Pedestrian tracking is a challenging task in the area of visual object tracking research and it is a vital component of various vision-based applications such as surveillance systems, human-following robots, and autonomous vehicles. In this paper, we proposed a single pedestrian tracking (SPT) framework for identifying each instance of a person across all video frames through a tracking-by-detection paradigm that combines deep learning and metric learning-based approaches. The SPT framework comprises three main modules: detection, re-identification, and tracking. Our contribution is a significant improvement in the results by designing two compact metric learning-based models using Siamese architecture in the pedestrian re-identification module and combining one of the most robust re-identification models for data associated with the pedestrian detector in the tracking module. We carried out several analyses to evaluate the performance of our SPT framework for single pedestrian tracking in the videos. The results of the re-identification module validate that our two proposed re-identification models surpass existing state-of-the-art models with increased accuracies of 79.2% and 83.9% on the large dataset and 92% and 96% on the small dataset. Moreover, the proposed SPT tracker, along with six state-of-the-art (SOTA) tracking models, has been tested on various indoor and outdoor video sequences. A qualitative analysis considering six major environmental factors verifies the effectiveness of our SPT tracker under illumination changes, appearance variations due to pose changes, changes in target position, and partial occlusions. In addition, quantitative analysis based on experimental results also demonstrates that our proposed SPT tracker outperforms the GOTURN, CSRT, KCF, and SiamFC trackers with a success rate of 79.7% while beating the DiamSiamRPN, SiamFC, CSRT, GOTURN, and SiamMask trackers with an average of 18 tracking frames per second.

Intelligent robotic systems frequently operate under stringent energy limitations, especially in complex and dynamic environments. To enhance both adaptability and reliability, this study introduces a semantic planning framework that integrates ontology-driven reasoning with energy awareness. The framework estimates energy consumption based on the platform-specific behavior of sensing, actuation, and computational modules while continuously updating place-level semantic representations using real-time execution data. These representations encode not only spatial and contextual semantics but also energy characteristics acquired from prior operational history. By embedding historical energy usage profiles into hierarchical semantic maps, this framework enables more efficient route planning and context-aware task assignment. A shared semantic layer facilitates coordinated planning for both single-robot and multi-robot systems, with the decisions informed by energy-centric knowledge. This approach remains hardware-independent and can be applied across diverse platforms, such as indoor service robots and ground-based autonomous vehicles. Experimental validation using a differential-drive mobile platform in a structured indoor setting demonstrates improvements in energy efficiency, the robustness of planning, and the quality of the task distribution. This framework effectively connects high-level symbolic reasoning with low-level energy behavior, providing a unified mechanism for energy-informed semantic decision-making.

We propose a novel geometry-constrained learning-based method for camera-in-hand visual servoing systems that eliminates the need for camera intrinsic parameters, depth information, and the robot’s kinematic model. Our method uses a cerebellar model articulation controller (CMAC) to execute online Jacobian estimation within the control framework. Specifically, we introduce a fixed-dimension, uniform-magnitude error function based on the projective homography matrix. The fixed-dimension error function ensures a constant Jacobian size regardless of the number of feature points, thereby reducing computational complexity. By not relying on individual feature points, the approach maintains robustness even when some features are occluded. The uniform magnitude of the error vector elements simplifies neural network input normalization, thereby enhancing online training efficiency. Furthermore, we incorporate geometric constraints between feature points (such as collinearity preservation) into the network update process, ensuring that model predictions conform to the fundamental principles of projective geometry and eliminating physically impossible control outputs. Experimental and simulation results demonstrate that our approach achieves superior robustness and faster learning rates compared to other model-free image-based visual servoing methods.

This paper introduces a hierarchical object-centric descriptor framework called TOSD (Triplet Object-Centric Semantic Descriptor). The goal of this method is to overcome the limitations of existing pixel-based and global feature embedding approaches. To this end, the framework adopts a hierarchical representation that is explicitly designed for multi-level reasoning. TOSD combines shape, color, and topological information without depending on predefined class labels. The shape descriptor captures the geometric configuration of each object. The color descriptor focuses on internal appearance by extracting normalized color features. The topology descriptor models the spatial and semantic relationships between objects in a scene. These components are integrated at both object and scene levels to produce compact and consistent embeddings. The resulting representation covers three levels of abstraction: low-level pixel details, mid-level object features, and high-level semantic structure. This hierarchical organization makes it possible to represent both local cues and global context in a unified form. We evaluate the proposed method on multiple vision tasks. The results show that TOSD performs competitively compared to baseline methods, while maintaining robustness in challenging cases such as occlusion and viewpoint changes. The framework is applicable to visual odometry, SLAM, object tracking, global localization, scene clustering, and image retrieval. In addition, this work extends our previous research on the Semantic Modeling Framework, which represents environments using layered structures of places, objects, and their ontological relations.

In heterogeneous multi-robot systems, allocating tasks efficiently requires determining whether each robot can actually carry out a given task. In existing multi-robot task allocation research, however, such task feasibility has typically been handled inside a particular optimizer or symbolic planner, while spatial traversability has been assessed against static criteria that cannot capture the changes induced by a robot’s loaded state. This paper proposes an ontology-based semantic feasibility reasoning method for heterogeneous multi-robot task allocation. The proposed method defines semantic models for robots, tasks, and places and applies hybrid reasoning, combining declarative reasoning with procedural evaluation, to determine multi-axis capability conditions and loaded-state place reachability. The reasoning result is formalized as an allocator-independent ReasonerOutput that serves as a common input for diverse allocation algorithms. In experiments spanning four scenarios over three fleet configurations and four allocators, together with an ablation study on 200 randomized instances at each of three problem scales, the proposed ReasonerOutput consistently functioned as a shared semantic feasibility constraint. The experiments further showed that both the fleet composition and the loaded state of the target item affect assignment feasibility. These results indicate that, for the static one-shot assignment setting evaluated here, the proposed method makes the task feasibility of heterogeneous robots explicit through semantic reasoning and allows allocators of differing algorithmic character to draw on this feasibility in common.

To achieve Level 4 and above autonomous driving, vehicle localization must remain accurate and reliable under diverse real-world conditions, including complex traffic scenarios, environmental changes, and partial sensor failures. Conventional localization approaches primarily rely on geometric consistency among multi-sensor observations, which can produce physically or contextually implausible pose estimates when sensor reliability degrades or observations become ambiguous. This paper proposes a semantic localization framework that integrates ontology-based semantic reasoning directly into the localization process. The proposed approach reformulates localization as a context-aware constraint selection problem guided by semantic consistency among objects, places, and vehicle poses. By evaluating logical and contextual validity at the hypothesis generation stage, semantically invalid pose hypotheses are eliminated early, and only situation-appropriate semantic constraints are selectively applied during optimization. As a result, compared to the localization system without semantic rules, the proposed framework achieves an average reduction of approximately 35.6% in mean localization error and 47.0% in maximum localization error across both longitudinal and lateral directions. Specifically, the framework supports structured multi-sensor fusion by selectively using sensor information semantically relevant to the driving context. Through this semantics-driven hypothesis reduction, the system reduces computational complexity while enhancing localization robustness and accuracy, particularly under sensor degradation and dynamic environmental conditions. The design of the semantic reasoning structure is also adaptable to cooperative perception scenarios, such as V2V-based information sharing.