Accurate and robust environmental perception is crucial for robot autonomous navigation. While current methods typically adopt optical sensors, such as cameras and LiDARs, as primary sensing modalities, their susceptibility to visual occlusion—such as dirt adhering to the lens or physical blockage of the sensor—often leads to degraded performance or complete system failure. In this paper, we focus on agricultural scenarios where robots are exposed to the risk of onboard sensor contamination. Leveraging radar’s strong penetration capability, we introduce a radar-based 3D environmental perception framework as a viable alternative. It comprises three core modules designed for dense and accurate semantic perception: first, parallel frame accumulation to enhance signal-to-noise ratio of raw radar data; second, a diffusion model-based hierarchical learning framework that first filters radar sidelobe artifacts then generates fine-grained 3D semantic point clouds; and third, a specifically designed sparse 3D network optimized for processing large-scale raw radar data. We conducted extensive benchmark comparisons and experimental evaluations on a self-built dataset collected in real-world agricultural field scenes. Results demonstrate that our method achieves superior structural and semantic prediction performance compared to existing methods, while simultaneously reducing computational and memory costs by 44.3% and 27.5%, respectively. Furthermore, our approach achieves complete reconstruction and accurate classification of thin structures such as poles and wires—which existing methods struggle to perceive—highlighting its potential for dense and accurate 3D radar perception.

Terrestrial and aerial bimodal vehicles have gained significant interest due to their energy efficiency and versatile maneuverability across different domains. However, most existing passive-wheeled bimodal vehicles rely on attitude regulation to generate forward thrust, which inevitably results in energy waste on producing lifting force. In this work, we propose a novel passive-wheeled bimodal vehicle called TrofyBot that can rapidly change the thrust direction with a single servo motor and a transformable parallelogram linkage mechanism (TPLM). Cooperating with a bidirectional force generation module (BFGM) for motors to produce bidirectional thrust, the robot achieves flexible mobility as a differential driven rover on the ground. This design achieves 95.37% energy saving efficiency in terrestrial locomotion, allowing the robot continuously move on the ground for more than two hours in current setup. Furthermore, the design obviates the need for attitude regulation and therefore provides a stable sensor field of view (FoV). We model the bimodal dynamics for the system, analyze its differential flatness property, and design a controller based on hybrid model predictive control for trajectory tracking. A prototype is built and extensive experiments are conducted to verify the design and the proposed controller, which achieves high energy efficiency and seamless transition between modes.

Terrestrial and aerial bimodal vehicles have gained widespread attention due to their cross-domain maneuverability. Nevertheless, their bimodal dynamics significantly increase the complexity of motion planning and control, thus hindering robust and efficient autonomous navigation in unknown environments. To resolve this issue, we develop a model-based planning and control framework for terrestrial aerial bi-modal vehicles. This work begins by deriving a unified dynamic model and the corresponding differential flatness. Leveraging differential flatness, an optimization-based trajectory planner is proposed, which takes into account both solution quality and computational efficiency. Moreover, we design a tracking controller using nonlinear model predictive control based on the proposed unified dynamic model to achieve accurate trajectory tracking and smooth mode transition. We validate our framework through extensive benchmark comparisons and experiments, demonstrating its effectiveness in terms of planning quality and control performance.

The Roller-Quadrotor is a novel quadrotor that combines the maneuverability of aerial drones with the endurance of ground vehicles. This work focuses on the design, modeling, and experimental validation of the Roller-Quadrotor. Flight capabilities are achieved through a quadrotor config-uration, with four thrust-providing actuators. Additionally, rolling motion is facilitated by a unicycle-driven and rotor-assisted turning structure. By utilizing terrestrial locomotion, the vehicle can overcome rolling and turning resistance, thereby conserving energy compared to its flight mode. This innovative approach not only tackles the inherent challenges of traditional rotorcraft but also enables the vehicle to roll through narrow gaps and overcome obstacles by taking advantage of its aerial mobility. We develop comprehensive models and controllers for the Roller-Quadrotor and validate their performance through experiments. The results demonstrate its seamless transition between aerial and terrestrial locomotion, as well as its ability to safely roll through gaps half the size of its diameter. Moreover, the terrestrial range of the vehicle is approximately 2.8 times greater, while the operating time is about 41.2 times longer compared to its aerial capabilities. These findings underscore the feasibility and effectiveness of the proposed structure and control mechanisms for efficient rolling through challenging terrains while conserving energy.

This paper proposes a systematic solution that uses an unmanned aerial vehicle (UAV) to aggressively and safely track an agile target. It properly handles the challenging situations where the intent of the target and the dense environments are unknown. Our work is divided into two parts: target motion prediction and tracking trajectory planning. The target motion prediction method utilizes target observations to reliably predict its future motion. The tracking trajectory planner follows the hierarchical workflow. A target informed kinody-namic searching method is adopted as the front-end, which heuristically searches for a safe tracking trajectory. The back- end optimizer then refines it into a spatial-temporal optimal trajectory. The proposed solution is integrated into an onboard quadrotor system. We fully test the system in challenging real-world tracking missions. Moreover, benchmark comparisons validate that the proposed method surpasses the cutting-edge methods on time efficiency and tracking effectiveness.

With the development of intelligent sensing and human-robot interaction technology, homecare robots play an increasingly important role in the field of homecare services. At the same time, the interaction between the operator and the homecare robot is particularly valued. This paper proposed a homecare robot interaction system based on a wearable inertial motion capture device. In this system, the wearable motion capture device is used to capture the operator's motion signals. After processing the motion signals and recognizing the corresponding motion poses, the system controls the homecare robot to imitate the intention of the operator. In this paper, we focused on controlling the movement of the homecare robot through the operator's lower limb motion data and designed a novel gait recognition algorithm. The proposed system was evaluated experimentally, proving that the system has strong performance and practicality.