Miroslav Kulich

Robust data fusion with occupancy grid

Accurate models of the environment are a crucial requirement for autonomous mobile robots. The process of how to acquire knowledge about the operating environment is one of the most challenging problems in this research area. The quality of the model depends on the number and types of sensors used. Occupancy grids are the most common low-level models of the environment used in robotics for fusion of noisy data. This paper first introduces a novel method for building an occupancy grid from a monocular color camera. The next part of the work describes a method for fusion of camera data with data from a rangefinder. The final part presents a new method for measuring the quality of the occupancy grid based on the quality of the path created by the grid. The methods were experimentally verified with an indoor experimental robot at the Czech Technical University.

SyRoTek—Distance Teaching of Mobile Robotics

E-learning is a modern and effective approach for training in various areas and at different levels of education. This paper gives an overview of SyRoTek, an e-learning platform for mobile robotics, artificial intelligence, control engineering, and related domains. SyRoTek provides remote access to a set of fully autonomous mobile robots placed in a restricted area with dynamically reconfigurable obstacles, which enables solving a huge variety of problems. A user is able to control the robots in real time by their own developed algorithms as well as being able to analyze gathered data and observe activity of the robots by provided interfaces. The system is currently used for education at the Czech Technical University in Prague, Prague, Czech Republic, and at the University of Buenos Aires, Buenos, Aires, Argentina, and it is freely accessible to other institutions. In addition to the system overview, this paper presents the experience gained from the actual deployment of the system in teaching activities.

Goal assignment using distance cost in multi-robot exploration

In this paper, we discuss the problem of goal assignment in the multi-robot exploration task. The presented work is focused on the underlying optimal assignment problem of the multi-robot task allocation that is addressed by three state-of-the art approaches. In addition, we propose a novel exploration strategy considering allocation of all current goals (not only immediate goal) for each robot, which leads to the multiple traveling salesman problem formulation. Although the problem is strongly NP-hard, we show its approximate solution is computationally feasible and its overall requirements are competitive to the previous approaches. The proposed approach and three well-known approaches are compared in series of problems considering various numbers of robots and sensor ranges. Based on the evaluation of the results the proposed exploration strategy provides shorter exploration times than the former approaches.

An application of the self-organizing map in the non-Euclidean Traveling Salesman Problem

An application of the self-organizing map (SOM) to the Traveling Salesman Problem (TSP) has been reported by many researchers, however these approaches are mainly focused on the Euclidean TSP variant. We consider the TSP as a problem formulation for the multi-goal path planning problem in which paths among obstacles have to be found. We apply a simple approximation of the shortest path that seems to be suitable for the SOM adaptation procedure. The approximation is based on a geometrical interpretation of SOM, where weights of neurons represent nodes that are placed in the polygonal domain. The approximation is verified in a set of real problems and experimental results show feasibility of the proposed approach for the SOM based solution of the non-Euclidean TSP.

European Robotics Symposium 2008

Adaptive Multiple Resources Consumption Control for an Autonomous Rover.- Adaptive Snake Robot Locomotion: A Benchmarking Facility for Experiments.- Architecture for Neuronal Cell Control of a Mobile Robot.- The Ares Robot: Case Study of an Affordable Service Robot.- Balancing the Information Gain Against the Movement Cost for Multi-robot Frontier Exploration.- Compiling POMDP Models for a Multimodal Service Robot from Background Knowledge.- Constraint Based Object State Modeling.- A COTS-Based Mini Unmanned Aerial Vehicle (SR-H3) for Security, Environmental Monitoring and Surveillance Operations: Design and Test.- Eyes-Neck Coordination Using Chaos.- Formation Graphs and Decentralized Formation Control of Multi Vehicles with Kinematics Constraints.- Global Urban Localization of an Outdoor Mobile Robot with Genetic Algorithms.- Grip Force Control Using Vision-Based Tactile Sensor for Dexterous Handling.- HNG: A Robust Architecture for Mobile Robots Systems.- Information Relative Map Going Toward Constant Time SLAM.- Measuring Motion Expressiveness in Wheeled Mobile Robots.- Modeling, Simulation and Control of Pneumatic Jumping Robot.- Multilayer Perceptron Adaptive Dynamic Control of Mobile Robots: Experimental Validation.- Path Planning and Tracking Control for an Automatic Parking Assist System.- Performance Evaluation of Ultrasonic Arc Map Processing Techniques by Active Snake Contours.- Planning Robust Landmarks for Sensor Based Motion.- Postural Control on a Quadruped Robot Using Lateral Tilt: A Dynamical System Approach.- Propose of a Benchmark for Pole Climbing Robots.- Rats Life: A Cognitive Robotics Benchmark.- Reactive Trajectory Deformation to Navigate Dynamic Environments.- Recovery in Autonomous Robot Swarms.- Robot Force/Position Tracking on a Surface of Unknown Orientation.- Scalable Operators for Feature Extraction on 3-D Data.- Semi-autonomous Learning of an RFID Sensor Model for Mobile Robot Self-localization.- A Simple Visual Navigation System with Convergence Property.- Stability of On-Line and On-Board Evolving of Adaptive Collective Behavior.- A Unified Framework for Whole-Body Humanoid Robot Control with Multiple Constraints and Contacts.- Visual Approaches for Handle Recognition.- Visual Top-Down Attention Framework for Robots in Dynamic Environments.- Visual Topological Mapping.- 3D Mapping and Localization Using Leveled Map Accelerated ICP.

Cooperative planning for heterogeneous teams in rescue operations

The first-place issue in cooperative activities of multiple entities in a common working environment is coordination and planning of systems activities ensuring completion of a common goal. As manual management of the problem is hard, finding a proper solution of the both tasks definitely leads to an efficient target behavior of these entities. In particular, the efficiency measured in terms of e.g. mission time, precise fulfilling of the common goal stands for the core issues in applications of a rescue type of scenarios and alike. The there under presented approach introduces a novel approach suitable for computer-aided or fully automatic mission planning and control within heterogenous teams of multiple robots and humans. The achieved performance of the approach has been experimentally verified and some interesting results are shown.

Growing neural gas efficiently

This paper presents optimization techniques that substantially speed up the Growing Neural Gas (GNG) algorithm. The GNG is an example of the Self-Organizing Map algorithm that is a subject of an intensive research interest in recent years as it is used in various practical applications. However, a poor time performance on large scale problems requiring neural networks with a high amount of nodes can be a limiting factor for further applications (e.g., cluster analysis, classification, 3-D reconstruction) or a wider usage. We propose two optimization techniques that are aimed exclusively on an efficient implementation of the GNG algorithm internal structure rather than on a modification of the original algorithm. The proposed optimizations preserve all properties of the GNG algorithm and enable to use it on large scale problems with reduced computational requirements in several orders of magnitude.

On distance utility in the exploration task

Performance of exploration strategies strongly depends on the process of determination of a next robot goal. Current approaches define different utility functions how to evaluate and select possible next goal candidates. One of the mostly used evaluation criteria is the distance cost that prefers candidates close to the current robot position. If this is the only criterion, simply the nearest candidate is chosen as the next goal. Although this criterion is simple to implement and gives feasible results there are situations where the criterion leads to wrong decisions. This paper presents the distance cost that reflects traveling through all goal candidates. The cost is determined as a solution of the Traveling Salesman Problem using the Chained Lin-Kernighan heuristic. The cost can be used as a stand-alone criterion as well as it can be integrated into complex decision systems. Experimental results for open-space and office-like experiments show that the proposed approach outperforms the standard one in the length of the traversed trajectory during the exploration while the computational burden is not significantly increased.

A Sensor Placement Algorithm for a Mobile Robot Inspection Planning

In this paper, we address the inspection planning problem to “see” the whole area of the given workspace by a mobile robot. The problem is decoupled into the sensor placement problem and the multi-goal path planning problem to visit found sensing locations. However the decoupled approach provides a feasible solution, its overall quality can be poor, because the sub-problems are solved independently. We propose a new randomized approach that considers the path planning problem during solution process of the sensor placement problem. The proposed algorithm is based on a guiding of the randomization process according to prior knowledge about the environment. The algorithm is compared with two algorithms already used in the inspection planning. Performance of the algorithms is evaluated in several real environments and for a set of visibility ranges. The proposed algorithm provides better solutions in both evaluated criterions: a number of sensing locations and a length of the inspection path.

Where's waldo at time t ? using spatio-temporal models for mobile robot search

We present a novel approach to mobile robot search for non-stationary objects in partially known environments. We formulate the search as a path planning problem in an environment where the probability of object occurrences at particular locations is a function of time. We propose to explicitly model the dynamics of the object occurrences by their frequency spectra. Using this spectral model, our path planning algorithm can construct plans that reflect the likelihoods of object locations at the time the search is performed. Three datasets collected over several months containing person and object occurrences in residential and office environments were chosen to evaluate the approach. Several types of spatio-temporal models were created for each of these datasets and the efficiency of the search method was assessed by measuring the time it took to locate a particular object. The results indicate that modeling the dynamics of object occurrences reduces the search time by 25% to 65% compared to maps that neglect these dynamics.