LiDARs are widely used in autonomous robots due to their ability to provide accurate environment structural information. However, the large size of point clouds poses challenges in terms of data storage and transmission. In this paper, we propose a novel point cloud compression and transmission framework for resource-constrained robotic applications, called RCPCC. We iteratively fit the surface of point clouds with a similar range value and eliminate redundancy through their spatial relationships. Then, we use Shape-adaptive DCT (SA-DCT) to transform the unfit points and reduce the data volume by quantizing the transformed coefficients. We design an adaptive bitrate control strategy based on QoE as the optimization goal to control the quality of the transmitted point cloud. Experiments show that our framework achieves compression rates of 40×to 80× while maintaining high accuracy for downstream applications. our method significantly outperforms other baselines in terms of accuracy when the compression rate exceeds 70×. Furthermore, in situations of reduced communication bandwidth, our adaptive bitrate control strategy demonstrates significant QoE improvements. The code will be available at https://github.com/HITSZ-NRSL/RCPCC.git.

High-precision tiny object alignment remains a common and critical challenge for humanoid robots in real world. To address this problem, this paper proposes a vision-based framework for precisely estimating and controlling the relative position between a handheld tool and a target object for humanoid robots, e.g., a screwdriver tip and a screw head slot. By fusing images from the head and torso cameras on a robot with its head joint angles, the proposed Transformer-based visual servoing method can correct the handheld tool’s positional errors effectively, especially at a close distance. Experiments on M4-M8 screws demonstrate an average convergence error of 0.8-1.3 mm and a success rate of 93%-100%. Through comparative analysis, the results validate that this capability of high-precision tiny object alignment is enabled by the Distance Estimation Transformer architecture and the Multi-Perception-Head mechanism proposed in this paper.

Visual place recognition is a critical component of robust simultaneous localization and mapping systems. Conventional approaches primarily rely on RGB imagery, but their performance degrades significantly in extreme environments, such as those with poor illumination and airborne particulate interference (e.g., smoke or fog), which significantly degrade the performance of RGB-based methods. Furthermore, existing techniques often struggle with cross-scenario generalization. To overcome these limitations, we propose an RGB-thermal multimodal fusion framework for place recognition, specifically designed to enhance robustness in extreme environmental conditions. Our framework incorporates a dynamic RGB-thermal fusion module, coupled with dual fine-tuned vision foundation models as the feature extraction backbone. Experimental results on public datasets and our self-collected dataset demonstrate that our method significantly outperforms state-of-the-art RGB-based approaches, achieving generalizable and robust retrieval capabilities across day and night scenarios. The code is available at https://github.com/HITSZ-NRSL/RGB-Thermal-VPR.

Utilizing robots for autonomous target search in complex and unknown environments can greatly improve the efficiency of search and rescue missions. However, existing methods have shown inadequate performance due to hardware platform limitations, inefficient viewpoint selection strategies, and conservative motion planning. In this work, we propose HEATS, which enhances the search capability of mobile manipulators in complex and unknown environments. We design a target viewpoint planner tailored to the strengths of mobile manipulators, ensuring efficient and comprehensive viewpoint planning. Supported by this, a whole-body motion planner integrates global path search with local IPC optimization, enabling the mobile manipulator to safely and agilely visit target viewpoints, significantly improving search performance. We present extensive simulated and real-world tests, in which our method demonstrates reduced search time, higher target search completeness, and lower movement cost compared to classic and state-of-the-art approaches. Our method will be open-sourced for community benefit3.

Target tracking is a fundamental task in the domain of robotics. The effectiveness of target tracking hinges upon various factors, such as tracking distance, occlusions, collision avoidance, etc. However, few existing works can simultaneously tackle these considerations of tracking single and multiple targets in complex environments. In this study, the interaction mechanism of target tracking between the robot, the environment and the targets is analyzed, and a general measure named detectability is introduced to correlate the tracking performance for guiding robotic motion planning. Based on the detectability measure, the robotic motion planning framework based on Model Predictive Control (MPC) is proposed to achieve continuous and robust tracking of single, two and three targets in complex environments. Simulations and experiments are performed and verify the performances of our method better than the state-of-the-art methods.

Torque ripple reduction in quasi-direct drive (QDD) motors is crucial in their robotic applications for dynamic locomotion and dexterous manipulation. In this paper, we present a novel approach for reducing torque ripples of QDD motors, which integrates an angle-based repetitive learning observer (ARLO) and a model predictive control-based field-oriented controller (MPC-FOC). The proposed method successfully improves the torque loop control bandwidth and surpasses conventional proportional-integral (PI) controllers owing to the integrated physical constraints inside MPC. Additionally, the ARLO portion is able to mitigate ripple caused by the inherent cogging torque in brushless motors and also the periodic friction torque from the planetary gearboxes in QDD systems. The effectiveness of the proposed method is demonstrated through both simulation of a single QDD motor and experiments on a two-degree-of-freedom robotic leg, where the performance improvement can be 72.7% in speed tracking and 58.5% in trajectory tracking. The proposed method shows great potential in facilitating smooth motion and precise force control in future robotic applications.