https://ejournal.unitomo.ac.id/index.php/ijair <hr> <p>Accredited by the Minister of Education, Culture, Higher Education, and Research, Republic of Indonesia, Number <strong><a href="https://drive.google.com/file/d/1_zkll3nTJmBZtFegc9d6JnXGMKp2X2kV/view?usp=drive_link">295/C/C3/KPT/2026</a></strong> as <strong>Ranking 3 (<a href="https://sinta.kemdiktisaintek.go.id/journals/profile/9351">SINTA 3</a>)</strong>.</p> <p><strong>International Journal of Artificial Intelligence &amp; Robotics (IJAIR)</strong> is one of the journals published by the Informatics Engineering Department of Dr. Soetomo University, which was established in November 2019. <strong>IJAIR </strong>is a journal with a Double-Blind Peer Review process dedicated to the publication of quality research results in the fields of Computer Science and Technology, Artificial Intelligence &amp; Robotics, but not implicitly limited to them. All publications in the IJAIR journal are open access, which allows articles to be freely available online without any subscription and free of charge. The journal publishes twice a year in November and May, containing five (5) articles per issue. Crossref provides a unique DOI for each article in this journal.</p> <p><a href="https://portal.issn.org/resource/ISSN/2686-6269"><strong>ISSN (online) : 2686-6269</strong></a></p> <p><strong>Focus and Scope</strong>:</p> <p>Machine Learning &amp; Soft Computing, Data Mining &amp; Big Data, Computer Vision &amp; Pattern Recognition, and Robotics.</p> Informatics Department-Universitas Dr. Soetomo en-US International Journal of Artificial Intelligence & Robotics (IJAIR) 2686-6269 <p dir="ltr">Authors who publish with&nbsp;<strong>International Journal of Artificial Intelligence &amp; Robotics (IJAIR)</strong> agree to the following terms:</p> <ol> <li class="show" dir="ltr"> <p dir="ltr">Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a <a href="http://creativecommons.org/licenses/by-sa/4.0/">Creative Commons Attribution License (CC BY-SA 4.0)</a> that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.&nbsp;</p> </li> <li class="show" dir="ltr"> <p dir="ltr">Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.</p> </li> <li class="show" dir="ltr"> <p dir="ltr">Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.</p> </li> </ol> https://ejournal.unitomo.ac.id/index.php/ijair/article/view/11347 <p class="Abstract" style="margin-right: 7.45pt; text-indent: 0cm;"><span lang="EN-US" style="font-weight: normal;">This paper presents a novel integrated vision-kinematics control system for autonomous robotic manipulation, leveraging deep learning and the Internet of Robotic Things (IoRT). Unlike previous works that focus on isolated modules, our framework uniquely combines real-time YOLOv5-based object detection deployed on a Raspberry Pi with geometric inverse kinematics implemented on an ESP32 controller for a 6-DOF robotic arm. Under controlled conditions (fixed camera height of 40 cm, uniform workspace illumination, and high-contrast colour-coded objects), the detection module achieved a 100% detection rate on the test set (300 images containing 900 object instances) with a mean average precision ([email protected]) of 0.93 across three object classes. The system demonstrates precise end-effector positioning with an average error of 4.3 mm in open-loop control mode, a significant achievement given the absence of joint feedback sensors. All components are seamlessly integrated via an MQTT-based IoRT communication layer, ensuring reliable, low-latency data exchange for remote monitoring and control. Comprehensive experimental validation shows an 80% success rate in autonomous pick-and-place operations, with stable performance under multi-object scenarios and communication reliability exceeding 99%. This work bridges the gap between high-cost industrial systems and low-cost research platforms by demonstrating that intelligent system integration can compensate for hardware limitations, offering a scalable, modular, and cost-effective framework for intelligent robotic manipulation in Industry 4.0 applications, particularly suitable for educational robotics and light industrial automation.</span></p> Dodit Suprianto Ginanjar Suwasono Adi Muhamad Raehan Abiyan Lukman Hakim Rini Agustina Nugroho Suharto Sri Wahyuni Dali Copyright (c) 2026 Dodit Suprianto, Ginanjar Suwasono Adi, Muhamad Raehan Abiyan , Lukman Hakim, Rini Agustina, Nugroho Suharto, Sri Wahyuni Dali http://creativecommons.org/licenses/by-sa/4.0 2026-05-31 2026-05-31 8 1 1 16 10.25139/ijair.v8i1.11347