections from multiple images while maintaining their structural integrity. The presented method was experimentally verified and evaluated on two different power plants. The final fusion of 3D positions and semantic structures results in a compact georeferenced model suitable for power plant maintenance.

Miroslav Kulich, Jiří Kubalík, Libor Přeučil

This paper deals with the problem of autonomous navigation of a mobile robot in an unknown 2D environment to fully explore the environment as efficiently as possible. We assume a terrestrial mobile robot equipped with a ranging sensor with a limited range and 360 degrees field of view. The key part of the exploration process is formulated as the d-Watchman Route Problem which consists of two coupled tasks - candidate goals generation and finding an optimal path through a subset of goals - which are solved in each exploration step. The latter has been defined as a constrained variant of the Generalized Traveling Salesman Problem and solved using an evolutionary algorithm. An evolutionary algorithm that uses an indirect representation and the nearest neighbor based constructive procedure was proposed to solve this problem. Individuals evolved in this evolutionary algorithm do not directly code the solutions to the problem. Instead, they represent sequences of instructions to construct a feasible solution. The problems with efficiently generating feasible solutions typically arising when applying traditional evolutionary algorithms to constrained optimization problems are eliminated this way. The proposed exploration framework was evaluated in a simulated environment on three maps and the time needed to explore the whole environment was compared to state-of-the-art exploration methods. Experimental results show that our method outperforms the compared ones in environments with a low density of obstacles by up to 12.5%, while it is slightly worse in office-like environments by 4.5% at maximum. The framework has also been deployed on a real robot to demonstrate the applicability of the proposed solution with real hardware.

Gaël Écorchard, Karel Košnar, Libor Přeučil

In a robotised warehouse, as in any place where robots move autonomously, a major issue is the localization or detection of human operators during their intervention in the work area of the robots. This paper introduces a wearable human localization system for large warehouses, which utilize preinstalled infrastructure used for localization of automated guided vehicles (AGVs). A monocular down-looking camera is detecting ground nodes, identifying them and computing the absolute position of the human to allow safe cooperation and coexistence of humans and AGVs in the same workspace. A virtual safety area around the human operator is set up and any AGV in this area is immediately stopped. In order to avoid triggering an emergency stop because of the short distance between robots and human operators, the trajectories of the robots have to be modified so that they do not interfere with the human. The purpose of this paper is to demonstrate an absolute visual localization method working in the challenging environment of an automated warehouse with low intensity of light, massively changing environment and using solely monocular camera placed on the human body.

Michaela Brejchová, Miroslav Kulich, Jan Petrš et al.

MoleMOD is a heterogeneous self-reconfigurable modular robotic system to be employed in architecture and civil engineering. In this paper we present two components of the MoleMOD infrastructure - a test environment and a planning algorithm. The test environment for simulation and visualization of active parts as well as passive blocks of MoleMOD is based on Gazebo - a powerful general-purpose robotic simulator. The key effort has been put into preparation of realistic models of passive and active components taking into account their physical characteristics. Moreover, given a starting configuration of the MoleMOD system and a final configuration an approach to plan collision-free trajectories for a fleet of active parts is introduced.

Jakub Hvězda, Miroslav Kulich, Libor Přeučil

In this chapter, we propose a novel approach for solving the coordination of a fleet of mobile robots, which consists of finding a set of collision-free trajectories for individual robots in the fleet. This problem is studied for several decades, and many approaches have been introduced. However, only a small minority is applicable in practice because of their properties - small computational requirement, producing solutions near-optimum, and completeness. The approach we present is based on a multi-robot variant of Rapidly Exploring Random Tree algorithm (RRT) for discrete environments and significantly improves its performance. Although the solutions generated by the approach are slightly worse than one of the best state-of-the-art algorithms presented in [23], it solves problems where ter Morses algorithm fails.

Tomislav Petković, Jakub Hvězda, Tomáš Rybecký et al.

With the substantial growth of logistics businesses the need for larger and more automated warehouses increases, thus giving rise to fully robotized shop-floors with mobile robots in charge of transporting and distributing goods. However, even in fully automatized warehouse systems the need for human intervention frequently arises, whether because of maintenance or because of fulfilling specific orders, thus bringing mobile robots and humans ever closer in an integrated warehouse environment. In order to ensure smooth and efficient operation of such a warehouse, paths of both robots and humans need to be carefully planned; however, due to the possibility of humans deviating from the assigned path, this becomes an even more challenging task. Given that, the supervising system should be able to recognize human intentions and its alternative paths in real-time. In this paper, we propose a framework for human deviation detection and intention recognition which outputs the most probable paths of the humans workers and the planner that acts accordingly by replanning for robots to move out of the human's path. Experimental results demonstrate that the proposed framework increases total number of deliveries, especially human deliveries, and reduces human-robot encounters.

Luis G. Camara, Libor Přeučil

We present a Visual Place Recognition system that follows the two-stage format common to image retrieval pipelines. The system encodes images of places by employing the activations of different layers of a pre-trained, off-the-shelf, VGG16 Convolutional Neural Network (CNN) architecture. In the first stage of our method and given a query image of a place, a number of top candidate images is retrieved from a previously stored database of places. In the second stage, we propose an exhaustive comparison of the query image against these candidates by encoding semantic and spatial information in the form of CNN features. Results from our approach outperform by a large margin state-of-the-art visual place recognition methods on five of the most commonly used benchmark datasets. The performance gain is especially remarkable on the most challenging datasets, with more than a twofold recognition improvement with respect to the latest published work.

Miroslav Kulich, Libor Přeučil, Juan José Miranda Bront

Two variants of multi-robot search for a stationary object in a priori known environment represented by a graph are studied in the paper. The first one is a generalization of the Traveling Deliveryman Problem where more than one deliveryman is allowed to be used in a solution. Similarly, the second variant is a generalization of the Graph Search Problem. A novel heuristics suitable for both problems is proposed which is furthermore integrated into a cluster-first route second approach. A set of computational experiments was conducted over the benchmark instances derived from the TSPLIB library. The results obtained show that even a standalone heuristics significantly outperforms the standard solution based on k- means clustering in quality of results as well as computational time. The integrated approach furthermore improves solutions found by a standalone heuristics by up to 15% at the expense of higher computational complexity.

Miroslav Kulich, Viktor Kozák, Libor Přeučil

In this paper, we present an integrated solution to memory-efficient environment modeling by an autonomous mobile robot equipped with a laser range-finder. Majority of nowadays approaches to autonomous environment modeling, called exploration, employs occupancy grids as environment representation where the working space is divided into small cells each storing information about the corresponding piece of the environment in the form of a probabilistic estimate of its state. In contrast, the presented approach uses a polygonal representation of the explored environment which consumes much less memory, enables fast planning and decision-making algorithms and it is thus reliable for large-scale environments. Simultaneous localization and mapping (SLAM) has been integrated into the presented framework to correct odometry errors and to provide accurate position estimates. This involves also a refinement of the already generated environment model in case of loop closure, i.e. when the robot detects that it revisited an already explored place. The framework has been implemented in Robot Operating System (ROS) and tested with a real robot in various environments. The experiments show that the polygonal representation with SLAM integrated can be used in the real world as it is fast, memory efficient and accurate. Moreover, the refinement can be executed in real-time during the exploration process.

Jakub Hvězda, Tomáš Rybecký, Miroslav Kulich et al.

In order to ensure efficient flow of goods in an automated warehouse and to guarantee its continuous distribution to/from picking stations in an effective way, decisions about which goods will be delivered to which particular picking station by which robot and by which path and in which time have to be made based on the current state of the warehouse. This task involves solution of two suproblems: (1) task allocation in which an assignment of robots to goods they have to deliver at a particular time is found and (2) planning of collision-free trajectories for particular robots (given their actual and goal positions). The trajectory planning problem is addressed in this paper taking into account specifics of automated warehouses. First, assignments of all robots are not known in advance, they are instead presented to the algorithm gradually one by one. Moreover, we do not optimize a makespan, but a throughput - the sum of individual robot plan costs. We introduce a novel approach to this problem which is based on the context-aware route planning algorithm [1]. The performed experimental results show that the proposed approach has a lower fail rate and produces results of higher quality than the original algorithm. This is redeemed by higher computational complexity which is nevertheless low enough for real-time planning.

Jakub Hvězda, Miroslav Kulich, Libor Přeučil

This paper addresses the problem of coordination of a fleet of mobile robots - the problem of finding an optimal set of collision-free trajectories for individual robots in the fleet. Many approaches have been introduced during the last decades, but a minority of them is practically applicable, i.e. fast, producing near-optimal solutions, and complete. We propose a novel probabilistic approach based on the Rapidly Exploring Random Tree algorithm (RRT) by significantly improving its multi-robot variant for discrete environments. The presented experimental results show that the proposed approach is fast enough to solve problems with tens of robots in seconds. Although the solutions generated by the approach are slightly worse than one of the best state-of-the-art algorithms presented in (ter Mors et al., 2010), it solves problems where ter Mors's algorithm fails.

Gaël Écorchard, Adam Heinrich, Libor Přeučil

This paper describes the design and implementation of a ground-related odometry sensor suitable for micro aerial vehicles. The sensor is based on a ground-facing camera and a single-board Linux-based embedded computer with a multimedia System on a Chip (SoC). The SoC features a hardware video encoder which is used to estimate the optical flow online. The optical flow is then used in combination with a distance sensor to estimate the vehicle's velocity. The proposed sensor is compared to a similar existing solution and evaluated in both indoor and outdoor environments.

Jérémy Taquet, Gaël Écorchard, Libor Přeučil

In a robotised warehouse a major issue is the safety of human operators in case of intervention in the work area of the robots. The current solution is to shut down every robot but it causes a loss of productivity, especially for large robotised warehouses. In order to avoid this loss we need to ensure the operator's security during his/her intervention in the warehouse without powering off the robots. The human operator needs to be localised in the warehouse and the trajectories of the robots have to be modified so that they do not interfere with the human. The purpose of this paper is to demonstrate a visual localisation method with visual elements that are already available in the current warehouse setup.

Miroslav Kulich, Roman Sushkov, Libor Přeučil

Routing problems are optimization problems that consider a set of goals in a graph to be visited by a vehicle (or a fleet of them) in an optimal way, while numerous constraints have to be satisfied. We present a solution based on multidimensional scaling which significantly reduces computational time of a self-organizing neural network solving a typical routing problem -- the Travelling Salesman Problem (TSP) in a polygonal domain, i.e. in a space where obstacles are represented by polygons. The preliminary results show feasibility of the proposed approach and although the results are presented only for TSP, the method is general so it can be used also for other variants of routing problems.

Miroslav Kulich, Vojtěch Lhotský, Libor Přeučil

Exploration of an unknown environment by a mobile robot is a complex task involving solution of many fundamental problems from data processing, localization to high-level planning and decision making. The exploration framework we developed is based on processing of RGBD data provided by a MS Kinect2 sensor, which allows to take advantage of state-of-the-art SLAM (Simultaneous Localization and Mapping) algorithms and to autonomously build a realistic 3D map of the environment with projected visual information about the scene. In this paper, we describe practical issues that appeared during deployment of the framework in real indoor and outdoor environments and discuss especially properties of SLAM algorithms processing MS Kinect2 data on an embedded computer.