Predictor-Based Time Delay Control of A Hex-Jet Unmanned Aerial Vehicle

Turbojet-powered VTOL UAVs have garnered increased attention in heavy-load transport and emergency services, due to their superior power density and thrust-to-weight ratio compared to existing electronic propulsion systems. The main challenge with jet-powered UAVs lies in the complexity of thrust vectoring mechanical systems, which aim to mitigate the slow dynamics of the turbojet. In this letter, we introduce a novel turbojet-powered UAV platform named Hex-Jet. Our concept integrates thrust vectoring and differential thrust for comprehensive attitude control. This approach notably simplifies the thrust vectoring mechanism. We utilize a predictor-based time delay control method based on the frequency domain model in our Hex-Jet controller design to mitigate the delay in roll attitude control caused by turbojet dynamics. Our comparative studies provide valuable insights for the UAV community, and flight tests on the scaled prototype demonstrate the successful implementation and verification of the proposed predictor-based time delay control technique.

DeMoBot: Deformable Mobile Manipulation with Vision-based Sub-goal Retrieval

Imitation learning (IL) algorithms typically distill experience into parametric behavior policies to mimic expert demonstrations. Despite their effectiveness, previous methods often struggle with data efficiency and accurately aligning the current state with expert demonstrations, especially in deformable mobile manipulation tasks characterized by partial observations and dynamic object deformations. In this paper, we introduce \textbf{DeMoBot}, a novel IL approach that directly retrieves observations from demonstrations to guide robots in \textbf{De}formable \textbf{Mo}bile manipulation tasks. DeMoBot utilizes vision foundation models to identify relevant expert data based on visual similarity and matches the current trajectory with demonstrated trajectories using trajectory similarity and forward reachability constraints to select suitable sub-goals. Once a goal is determined, a motion generation policy will guide the robot to the next state until the task is completed. We evaluated DeMoBot using a Spot robot in several simulated and real-world settings, demonstrating its effectiveness and generalizability. With only 20 demonstrations, DeMoBot significantly outperforms the baselines, reaching a 50\% success rate in curtain opening and 85\% in gap covering in simulation.