Optimal path planning requires finding a series of feasible states from the starting point to the goal to optimize objectives. Popular path planning algorithms, such as Effort Informed Trees (EIT*), employ effort heuristics to guide the search. Effective heuristics are accurate and computationally efficient, but achieving both can be challenging due to their conflicting nature. This paper proposes Direction Informed Trees (DIT*), a sampling-based planner that focuses on optimizing the search direction for each edge, resulting in goal bias during exploration. We define edges as generalized vectors and integrate similarity indexes to establish a directional filter that selects the nearest neighbors and estimates direction costs. The estimated direction cost heuristics are utilized in edge evaluation. This strategy allows the exploration to share directional information efficiently. DIT* convergence faster than existing single-query, sampling-based planners on tested problems in$\mathbb{R}^{4}$to$\mathbb{R}^{16}$and has been demonstrated in real-world environments with various planning tasks. A video showcasing our experimental results is available at: https://youtu.be/2SX6QT2NOek.

The whisker-inspired tactile sensor is advantageous for enhancing robotic perception in proximate range and darkness via non-intrusive contacts. However, localizing contact along the whisker shaft is challenging due to the non-injective mapping between tangential contacts and the resulting bending moments at the whisker base. Previous studies suggest that incorporating axial force measurements can resolve this ambiguity. In this work, we develop a magnetically transduced whisker sensor that integrates axial force sensing as an additional mechanical signal. The sensor features a tapered whisker with a custom slope and a 3-DoF suspension mechanism, enabling axial displacement at the base, which is proportional to the applied axial force. We construct a Penalized Gaussian Process model trained on synthetic data to estimate the whisker’s motion and refine it with real-data constraints. The design is compact, low-cost, and validated through simulations and real-world experiments to differentiate tangential contacts. Furthermore, we propose an optimization-based approach for estimating instantaneous contact locations. Experimental results demonstrate that the proposed method can effectively track contacts in millimeter-level accuracy with a mean error of 7.17 mm, achieving a higher accuracy with only 4.02 mm in large-deflection and close-to-base regions.

Deep learning techniques have led to remarkable breakthroughs in the field of object detection and have spawned a lot of scene-understanding tasks in recent years. Scene graph has been the focus of research because of its powerful semantic representation and applications to scene understanding. Scene Graph Generation (SGG) refers to the task of automatically mapping an image or a video into a semantic structural scene graph, which requires the correct labeling of detected objects and their relationships. In this paper, a comprehensive survey of recent achievements is provided. This survey attempts to connect and systematize the existing visual relationship detection methods, to summarize, and interpret the mechanisms and the strategies of SGG in a comprehensive way. Deep discussions about current existing problems and future research directions are given at last. This survey will help readers to develop a better understanding of the current researches.