Ing. Jiří Ulrich

Digital and mechatronic methods, paired with artificial intelligence and machine learning, are game-changing technologies in behavioral science. The central element of the most important pollinator species (honeybees) is the colony’s queen. The behavioral strategies of these ecologically important organisms are under-researched, due to the complexity of honeybees’ self-regulation and the difficulties of studying queens in their natural colony context. We created an autonomous robotic observation and behavioral analysis system aimed at 24/7 observation of the queen and her interactions with worker bees and comb cells, generating unique behavioral datasets of unprecedented length and quality. Significant key performance indicators of the queen and her social embedding in the colony were gathered by this tailored but also versatile robotic system. Data collected over 24-hour and 30-day periods demonstrate our system’s capability to extract key performance indicators on different system levels: Microscopic, mesoscopic, and macroscopic data are collected in parallel. Additionally, interactions between various agents are also observed and quantified. Long-term continuous observations yield high amounts of high-quality data when performed by an autonomous robot, going significantly beyond feasibly obtainable results of human observation methods or stationary camera systems. This allows a deep understanding of the innermost mechanisms of honeybees’ swarm-intelligent self-regulation as well as studying other ocial insect colonies, biocoenoses and ecosystems in novel ways. Social insects are keystone species in all ecosystems, thus understanding them better will be valuable to monitor, interpret, protect and even to restore our fragile ecosystems globally.

Despite the importance of honeybees as pollinators for the entire ecosystem and their recent decline threatening agricultural production, the dynamics of the living colony are not well understood. In our EU H2020 RoboRoyale project, we aim to support the pollination activity of the honeybees through robots interacting with the core element of the honeybee colony, the honeybee queen. In order to achieve that, we need to understand how the honeybee queen behaves and interacts with the surrounding worker bees. To gather the necessary data, we observe the queen with a moving camera, and occasionally, we instruct the system to perform selective observations elsewhere. In this paper, we deal with the problem of searching for the honeybee queen inside a living colony. We demonstrate that combining spatio-temporal models of queen presence with efficient search methods significantly decreases the time required to find her. This will minimize the chance of missing interesting data on the infrequent behaviors or queen-worker interactions, leading to a better understanding of the queen's behavior over time. Moreover, a faster search for the queen allows the robot to leave her more frequently and gather more data in other areas of the honeybee colony.

Nowadays, robots can operate in environments which are not tailored for them. This allows their deployments in changing and human-populated environments, which recent advances in machine learning methods enabled. The efficiency of these methods is largely determined by the quality of their training data. An up-to-date and well-balanced training dataset is paramount for achieving robust robot operation. To achieve long-term operation, the robot has to deal with perpetual environmental changes, forcing it to keep its models up-to-date. We present an exploration method allowing a mobile robot to gather high-quality data to update its models both while performing its duties and when idle, maximizing effectivity. The robot evaluates the quality of the data gathered in the past and based on that, it creates preferences which influence how often these locations are visited. This exploration method was integrated with a self-supervised visual teach-and-repeat pipeline. We show the precision and robustness of visual-based navigation to improve when using machine-learned models trained by our exploration method. Our research resulted in a robotic navigation system that can not only annotate its training data but also ensure that its training dataset is balanced and up-to-date. The codes, datasets, trained models and examples for our experiments can be found online for better reproducibility at .

Honeybees are one of the most important pollinators in the ecosystem. Unfortunately, the dynamics of living honeybee colonies are not well understood due to their complexity and difficulty of observation. In our project 'RoboRoyale', we build and operate a robot to be a part of a bio-hybrid system, which currently observes the honeybee queen in the colony and physically tracks it with a camera. Apart from tracking and observing the queen, the system needs to monitor the state of the honeybee comb which is most of the time occluded by workerbees. This introduces a necessary tradeoff between tracking the queen and visiting the rest of the hive to create a daily map. We aim to collect the necessary data more effectively. We evaluate several mapping methods that consider the previous observations and forecasted densities of bees occluding the view. To predict the presence of bees, we use previously established maps of dynamics developed for autonomy in human-populated environments. Using data from the last observational season, we show significant improvement of the informed comb mapping methods over our current system. This will allow us to use our resources more effectively in the upcoming season.

Honeybees are irreplaceable pollinators with a direct impact on the global food supply.Researchers focus on understanding the dynamics of colonies to support their health and growth.In our project “RoboRoyale”, we aim to strengthen the colony through miniature robots interacting with the honeybee queen.To assess the colony's health and the effect of the interactions, it is crucial to monitor the whole honeybee comb and its development.In this work, we introduce key components of a system capable of autonomously evaluating the state of the comb without any disturbance to the living colony.We evaluate several methods for visual mapping of the comb by a moving camera and several algorithms for detecting visible cells between occluding bees.By combining image stitching techniques with open cell detection and their localization, we show that it is possible to capture how the comb evolves over time.Our results lay the foundations for real-time monitoring of a honeybee comb, which could prove essential in honeybee and environmental research.

The recent advancement of deep reinforcement learning (DRL) contributed to robotics by allowing automatic controller design. The automatic controller design is a crucial approach for designing swarm robotic systems, which require more complex controllers than a single robot system to lead a desired collective behavior. Although the DRL-based controller design method showed its effectiveness for swarm robotic systems, the reliance on the central training server is a critical problem in real-world environments where robot-server communication is unstable or limited. We propose a novel federated learning (FL)-based DRL training strategy federated learning DDPG (FLDDPG) for use in swarm robotic applications. Through the comparison with baseline strategies under a limited communication bandwidth scenario, it is shown that the FLDDPG method resulted in higher robustness and generalization ability into a different environment and real robots, while the baseline strategies suffer from the limitation of communication bandwidth. This result suggests that the proposed method can benefit swarm robotic systems operating in environments with limited communication bandwidth, e.g., in high radiation, underwater, or subterranean environments.

This paper presents the concept of a robotic system collaborating with a swarm of social insects inside their hive. This robot consists of a micro- and macro-manipulator and a tracking system. The micro-manipulator uses bio-mimetic agents to interact with an individual specimen. The macro-manipulator positions and keeps the micro-manipulator's base around the given individual while moving in the hive. This individual is tracked by a fiducial marker-based visual detection and localisation system, which also provides positions of the bio-mimetic agents. The base of the system was experimentally verified in a honeybee observation hive, where it flawlessly tracked the honeybee queen for several hours, gathering sufficient data to extract the behaviours of honeybee workers in the queen's vicinity. These data were then used in simulation to verify if the micro-manipulator's bio-mimetic agents could mimic some of the honeybee workers' behaviours.

The ability to reliably determine its own position, as well as the position of surrounding objects, is crucial for any autonomous robot. While this can be achieved with a certain degree of reliability, augmenting the environment with artificial markers that make these tasks easier is often practical. This applies especially to the evaluation of robotic experiments, which often require exact ground truth data containing the positions of the robots. This paper proposes a new method for estimating the position and orientation of circular fiducial markers in 3D space. Simulated and real experiments show that our method achieved three times lower localisation error than the method it derived from. The experiments also indicate that our method outperforms state-of-the-art systems in terms of orientation estimation precision while maintaining similar or better accuracy in position estimation. Moreover, our method is computationally efficient, allowing it to detect and localise several markers in a fraction of the time required by the state-of-the-art fiducial markers. Furthermore, the presented method requires only an off-the-shelf camera and printed tags, can be quickly set up and works in natural light conditions outdoors. These properties make it a viable alternative to expensive high-end localisation systems.

Socia1 insects, especially honeybees, play an essential role in nature, and their recent decline threatens the stability of many ecosystems. The behaviour of social insect colonies is typically governed by a central individual, e.g., by the honeybee queen. The RoboRoyale project aims to use robots to interact with the queen to affect her behaviour and the entire colony’s activity. This paper presents a necessary component of such a robotic system, a method capable of real-time detection, localisation, and tracking of the honeybee queen inside a large colony. To overcome problems with occlusions and computational complexity, we propose to combine two vision-based methods for fiducial marker localisation and tracking. The experiments performed on the data captured from inside the beehives demonstrate that the resulting algorithm outperforms its predecessors in terms of detection precision, recall, and localisation accuracy. The achieved performance allowed us to integrate the method into a larger system capable of physically tracking a honeybee queen inside its colony. The ability to observe the queen in fine detail for prolonged periods of time already resulted in unique observations of queen-worker interactions. The knowledge will be crucial in designing a system capable of interacting with the honeybee queen and affecting her activity.

Despite the advances in mobile robotics, the introduction of autonomous robots in human-populated environments is rather slow. One of the fundamental reasons is the acceptance of robots by people directly affected by a robot's presence. Understanding human behavior and dynamics is essential for planning when and how robots should traverse busy environments without disrupting people's natural motion and causing irritation. Research has exploited various techniques to build spatio-temporal representations of people's presence and flows and compared their applicability to plan optimal paths in the future. Many comparisons of how dynamic map-building techniques show how one method compares on a dataset versus another, but without consistent datasets and high-quality comparison metrics, it is difficult to assess how these various methods compare as a whole and in specific tasks. This article proposes a methodology for creating high-quality criteria with interpretable results for comparing long-term spatio-temporal representations for human-aware path planning and human-aware navigation scheduling. Two criteria derived from the methodology are then applied to compare the representations built by the techniques found in the literature. The approaches are compared on a real-world, long-term dataset, and the conception is validated in a field experiment on a robotic platform deployed in a human-populated environment. Our results indicate that continuous spatio-temporal methods independently modeling spatial and temporal phenomena outperformed other modeling approaches. Our results provide a baseline for future work to compare a wide range of methods employed for long-term navigation and provide researchers with an understanding of how these various methods compare in various scenarios.

This paper proposes a new method for the full 6 degrees of free- dom pose estimation of a circular fiducial marker. This circular black-and-white planar marker provides a unique and versatile identification of individual markers while maintaining a real-time detection. Such a marker and the vision localisation system based on it is suitable for both external and self-localisation. Together with an off-the-shelf camera, the marker aims to provide a sufficient pose estimation accuracy to substitute the current high-end locali sation systems. In order to assess the performance of our proposed marker system, we evaluate its capabilities against the current state of-the-art methods in terms of their ability to estimate the 2D and 3D positions. For such purpose, a real-world dataset, inspired by typical applications in mobile and swarm robotics, was collected as the performance under the real conditions provides better insights into the method’s potential than an artificially simulated environ ment. The experiments performed show that the method presented here achieved three times the accuracy of the marker it was derived from.

Pheromones are chemical substances released into the environment by an individual animal, which elicit stereotyped behaviours widely found across the animal kingdom. Inspired by the effective use of pheromones in social insects, pheromonal communication has been adopted to swarm robotics domain using diverse approaches such as alcohol, RFID tags and light. COS phi is one of the light-based artificial pheromone systems which can emulate realistic pheromones and environment properties through the system. This article provides a significant improvement to the state-of-the-art by proposing a novel artificial pheromone system that simulates pheromones with environmental effects by adopting a model of spatio-temporal development of pheromone derived from a flow of fluid in nature. Using the proposed system, we investigated the collective behaviour of a robot swarm in a bio-inspired aggregation scenario, where robots aggregated on a circular pheromone cue with different environmental factors, that is, diffusion and pheromone shift. The results demonstrated the feasibility of the proposed pheromone system for use in swarm robotic applications.

Chronorobotics is the investigation of scientific methods allowing robots to adapt to and learn from the perpetual changes occurring in natural and human-populated environments. We present methods that can introduce the notion of dynamics into spatial environment models, resulting in representations which provide service robots with the ability to predict future states of changing environments. Several long-term experiments indicate that the aforementioned methods gradually improve the efficiency of robots' autonomous operations over time. More importantly, the experiments indicate that chronorobotic concepts improve robots' ability to seamlessly merge into human-populated environments, which is important for their integration and acceptance in human societies

Models of human behaviour, such as pedestrian flows, are beneficial for safe and efficient operation of mobile robots. We present a new methodology for benchmarking of pedestrian flow models based on the afforded safety of robot navigation in human-populated environments. While previous evaluations of pedestrian flow models focused on their predictive capabilities, we assess their ability to support safe path planning and scheduling. Using real-world datasets gathered continuously over several weeks, we benchmark state-of-the-art pedestrian flow models, including both time-averaged and time-sensitive models. In the evaluation, we use the learned models to plan robot trajectories and then observe the number of times when the robot gets too close to humans, using a predefined social distance threshold. The experiments show that while traditional evaluation criteria based on model fidelity differ only marginally, the introduced criteria vary significantly depending on the model used, providing a natural interpretation of the expected safety of the system. For the time-averaged flow models, the number of encounters increases linearly with the percentage operating time of the robot, as might be reasonably expected. By contrast, for the time-sensitive models, the number of encounters grows sublinearly with the percentage operating time, by planning to avoid congested areas and times.

This paper concerns adaptive image processing for visual teach-and-repeat navigation systems of autonomous vehicles operating outdoors. The robustness and the accuracy of these systems rely on their ability to extract relevant information from the on-board camera images, which is then used for the autonomous navigation and the map building. In this paper, we present methods that allow an image-based navigation system to adapt to a varying appearance of outdoor environments caused by dynamic illumination conditions and naturally occurring environment changes. In the performed experiments, we demonstrate that the adaptive and the learning methods for camera parameter control, image feature extraction and environment map refinement allow autonomous vehicles to operate in real, changing world for extended periods of time.

We present a human-centric spatiotemporal model for service robots operating in densely populated environments for long time periods. The method integrates observations of pedestrians performed by a mobile robot at different locations and times into a memory efficient model, that represents the spatial layout of natural pedestrian flows and how they change over time. To represent temporal variations of the observed flows, our method does not model the time in a linear fashion, but by several dimensions wrapped into themselves. This representation of time can capture long-term (i.e. days to weeks) periodic patterns of peoples' routines and habits. Knowledge of these patterns allows making long-term predictions of future human presence and walking directions, which can support mobile robot navigation in human-populated environments. Using datasets gathered for several weeks, we compare the model to state-of-the-art methods for pedestrian flow modelling.