Multi-robot mapping and environment modeling have several advantages that make it an attractive alternative to the mapping with a single robot: faster exploration, higher fault tolerance, richer data due to different sensors being used by different systems. However, the environment modeling with several robotic systems operating in the same area causes problems of higher-order—acquired knowledge fusion and synchronization over time, revealing the same environment properties using different sensors with different technical specifications. While the existing robot map and environment model merging techniques allow merging certain homogeneous maps, the possibility to use sensors of different physical nature and different mapping algorithms is limited. The resulting maps from robots with different specifications are heterogeneous, and even though some research on how to merge fundamentally different maps exists, it is limited to specific applications. This research reviews the state of the art in homogeneous and heterogeneous map merging and illustrates the main research challenges in the area. Six factors are identified that influence the outcome of map merging: (1) robotic platform hardware configurations, (2) map representation types, (3) mapping algorithms, (4) shared information between robots, (5) relative positioning information, (6) resulting global maps.

Predicting the growth of broiler chickens is an essential task in the poultry industry. The data used in the study include both the production environmental indicators (temperature, gas concentration, humidity, and others) and the growth rates of poultry (weight, amount of feed consumed, fall) by analyzing their correlations throughout several production cycles. The proposed approach includes several stages, starting with data pre-processing, broiler weight data augmentation, comparison with a reference model, definition, and detection of uncomfortable and dangerous environmental conditions. For the model-building part, the Long short-term memory (LSTM) artificial neural network is applied. The validation of the forecasting model is done by comparing the forecasted weight provided by the model with the actual weight measurements during the randomly selected bird life cycle and varied environmental conditions. The acquired results showed that the provided forecast accuracy is sufficient for production management.

An important prerequisite for creation of autonomous robot is the ability to create the map of the environment. Robots need to be able to construct a map of the environment and to use it for navigation. In this paper a multirobot mapping approach is proposed, where artificial landmarks (glyphs) are used for robot localisation and only simple sensors - bumper sensors - are available for obstacle detection. The proposed mapping system creates the map of the environment and allows changing the map rapidly in case of inaccurate robot position measurements or sensor errors. The system has a central server and robots use wireless network to communicate with the server. The mapping implementation takes into account the fact that the network has limited bandwidth and only limited amount of data can be sent.

The presented work proposes a practical approach to bird weight data processing and augmentation to enable production outcome forecast model training, which contributes to higher productivity. We suggest using the parametrized model, where parameter values are found through genetic optimization and thus are closely corresponding to broiler body weight factual measurements. The proposed approach is implemented as a stand-alone software system, exposing the models through containerized web services enabling different use scenarios.