A practical multirobot localization system

We present a fast and precise vision-based software intended for multiple robot localization. The core component of the software is a novel and efficient algorithm for black and white pattern detection. The method is robust to variable lighting conditions, achieves sub-pixel precision and its computational complexity is independent of the processed image size. With off-the-shelf computational equipment and low-cost cameras, the core algorithm is able to process hundreds of images per second while tracking hundreds of objects with a millimeter precision. In addition, we present the method's mathematical model, which allows to estimate the expected localization precision, area of coverage, and processing speed from the camera's intrinsic parameters and hardware's processing capacity. The correctness of the presented model and performance of the algorithm in real-world conditions is verified in several experiments. Apart from the method description, we also make its source code public at \emph{http://purl.org/robotics/whycon}; so, it can be used as an enabling technology for various mobile robotic problems

COSΦ: Vision-based artificial pheromone system for robotic swarms

We propose a novel spatio-temporal mobile-robot exploration method for dynamic, human-populated environments. In contrast to other exploration methods that model the environment as being static, our spatio-temporal exploration method creates and maintains a world model that not only represents the environment's structure, but also its dynamics over time. Consideration of the world dynamics adds an extra, temporal dimension to the explored space and makes the exploration task a never-ending data-gathering process to keep the robot's environment model up-to-date. Thus, the crucial question is not only where, but also when to observe the explored environment. We address the problem by application of information-theoretic exploration to world representations that model the environment states' uncertainties as probabilistic functions of time. The predictive ability of the spatio-temporal model allows the exploration method to decide not only where, but also when to make environment observations. To verify the proposed approach, an evaluation of several exploration strategies and spatio-temporal models was carried out using real-world data gathered over several months. The evaluation indicates that through understanding of the environment dynamics, the proposed spatio-temporal exploration method could predict which locations were going to change at a specific time and use this knowledge to guide the robot. Such an ability is crucial for long-term deployment of mobile robots in human-populated spaces that change over time

An Aerial Robotics Investigation into the Stability, Coordination, and Movement of Strategies for Directing Swarm and Formation of Autonomous MAVs and Diverse Groups of Driverless Vehicles (UGVs)

This study will discuss the matter of movement communication and preparation of tight configurations of land & flying robots. Remotely Operated Cars (UGVs) and Unmanned Aerial Vehicles (UAVs), in specific Micro Aerial Vehicles (MAVs), would be used to fix circumstances where a creation of UGVs and UAVs, in specific Micro Aerial Vehicles (MAVs), should counteract their velocity and direction to finish a mission of traffic sequence to a targeted area. The motion planning and stabilisation strategy given here is a useful tool for deploying closely collaborating robot teams including both outdoor and indoor settings. The installation of large groups of Micro Aerial Vehicles (MAVs) in a legitimate (indoor and outdoor) environment without the use of auxiliary positioning applications (such as Vicon or GPS) is indeed a natural development in the area of autonomously flying systems. Stability, control, and trajectory planning techniques for guiding swarm or configurations of unmanned MAVs, as well as diverse groups with Unmanned Ground Vehicles (UGVs) operating alongside MAVs, will be discussed in greater detail. These approaches discussed all are designed for the use of inter squads in true complex scenarios even without necessity for worldwide translation or motion capture systems, as they are predicated on board optical comparative localisation of single MAVs. This multi - objective optimisation being an enabler for the introduction of swarming of tiny autonomous drones beyond the labs with equipment for precise robot positioning. Model Predictive Control (MPC) is being used to address a formations to goal territory issue, and the form drive idea is based on a simulated approach. The Particle swarm optimization approach is utilised for digital leader trajectories planning, as well as control and stabilisation of follows (MAVs and UGVs). The proposed technique could be tested in the future using a range of simulation and practical tests

Power-law distribution of long-term experimental data in swarm robotics

Bio-inspired aggregation is one of the most fundamental behaviours that has been studied in swarm robotic for more than two decades. Biology revealed that the environmental characteristics are very important factors in aggregation of social insects and other animals. In this paper, we study the effects of different environmental factors such as size and texture of aggregation cues using real robots. In addition, we propose a mathematical model to predict the behaviour of the aggregation during an experiment

ON CONSTRUCTION OF A RELIABLE GROUND TRUTH FOR EVALUATION OF VISUAL SLAM ALGORITHMS

In this work we are concerning the problem of localization accuracy evaluation of visual-based Simultaneous Localization and Mapping (SLAM) techniques. Quantitative evaluation of the SLAM algorithm performance is usually done using the established metrics of Relative pose error and Absolute trajectory error which require a precise and reliable ground truth. Such a ground truth is usually hard to obtain, while it requires an expensive external localization system. In this work we are proposing to use the SLAM algorithm itself to construct a reliable ground-truth by offline frame-by-frame processing. The generated ground-truth is suitable for evaluation of different SLAM systems, as well as for tuning the parametrization of the on-line SLAM. The presented practical experimental results indicate the feasibility of the proposed approach

Colias-Φ: an autonomous micro robot for artificial pheromone communication

Ants pheromone communication is an efficient mechanism which took inspiration from nature. It has been used in various artificial intelligence and multi robotics researches. This paper presents the development of an autonomous micro robot to be used in swarm robotic researches especially in pheromone based communication systems. The robot is an extended version of Colias micro robot with capability of decoding and following artificial pheromone trails. We utilize a low-cost experimental setup to implement pheromone-based scenarios using a flat LCD screen and a USB camera. The results of the performed experiments with group of robots demonstrated the feasibility of Colias-Φ to be used in pheromone based experiments

Monte Carlo localization for teach-and-repeat feature-based navigation

This work presents a combination of a teach-and-replay visual navigation and Monte Carlo localization methods. It improves a reliable teach-and-replay navigation method by replacing its dependency on precise dead-reckoning by introducing Monte Carlo localization to determine robot position along the learned path. In consequence, the navigation method becomes robust to dead-reckoning errors, can be started from at any point in the map and can deal with the `kidnapped robot' problem. Furthermore, the robot is localized with MCL only along the taught path, i.e. in one dimension, which does not require a high number of particles and significantly reduces the computational cost. Thus, the combination of MCL and teach-and-replay navigation mitigates the disadvantages of both methods. The method was tested using a P3-AT ground robot and a Parrot AR.Drone aerial robot over a long indoor corridor. Experiments show the validity of the approach and establish a solid base for continuing this work