Robotics

This article presents a methodology for real-time forecasting of a fire-extinguishing agent jet trajectory from a robotic fire monitor under wind influence, which can significantly displace the impact area position and complicate targeting. The proposed methodology is designed for controlling firefighting robots in conditions where visual monitoring of the impact area is impeded by factors such as: obscuration of the fire-extinguishing agent flow by smoke, low visibility of its fragmented particles against the background environment, and long-range jet discharge. Trajectory forecasting is implemented using a neural network model. The training and verification of this model are performed with datasets constructed from the results of numerical simulations of fire-extinguishing agent motion under wind influence, based on Computational Fluid Dynamics (CFD) methods. Experimentally obtained data are used for the validation of the trained neural network model and the selected CFD models. The paper describes the methodology for conducting full-scale tests of fire monitors; a photogrammetric algorithm for generating validation datasets from the test results; an algorithm for calculating target characteristics, which describe the jet trajectory and are consistent with experimental data, used for forming training and verification datasets based on simulation; and a procedure for selecting Computational Fluid Dynamics models and their parameters to ensure the required accuracy. The article also presents the results of an experimental evaluation of the developed methodology’s effectiveness for real-time prediction of the water jet trajectory from a fire monitor under various control and disturbance parameters.

Collaborative perception can mitigate occlusion and range limitations in autonomous driving, but deployment remains constrained by strict bandwidth budgets and heterogeneous agent stacks. We propose a communication-efficient and backbone-agnostic framework in which each agent’s encoder is treated as a black box, and a lightweight interpreter maps its intermediate features into a canonical space. To reduce transmission cost, we integrate codebook-based compression that sends only compact discrete indices, while a prompt-guided decoder reconstructs semantically aligned features on the ego vehicle for downstream fusion. Training follows a two-phase strategy: Phase 1 jointly optimizes interpreters, prompts, and fusion components for a fixed set of agents; Phase 2 enables plug-and-play onboarding of new agents by tuning only their specific prompts. Experiments on OPV2V and OPV2VH+ show that our method consistently outperformed early-, intermediate-, and late-fusion baselines under equal or lower communication budgets. With a codebook of size 128, the proposed pipeline preserved over 95% of the uncompressed detection accuracy while reducing communication cost by more than two orders of magnitude. The model also maintained strong performance under bandwidth throttling, missing-agent scenarios, and heterogeneous sensor combinations. Compared to recent state-of-the-art methods such as PolyInter, MPDA, and PnPDA, our framework achieved higher AP while using significantly smaller message sizes. Overall, the combination of prompt-guided decoding and discrete Codebook compression provides a scalable, bandwidth-aware, and heterogeneity-resilient foundation for next-generation collaborative perception in connected autonomous vehicles.

Energy efficiency represents a fundamental aspect of sustainable industrial automation, where minimizing energy expenditure supports both environmental and economic goals. This work presents the modeling and comparative analysis of the energy consumption of three planar robotic manipulators performing pick-and-place operations: a serial RRR configuration (RRR-D2) and two alternative PRR architectures (PRR90 and PRR45) featuring linear prismatic guides. For each manipulator, kinematic and dynamic models are derived, and actuator electro-mechanical effects are incorporated to obtain realistic energy evaluations. The analysis is carried out over four representative trajectories and two design variables, enabling a consistent comparison in terms of both total and recoverable energy through regenerative braking. Results show that geometric and actuation parameters significantly influence energy performance and that specific PRR configurations can achieve comparable motion capabilities to the RRR structure with reduced energy demand. The proposed framework supports energy-aware robot selection and design, contributing to the development of efficient and sustainable planar manipulators for repetitive industrial operations.