Knowledge representation in autonomous robots with social roles has steadily gained importance through their supportive task assistance in domestic, hospital, and industrial activities. For active assistance, these robots must process semantic knowledge to perform the task more efficiently. In this context, ontology-based knowledge representation and reasoning (KR & R) techniques appear as a powerful tool and provide sophisticated domain knowledge for processing complex robotic tasks in a real-world environment. In this article, we surveyed ontology-based semantic representation unified into the current state of robotic knowledge base systems, with our aim being three-fold: (i) to present the recent developments in ontology-based knowledge representation systems that have led to the effective solutions of real-world robotic applications; (ii) to review the selected knowledge-based systems in seven dimensions: application, idea, development tools, architecture, ontology scope, reasoning scope, and limitations; (iii) to pin-down lessons learned from the review of existing knowledge-based systems for designing better solutions and delineating research limitations that might be addressed in future studies. This survey article concludes with a discussion of future research challenges that can serve as a guide to those who are interested in working on the ontology-based semantic knowledge representation systems for autonomous robots.

Modern robotic systems are evolving toward conducting missions based on semantic knowledge. Such systems require environmental modeling as essential for successful mission execution. However, there is an inefficiency in that manual modeling is required whenever a new environment is given, and adaptive modeling that can adapt to the environment is needed. In this paper, we propose an integrated framework that enables autonomous environmental modeling for service robots by fusing domain knowledge with open-vocabulary-based Vision-Language Models (VLMs). When a robot is deployed in a new environment, it builds occupancy maps through autonomous exploration and extracts semantic information about objects and places. Furthermore, we introduce human–robot collaborative modeling beyond robot-only environmental modeling. The collected semantic information is stored in a structured database and utilized on demand. To verify the applicability of the proposed framework to service robots, experiments are conducted in a simulated home environment and a real-world indoor corridor. Through the experiments, the proposed framework achieved over 80% accuracy in semantic information extraction in both environments. Semantic information about various types of objects and places was extracted and stored in the database, demonstrating the effectiveness of DK-SMF for service robots.

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.

Heterogeneous robot fleets transport items that differ in shape and weight, and robots differ in what they can handle and where they can pass. Conventional task allocation methods often compute allocation costs and routes on a single load-invariant spatial model, which may overlook changes in place traversability after loading. Payload capacity typically enters allocation only as a feasibility or ranking condition, so light tasks may be allocated to high-capacity robots also needed for heavier ones. Taking the feasibility information from semantic reasoning as input, this study combines a route cost on load-state-dependent traversability graphs with a normalized payload-margin cost in a makespan-oriented sequential allocation procedure. In ablation experiments, load-invariant route planning produced non-traversable loaded routes for approximately one quarter of the tasks, whereas load-state-dependent planning and validation produced none, as expected by construction. Against a violation-free variant with load-invariant costs and no payload term, the proposed configuration shortened the makespan by 6.7–20.1%, mainly through the payload-margin term; its route cost made the predicted completion times consistent with the planned routes. The reduction persisted on a larger layout and with congested loading and unloading places. In ROS 2 and Gazebo, eight robots followed the planned place sequences in both completed runs.

Conventional local planners treat obstacles as uniform geometric entities, regardless of whether an object is deformable, fragile, or a person. This paper presents SOCA (Semantic Object-Class-Aware), a semantic costmap layer for an unmodified Dynamic Window Approach (DWA) planner. Given externally supplied object identity or Semantic Safety Class (SSC) and planar geometry, SOCA converts five navigation-safety attributes into a class-dependent exclusion core and graded cost band. SOCA was compared with DWA and two class-blind baselines in 750 Gazebo runs and 300 physical trials, including 120 logged trials. With one obstacle, SOCA maintained clearances of 0.35, 0.40, and 0.91 m from a plastic bag, glassware, and a mannequin, respectively, compared with 0.23–0.28 m for DWA. With three objects, SOCA maintained greater person-class clearance than DWA in every label permutation (p = 0.002), with 0.3–0.8 s longer traversal times. On the robot, SOCA maintained greater clearance than DWA for every object class in all 30 paired blocks and was faster than the uniform baseline. Forced labels shifted clearance toward the assigned class, and the class ordering was preserved for the tested speed coefficients and enlarged preferred clearances. These results apply to stationary objects with supplied labels; moving entities remain outside the present scope.

Mobile platforms for installing prefabricated pipe and duct modules inside buildings must transport long payloads and lift them to elevated work positions. Changes in payload arrangement and height alter stability margins and load-transfer responses. This paper presents a payload-informed tip-over prevention framework for a previously developed high-lift swerve-drive construction platform. Four platen load cells estimate payload mass and planar center of gravity (CoG), while vertical CoG is supplied from geometry. Support-edge moments and an explicit error budget define conditional limits on directional translational acceleration and deceleration for pre-operation setting selection and compatibility checks. Load-transfer residuals computed using matched causal processing provide online observations during travel. The reported experiments comprise 46 records for normal and eccentric arrangements formed by repositioning the pipes at two nominal payload levels, approximately 1.05 t and 3.13 t, with platen heights up to approximately 5.04 m. Three stationary acquisitions per arrangement give mass standard deviations of 0.23–0.64 kg, characterizing within-layout repeatability. Nineteen LOW-speed driving records support residual characterization and 92 common-window single-stop response comparisons; six stationary HIGH-setting events demonstrate alarm and inhibit requests. The resulting evidence connects payload characterization, setting supervision and response observation for construction transport and lifting. The directional-limit formulation remains conditional on an identified dynamic-response model and an adequate error budget.