Robot Operating Research Articles

Robotics and artificial intelligence

The general objective of the research was to determine the advances related to the robotics and artificial intelligence. The specific objectives of the research are to determine the countries and organizations that successfully manufacture robots, identify the characteristics of the most representative robots and the software used in their design and operation. Methodology, in this research, 61 documents have been selected, carried out in the period 2018 - 2024; including: scientific articles, review articles and information from websites of recognized organizations. Results, the software used in the design and operation of the robots are: Robot operating systems (ros), industrial robot programming software, simulation software, robot vision software, autonomous navigation software, collaborative robot (cobot) software. Conclusions, artificial intelligence has been evolving and has managed to position itself as an important technology to improve various systems. The leading robotics producers are in Germany, Japan, and Switzerland. The organizations that successfully manufacture robots are ABB Group (Switzerland), Adept Technology Inc (United States) and Apex Automation and Robotics (Australia). And the software used in the design and operation of the robots are: Robot operating systems (ros), industrial robot programming software, simulation software, robot vision software, autonomous navigation software, collaborative robot (cobot) software.

Enhancing Robot Autonomy: Path Planning via Hand-Drawn Sketches and Computer Vision Integration

This investigation delves into an advanced method for enhancing robot autonomy by integrating hand-drawn sketches with computer vision systems to optimize route planning effectively. The procedure starts with the capture of image data, which is then processed through multiple stages until the sketches are turned into exact coordinates that guide a robot. Canny edge detection, a specific computer vision technique, is employed to detect the edges of the drawn lines, facilitating precise and dependable navigation for autonomous robot operations. Initially, the image is subjected to noise reduction and edge enhancement before converting the sketch lines into Cartesian coordinates. This step is succeeded by point filtering and ordering to ascertain accurate path adherence by the robot, with no coordinates overlooked. Scale adjustments are also made to match digital image coordinates with the real-world setting, thereby improving the robot’s interaction based on the received visual inputs. The program has been tested in Robotic Operating System 2 (ROS) using MoveIt2 and also in RoboDK software, where it was used to verify the results. It is important to note that path planning, which focuses on determining an optimal route from start to finish, is a subset of motion planning, which also considers the dynamics and kinematics of the robot's movement along that route.

REMOTE INTELLIGENT ROBOT MANIPULATOR CONTROL SUBSYSTEM

The issue of expanding the functionality of an intelligent software and hardware subsystem for capturing an object by a robot manipulator was considered. It is shown that the task of expanding the functionality of the subsystem for free-form object capture by a robot manipulator with technical vision based on the operations of building a depth map from the obtained stereo image is insufficiently developed and can be solved by integrating a remote robot cntrol subsystem connected to a local network or the Internet into the robotic complex. The subsystem was developed using a Raspberry Pi microcomputer, a Stereo Pi board, a board for connecting eight Adafruit PCA9865 servos, Raspberry PI cameras, and a camera module. The structure and electrical diagram of the subsystem, a description of the software, and the main code snippet of the web application program are presented. Based on the analysis of the research results, it is established that the proposed remote control subsystem provides an extension of the functionality of the intelligent software and hardware subsystem for capturing an object by a robot manipulator, since in addition to the functions of automatic object capture, due to remote adjustment of the accuracy and manufacturability of object capture, it provides the functions of high-precision positioning of objects relative to each other and especially the effort on the robot manipulator to perform precise operations. The reliability of the object capture is also increased due to the fact that the subsystem allows for the additional involvement of a team of competent users. The proposed system increases the degree of automation, provides the possibility of moderation, collective administration, and user feedback during robot operation.

Biotissue Curriculum Translates to Performance in the Operating Room for Gastrojejunostomy and Hepaticojejunostomy in Robotic Pancreaticoduodenectomy.

Robotic simulation training curricula aim to aid surgeons in attaining robotic operating room proficiency, but the crossover success remains to be examined. A retrospective cohort study grading robotic biotissue training models and intraoperative anastomotic videos. The curriculum included deliberate practice of inanimate drills of a hepaticojejunostomy (HJ) and gastrojejunostomy (GJ). Videos were blindly reviewed, and performance was evaluated by time, errors, and Objective Structured Assessment of Technical Skills (OSATS). Spearman's correlation coefficients (ρ) were calculated for prior experience, biotissue performance, and intraoperative performance. University of Pittsburgh Medical Center from 2014 to 2018. Thirty-one surgical oncology fellows participated in the 5-step proficiency-based robotic training curriculum for robotic pancreaticoduodenectomy. Fellows completed an average of 5.1 ± 3.7 HJ and 4.3 ± 3.3 GJ on biotissue. More practice on biotissue correlated with greater improvement on both times to complete an anastomosis (ρ = -0.51) and errors (ρ = -0.45). Average errors on biotissue GJ and longer time on the last attempt correlated with lower average intraoperative GJ OSATS (ρ = -0.64; ρ = -0.66). More errors on the last biotissue GJ correlated with longer average intraoperative GJ time (ρ = 0.58). Errors on the first and average biotissue HJ errors correlated with lower OSATS for the intraoperative HJ (ρ = -0.74; ρ = -0.80). Performance on biotissue correlated with intraoperative performance. Results suggest the importance deliberate practice to achieve surgical proficiency.

The safety and feasibility of robotic pancreaticoduodenectomy: A multicenter retrospective assessment of 425 patients in Japan.

The Japanese public health insurance system has recently covered robotic pancreaticoduodenectomy (RPD). This study aimed to review the results of RPD during the introductory period and elucidate its safety and feasibility in Japan. Consecutive data of 425 patients who underwent RPD were retrospectively collected from 10 high-volume centers in Japan between April 2020 and September 2022. The primary endpoints were the rates of conversion to open surgery and completion of minimally invasive surgery (MIS), defined as the use of a totally robotic operation or combined robotic and laparoscopic procedures. Secondary endpoints were operative time, blood loss, complication rate, and 30- and 90-day mortality rates. Study comprised 222 males and 203 females, with a median age of 70 (p10-p90; 50-81) years. The conversion to open surgery and completion rates of MIS were 3.8% (16 patients) and 91.1% (387 patients), respectively. The median operative time was 617 min (p10-p90; 456-834 min), and the median volume of blood loss was 160 g (p10-p90; 30-558 g). The complication rate (Clavien-Dindo classification grade ≥ IIIa) was 20.5%. The 30- and 90-day mortality rates were 0.2% and 0.5%, respectively. Our results indicate that RPD can be introduced successfully and is a promising approach for pancreaticoduodenectomy.

Single-Sheet Separation from Paper Stack Based on Friction Uncertainty Using High-Speed Robot Hand

The successful separation of a single sheet from a stack of paper is considered a paper-handling goal when using a robot hand. Under the condition of uncertain friction coefficients, a stochastic algorithm introducing randomness is formulated, which converges a paper stack to a state of single-sheet separation through the repetition of simple robot operations. This formulation is based on the proposed motion strategy for a robotic hand, which introduces a state of a partially separated paper bundle to temporarily allow the simultaneous separation of multiple sheets and a return operation to return the paper to the original paper bundle. The experimental results indicate that a single sheet can be completely separated from a vertically standing stack of business-card-sized papers by shifting the paper in a high-speed translational movement using two fingers of the robot hand that grasp the paper from both sides.

The Luna Analog Facility testbeds (ESA, EAC): contemporary characterization work of highland (lunar) and mare (EAC-1) lunar regolith simulants

The Luna Analog Facility, a joint ESA-DLR endeavour, consists of three components and spans an area of 1,000 m2, providing testbeds of simulated lunar environments. The main sections within the facility are a large area filled with lunar mare regolith simulant resembling mare regions and a smaller, individual “Dust Chamber”. The latter replicates highland conditions and contains approximately 20 tons of material, specifically simulating the fine-particle lunar regolith portion up to 250 µm. The Dust Chamber serves as a platform for testing various technologies, such as mechanical tools, robotic operations, in-situ resource utilization activities, and astronaut attire, as well as different procedures including rover and astronaut tasks. This work represents the geotechnical, geochemical and mineralogical characterization of the Lumina Sustainable Materials Ltd. 2023 batch highland simulants, from which Lunar250 is intended for use in the Luna Dust Chamber. Additionally, this work provides new results for ESA’s mare simulant, EAC-1. We provide data on particle size distribution, particle shape, abrasivity, density, water content, major and trace element geochemistry and modal mineralogy. As the simulants in the Luna Facility will be constantly overseen, this work organized by the Vulcan Facility (ESA) intends to support the monitoring of the geotechnical property variations of the simulants over time. Ultimately, we analysed several properties with different tools to emphasize how different methods and instruments affect the variability and reliability of the results.

IoRT-Based Middleware for Heterogeneous Multi-Robot Systems

The concurrence of social robots with different functionalities and cyber-physical systems in indoor environments has recently been increasing in many fields, such as medicine, education, and industry. In such scenarios, the collaboration of such heterogeneous robots demands effective communication for task completion. The concept of the Internet of Robotic Things (IoRT) is introduced as a potential solution, leveraging technologies like Artificial Intelligence, Cloud Computing, and Mesh Networks. This paper proposes an IoRT-based middleware that allows the communication of different types of robot operating systems in dynamic environments, using a cloud-based protocol. This middleware facilitates task assignment, training, and planning for heterogeneous robots, while enabling distributed communication via WiFi. The system operates in two control modes: local and cloud-based, for flexible communication and information distribution. This work highlights the challenges of current communication methods, particularly in ensuring information reach, agility, and handling diverse robots. To demonstrate the middleware suitability and applicability, an implementation of a proof-of-concept is shown in a touristic scenario where several guide robots can collaborate by effectively sharing information gathered from their heterogeneous sensor systems, with the aid of cloud processing or even internal communication processes. Results show that the performance of the middleware allows real-time applications for heterogeneous multi-robot systems in different domains.

Collaborative Robot Teleoperation in Mixed Reality Environment for Inspection Tasks

Abstract Collaborative robots are increasing their presence in the industrial production context due to their flexibility in the tasks they can perform. However, programming and teleoperating them could be a difficult task from the perspective of an unqualified worker. In cases where the worker has to collaborate remotely with a robotic arm, the probability of collisions between the robotic arm and the environment increase. In this work, we describe the design and implementation of a mixed-reality multimodal interface for a worker to teleoperate a robotic arm by interacting with its holographic digital twin, while remaining aware of surrounding obstacles to avoid any collisions. To teleoperate the robot, we have developed a multimodal interface with which the user can communicate naturally with voice (through a semantics-based task-oriented dialogue system), and by performing manual actions on holographic objects. The application also provides audiovisual information to the user about the status of the collaborative robot as well as the actions commanded. We compute and command the behavior of the robotic arm with ROS (Robot Operating System) and “MoveIt!” (motion planning framework), and we represent computed trajectories and the real-time pose of the robot in the mixed reality environment.

Research on Real-Time Multi-Robot Task Allocation Method Based on Monte Carlo Tree Search

Task allocation is an important problem in multi-robot systems, particularly in dynamic and unpredictable environments such as offshore oil platforms, large-scale factories, or disaster response scenarios, where high change rates, uncertain state transitions, and varying task demands challenge the predictability and stability of robot operations. Traditional static task allocation strategies often struggle to meet the efficiency and responsiveness demands of these complex settings, while optimization heuristics, though improving planning time, exhibit limited scalability. To address these limitations, this paper proposes a task allocation method based on the Monte Carlo Tree Search (MCTS) algorithm, which leverages the anytime property of MCTS to achieve a balance between fast response and continuous optimization. Firstly, the centralized adaptive MCTS algorithm generates preliminary solutions and monitors the state of the robots in minimal time. It utilizes dynamic Upper Confidence Bounds for Trees (UCT) values to accommodate varying task dimensions, outperforming the heuristic Multi-Robot Goal Assignment (MRGA) method in both planning time and overall task completion time. Furthermore, the parallelized distributed MCTS algorithm reduces algorithmic complexity and enhances computational efficiency through importance sampling and parallel processing. Experimental results demonstrate that the proposed method significantly reduces computation time while maintaining task allocation performance, decreasing the variance of planning results and improving algorithmic stability. Our approach enables more flexible and efficient task allocation in dynamically evolving and complex environments, providing robust support for the deployment of multi-robot systems.

EEG-Based Mobile Robot Control Using Deep Learning and ROS Integration

Efficient BCIs (Brain-Computer Interfaces) harnessing EEG (Electroencephalography) have shown potential in controlling mobile robots, also presenting new possibilities for assistive technologies. This study explores the integration of advanced deep learning models—ASTGCN, EEGNetv4, and a combined CNN-LSTM architecture—with ROS (Robot Operating System) to control a two-wheeled mobile robot. The models were trained using a published EEG dataset, which includes signals from subjects performing thought-based tasks. Each model was evaluated based on its accuracy, F1-score, and latency. The CNN-LSTM architecture model exhibited the best performance on the cross-subject strategy with an accuracy of 88.5%, demonstrating significant potential for real-time applications. Integration with ROS was facilitated through a custom middleware, enabling seamless translation of neural commands into robot movements. The findings indicate that the CNN-LSTM model not only outperforms existing EEG-based systems in terms of accuracy but also underscores the practical feasibility of implementing such systems in real-world scenarios. Considering its efficacy, CNN-LSTM shows a great potential for assistive technology in the future. This research contributes to the development of a more intuitive and accessible robotic control system, potentially enhancing the quality of life for individuals with mobility impairments.

Optimal dimensioning of elastic-link manipulators regarding lifetime estimation

Resourceful operation and design of robots is key for sustainable industrial automation. This will be enabled by lightweight design along with time and energy optimal control of robotic manipulators. Design and control of such systems is intertwined as the control must take into account inherent mechanical compliance while the design must accommodate the dynamic requirements demanded by the control. As basis for such design optimization, a method for estimating the lifetime of elastic link robotic manipulators is presented. This is applied to the geometry optimization of flexible serial manipulators performing pick-and-place operations, where the optimization objective is a combination of overall weight and vibration amplitudes. The lifetime estimation draws from a fatigue analysis combining the rainflow counting algorithm and the method of critical cutting plane. Tresca hypothesis is used to formulate an equivalent stress, and linear damage accumulation is assumed. The final robot geometry is selected from a Pareto front as a tradeoff of lifetime and vibration characteristic. The method is illustrated for a three degrees of freedom articulated robotic manipulator.

Research on UAV Autonomous Recognition and Approach Method for Linear Target Splicing Sleeves Based on Deep Learning and Active Stereo Vision

This study proposes an autonomous recognition and approach method for unmanned aerial vehicles (UAVs) targeting linear splicing sleeves. By integrating deep learning and active stereo vision, this method addresses the navigation challenges faced by UAVs during the identification, localization, and docking of splicing sleeves on overhead power transmission lines. First, a two-stage localization strategy, LC (Local Clustering)-RB (Reparameterization Block)-YOLO (You Only Look Once)v8n (OBB (Oriented Bounding Box)), is developed for linear target splicing sleeves. This strategy ensures rapid, accurate, and reliable recognition and localization while generating precise waypoints for UAV docking with splicing sleeves. Next, virtual reality technology is utilized to expand the splicing sleeve dataset, creating the DSS dataset tailored to diverse scenarios. This enhancement improves the robustness and generalization capability of the recognition model. Finally, a UAV approach splicing sleeve (UAV-ASS) visual navigation simulation platform is developed using the Robot Operating System (ROS), the PX4 open-source flight control system, and the GAZEBO 3D robotics simulator. This platform simulates the UAV’s final approach to the splicing sleeves. Experimental results demonstrate that, on the DSS dataset, the RB-YOLOv8n(OBB) model achieves a mean average precision (mAP0.5) of 96.4%, with an image inference speed of 86.41 frames per second. By incorporating the LC-based fine localization method, the five rotational bounding box parameters (x, y, w, h, and angle) of the splicing sleeve achieve a mean relative error (MRE) ranging from 3.39% to 4.21%. Additionally, the correlation coefficients (ρ) with manually annotated positions improve to 0.99, 0.99, 0.98, 0.95, and 0.98, respectively. These improvements significantly enhance the accuracy and stability of splicing sleeve localization. Moreover, the developed UAV-ASS visual navigation simulation platform effectively validates high-risk algorithms for UAV autonomous recognition and docking with splicing sleeves on power transmission lines, reducing testing costs and associated safety risks.

Artificial Intelligence in Law and Legal Analytics

The author discusses the concept of artificial intelligence and analyses the approaches to its concept and definitions existing in science. In a sketch of emergence of artificial intelligence the author notes artificial intelligence represents a natural stage in the development of technical devices designed to facilitate human intellectual activity. The author offers a classification of devices endowed with artificial intelligence, distinguishing between narrow (weak) AI and general (strong) AI; he outlines the most relevant areas of its development and describes the development stages. Among the spheres of possible application of devices with AI, the author lists the search for and structuring of information; identification of new connections and patterns that humans cannot see; assisting humans in professional activities; relieving humans from time-consuming and unproductive intellectual activities; management automation; assistance in making optimal management decisions. The article places a special focus on the threats and risks associated with the proliferation of AI. The author believes these include: displacement of humans from socially important spheres of activity, and job hijacking; decline in the level of education and qualification of workers leading to degradation of human intellect; corruption of humanity by idle and meaningless existence leading to its physical and cultural degradation; danger of robots making erroneous technical, economic, environmental, medical, etc. decisions; threat of failures in the operation of industrial robots and computerised control systems; particularly dangerous is the deliberate use of robots to cause harm, including the use of military and security systems capable of causing harm to people and property. The article discusses various options for granting AI a legal status. The author assumes a device endowed with artificial intelligence is a complex and autonomously operating tool of human activity, relatively independent of human person. The latter, however, is fully responsible for the consequences of the use of this tool. The author is sure it is counterproductive to artificially extend legal statuses developed for man, who is an individual endowed with consciousness and will, to AI. The article contains results of a “parallel exam” on legal analytics for students and artificial intelligence held at the Department of Law of the National Research University Higher School of Economics in May 2023. Also the author discusses specific areas and examples of the use of AI in law and legal analytics in the spheres of law -making, law enforcement, jurisprudence, and education.

CNN-Based Multi-Object Detection and Segmentation in 3D LiDAR Data for Dynamic Industrial Environments

Autonomous navigation in dynamic environments presents a significant challenge for mobile robotic systems. This paper proposes a novel approach utilizing Convolutional Neural Networks (CNNs) for multi-object detection in 3D space and 2D segmentation using bird’s eye view (BEV) maps derived from 3D Light Detection and Ranging (LiDAR) data. Our method aims to enable mobile robots to localize movable objects and their occupancy, which is crucial for safe and efficient navigation. To address the scarcity of labeled real-world datasets, a synthetic dataset based on a simulation environment is generated to train and evaluate our model. Additionally, we employ a subset of the NVIDIA r2b dataset for evaluation in the real world. Furthermore, we integrate our CNN-based detection and segmentation model into a Robot Operating System 2 (ROS2) framework, facilitating communication between mobile robots and a centralized node for data aggregation and map creation. Our experimental results demonstrate promising performance, showcasing the potential applicability of our approach in future assembly systems. While further validation with real-world data is warranted, our work contributes to advancing perception systems by proposing a solution for multi-source, multi-object tracking and mapping.

Comparison of Collision Avoidance Algorithms for Unmanned Surface Vehicle Through Free-Running Test: Collision Risk Index, Artificial Potential Field, and Safety Zone

This paper details the development of a collision avoidance algorithm for unmanned surface vehicles (USVs) and its validation using free-running tests. The USV, designed as a catamaran, incorporates a variety of sensors for its guidance, navigation, and control system. It performs turning maneuvers using thrusters positioned on the port and starboard sides. The robot operating system is used to streamline communication, transmitting data such as position, orientation, and situational information from diverse sensors. Using the collision risk index (CRI) method, the algorithm calculates risk based on the distance to obstacles and the angle to the desired waypoint, directing the USV on a path with minimized risk. Noise within the data captured by the two-dimensional light detection and ranging system is filtered out using the k-dimensional tree and Euclidean distance methods, ensuring single obstacles are distinctly identified. To assess the efficacy of the CRI-based collision avoidance algorithm, it was benchmarked against other algorithms rooted in the artificial potential field and safety zone methods within an artificial tank setting. The results highlight the CRI method’s superior time efficiency and optimality in comparison to its counterparts.

Teleoperation system for multiple robots with intuitive hand recognition interface

Robotic teleoperation is essential for hazardous environments where human safety is at risk. However, efficient and intuitive human–machine interaction for multi-robot systems remains challenging. This article aims to demonstrate a robotic teleoperation system, denominated AutoNav, centered around autonomous navigation and gesture commands interpreted through computer vision. The central focus is on recognizing the palm of the hand as a control interface to facilitate human–machine interaction in the context of multi-robots. The MediaPipe framework was integrated to implement gesture recognition from a USB camera. The system was developed using the Robot Operating System, employing a simulated environment that includes the Gazebo and RViz applications with multiple TurtleBot 3 robots. The main results show a reduction of approximately 50% in the execution time, coupled with an increase in free time during teleoperation, reaching up to 94% of the total execution time. Furthermore, there is a decrease in collisions. These results demonstrate the effectiveness and practicality of the robotic control algorithm, showcasing its promise in managing teleoperations across multi-robots. This study fills a knowledge gap by developing a hand gesture-based control interface for more efficient and safer multi-robot teleoperation. These findings enhance human–machine interaction in complex robotic operations. A video showing the system working is available at https://youtu.be/94S4nJ3IwUw.

A Latency Composition Analysis for Telerobotic Performance Insights Across Various Network Scenarios

Telerobotics involves the operation of robots from a distance, often using advanced communication technologies combining wireless and wired technologies and a variety of protocols. This application domain is crucial because it allows humans to interact with and control robotic systems safely and from a distance, often performing activities in hazardous or inaccessible environments. Thus, by enabling remote operations, telerobotics not only enhances safety but also expands the possibilities for medical and industrial applications. In some use cases, telerobotics bridges the gap between human skill and robotic precision, making the completion of complex tasks requiring high accuracy possible without being physically present. With the growing availability of high-speed networks around the world, especially with the advent of 5G cellular technologies, applications of telerobotics can now span a gamut of scenarios ranging from remote control in the same room to robotic control across the globe. However, there are a variety of factors that can impact the control precision of the robotic platform and user experience of the teleoperator. One such critical factor is latency, especially across large geographical areas or complex network topologies. Consequently, military telerobotics and remote operations, for example, rely on dedicated communications infrastructure for such tasks. However, this creates a barrier to entry for many other applications and domains, as the cost of dedicated infrastructure would be prohibitive. In this paper, we examine the network latency of robotic control over shared network resources in a variety of network settings, such as a local network, access-controlled networks through Wi-Fi and cellular, and a remote transatlantic connection between Finland and the United States. The aim of this study is to quantify and evaluate the constituent latency components that comprise the control feedback loop of this telerobotics experience—of a camera feed for an operator to observe the telerobotic platform’s environment in one direction and the control communications from the operator to the robot in the reverse direction. The results show stable average round-trip latency of 6.6 ms for local network connection, 58.4 ms when connecting over Wi-Fi, 115.4 ms when connecting through cellular, and 240.7 ms when connecting from Finland to the United States over a VPN access-controlled network. 255,235,61. These findings provide a better understanding of the capabilities and performance limitations of conducting telerobotics activities over commodity networks, and lay the foundation of our future work to use these insights for optimizing the overall user experience and the responsiveness of this control loop.

Robotic versus laparoscopic cholecystectomy: Can they be compared? A narrative review and personal considerations disproving low-level evidence.

Laparoscopic cholecystectomy (LC) is the gold standard for the treatment of symptomatic gallstones, acute cholecystitis, and acute gallstone pancreatitis. In recent years, the development and diffusion of robotic surgery have provided surgeons with the opportunity to apply this innovative approach to cholecystectomy, yielding interesting results. However, as with any new surgical technique, robotic cholecystectomy (RC) has met with skepticism within the surgical community. Beyond the understandable concerns regarding increased costs, some authors have claimed that RC is associated with a higher complication rate compared to LC. We reviewed the existing literature on this subject, discussing the limitations and strengths of the most significant publications and critically analyzing them. The analysis of the literature indicates that RC is safe and effective, with no definitive evidence of its inferiority compared to LC. Some of the published papers are of low quality and biased, even with significant sample sizes. Furthermore, we believe that comparing an established technique like LC with a new and not yet standardized one such as RC is somewhat illogical. RC represents a significant advance in minimally invasive surgery and should be viewed as an opportunity to familiarize oneself with the robotic device and to enhance the surgeon's skills in preparation for more complex robotic operations. The robotic approach can be beneficial in selected cases of cholecystectomy where fine dissection is required. With further reductions in costs, RC could become the future gold standard for benign gallbladder disorders.

Theory and experiment for autonomous quadrotor flight via bounded robust finite-time homogeneous sliding mode control: A roadmap to search and rescue

This paper introduces a new saturated robust control technique for quadrotor aircraft modeled by second-order Ordinary Differential Equations (ODEs), considering disturbances in the control channel (matched disturbances). The control design employs a Sliding Mode Control (SMC) approach, featuring in: (i) a novel saturated homogeneous sliding manifold, (ii) a novel tracking controller, namely, Bounded Robust Finite-Time Homogeneous Sliding Mode Control (BRFTHSMC). An Improved Fixed-time Convergent Extended State Observer (IFCESO) is incorporated into the control scheme to handle disturbances. Together the BRFTHSMC and the IFCESO establish a reliable Active Disturbance Rejection Control (ADRC) framework. The latter ensures finite-time convergence of the errors quantities to the origin along with effective disturbance rejection. Rigorous stability analysis is conducted based on Lyapunov theory. Beside control design, another distinguishing theoretical outcome of this paper in the form of Corollary is the extension of the present results to integrator-type systems (higher-order systems). The study is substantiated through MATLAB®/Simulink simulations and Robot Operating System (ROS)/Gazebo implementation, validating the theoretical foundation. Extensive experimental tests on real hardware, including attitude and Cartesian trajectory tracking under various disturbances, further affirm the theoretical findings. The synthesized control system surpasses alternative methods in terms of control signal’s boundedness, finite-time tracking stability, transient response performance, and steady-state precision. Notably, the control input circumvents singularity challenges observed in conventional SMC approaches. In addition to trajectory tracking experiments, the controller’s effectiveness is demonstrated in real-world search and rescue scenarios. Therefore, a Deep Neural Network (DNN) algorithm, based on a MS COCO-pretrained Single Shot Detector (SSD-Mobilenet-v2), is employed for a person detection mission in an unknown search area.