In the field of artificial intelligence and robotics, enabling robots to understand and execute complex tasks in unknown and open environments through natural language has become a frontier of current research. Traditional methods that rely on predefined action libraries face significant limitations when dealing with environmental diversity and task uncertainty. Accordingly, this study introduces a novel embodied intelligence framework named ‘Embodied Assistant’, which utilizes large language models (LLMs) for open-ended reasoning and adaptive task planning, can autonomously guide robots to flexibly complete challenging tasks in complex scenarios without relying on fixed action templates. By establishing effective multimodal LLM interaction pathways, this framework grants robots the ability to perceive and understand the task environment, concurrently allowing them to accurately interpret natural language instructions, and to independently plan action strategies along with robust robot trajectories. Moreover, the introduced multi-level task feedback mechanism effectively enhances the robots’ ability to self-correct and replan when they encounter planning failures. In extensive real-life testing scenarios, the proposed method achieved an 83.3% task success rate across various combined mobility and grasping tasks, significantly outperforming the most advanced baseline methods. A detailed analysis of robot fault recovery further demonstrates the substantial potential of this method in practical applications. Prompts and videos are provided at the project homepage: embodied-assistant.github.io

This article focuses on achieving efficient and safe navigation for robots in dynamic and unpredictable environments. We propose a hierarchical path planning framework that integrates global path planning with local dynamic obstacle avoidance. This framework aims to quickly plan a collision-free, shortest, and safest path for the robot while adapting the navigation path according to uncertainties in the operating environment. Global path planning is conducted using the improved gray wolf optimization algorithm (IGWO), trajectory tracking is achieved through a pure pursuit control algorithm, and a dynamic switching mechanism based on deep reinforcement learning (DRL) significantly enhances the navigation performance of the robotic system. The effectiveness of this approach has been verified through simulations and experiments. Simulation results indicate that in global path planning, the IGWO algorithm achieves faster convergence compared to algorithms, such as GWO-MP and GWO-CS. The planned path lengths are reduced by approximately 4.01% and 2.27%, respectively, and the fitness values are decreased by 4.78% and 1.9%, demonstrating superior path planning performance. For local dynamic obstacle avoidance, both single-robot and multirobot systems successfully avoided obstacles in multiple experiments. Finally, physical experiments conducted in various complex scenarios show that both single-robot and multirobot systems can effectively execute global planning and respond to unexpected obstacles. These results further demonstrate the method’s wide applicability and robust performance across diverse complex environments.

With the rapid advancement of deep learning technology, the problem of object detection in the field of remote sensing has received increasing attention. However, existing methods still face challenges when dealing with remote sensing image detection tasks, such as insufficient accuracy in small object recognition, an imbalance between recognition precision and detection efficiency, and difficulties in handling multi-scale variations. Based on the above problems, this letter proposes a lightweight and efficient remote sensing image object detector MERS-Net. Firstly, we propose an enhanced feature extraction module SCFE based on sub-channel mixing to improve the feature extraction capability of the model under complex scale transformation. Secondly, we incorporated the WaveletPool module along with the GSConv and VoVGSCSP modules from Slimneck into the model to reduce computational parameters. Finally, we designed a lightweight detection head MRF-Detect based on the parameter sharing mechanism to improve the recognition capability of the model under limited hardware conditions. We verified the effectiveness of MERS-Net on the public datasets DOTA, AI-TOD, and DIOR datasets. Compared with current mainstream algorithms such as YOLOv11, the average accuracy (mAP) is improved by 2.6%, 2.5% and 2.7%, respectively. At the same time, the parameter quantity and GFLOPs are significantly reduced, showing its superior detection performance.

Multiple aircraft tracking (MAT) plays a critical role in military and civil aerial surveillance systems. Many studies have focused on tracking multiple pedestrians and automobiles, leaving a gap in related research on MAT because of the peculiar tracking properties of multiple aircraft, such as small/tiny object formation properties, identical shapes and appearances, severe camera jitter and trail cloud occlusion. In this paper, to fill this gap, building on top of a tracker named TransTrack, we present a robust MAT method to address multiple aircraft tracking, referred to as RoMATer. The improvements are threefold: a receptive field enlarging (RFE) module is first integrated into the backbone of a feature extractor to assist in feature extraction of full-scale aircraft, and a context-aware encoder layer (CaEL) is proposed to introduce global and local contextual embeddings to provide supplementary discriminative tracking information; moreover, a motion and appearance association (MA-A) module is proposed to overcome the tracking challenges of aircrafts possessing highly identical shapes and appearances. Extensive experiments on our established HIT-MATD (MAT Dataset) dataset (the first multiaircraft tracking dataset) verify the SOTA performance of RoMATer on multiaircraft tracking, with an increase of ~10% in terms of the MOTAL and an increase of ~5% with respect to the recall compared to that of TransTrack. Experiments on public RarePlanes dataset verify the effectiveness of the proposed modules in detecting multiple aircraft at full scales. Moreover, RoMATer can also run at a high frame rate (~11FPS, Nvidia-A100).