IAES International Journal of Robotics and Automation (IJRA)
Robots are becoming part of people's everyday social lives and will increasingly become so. In future years, robots may become caretaker assistants for the elderly, or academic tutors for our children, or medical assistants, day care assistants, or psychological counselors. Robots may become our co-workers in factories and offices, or maids in our homes. The IAES International Journal of Robotics and Automation (IJRA) is providing a platform to researchers, scientists, engineers and practitioners throughout the world to publish the latest achievement, future challenges and exciting applications of intelligent and autonomous robots. IJRA is aiming to push the frontier of robotics into a new dimension, in which motion and intelligence play equally important roles. Its scope includes (but not limited) to the following: automation control, automation engineering, autonomous robots, biotechnology and robotics, emergence of the thinking machine, forward kinematics, household robots and automation, inverse kinematics, Jacobian and singularities, methods for teaching robots, nanotechnology and robotics (nanobots), orientation matrices, robot controller, robot structure and workspace, robotic and automation software development, robotic exploration, robotic surgery, robotic surgical procedures, robotic welding, robotics applications, robotics programming, robotics technologies, robots society and ethics, software and hardware designing for robots, spatial transformations, trajectory generation, unmanned (robotic) vehicles, etc.
Articles
512 Documents
Design of beefsteak tomato harvesting robot system in greenhouse
Thien An Dinh;
So Nam Phung;
Tri Cong Phung
IAES International Journal of Robotics and Automation (IJRA) Vol 15, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.11591/ijra.v15i2.pp353-364
One challenge for tomato harvesting robots is that some of the tomato stems were not detectable because they were hidden behind the leaves or other obstacles. The primary objective of this research is to design, simulate, and experiment with a tomato harvesting robot and propose an improved detection algorithm to overcome the above problem. The suggested detection algorithm is designed to first detect the tomato fruit itself, and if the stem is not visible, the system will automatically adjust the camera's viewing angle to provide a better perspective and uncover the hidden stem. Simulation and experimental tests were carried out in a real tomato greenhouse to evaluate the cutting and holding mechanism, as well as the camera-based detection algorithm. These experimental results confirmed the effectiveness of the gripper and detection system and revealed several challenges in the harvesting algorithm. By integrating advanced algorithms for tomato detection and harvesting, this robot will reduce damage to the tomatoes, ensuring higher quality and yield.
Design and implementation of NMPC for a two-DOF robotic arm using CasADi
Lahcen Boulbalah;
Faiza Dib;
Nabil Benaya;
Khaddouj Ben Meziane
IAES International Journal of Robotics and Automation (IJRA) Vol 15, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.11591/ijra.v15i2.pp307-318
Achieving accurate joint-space tracking in multi-link robotic arms is complicated by strong configuration-dependent nonlinearities and mandatory actuator limits that classical controllers are structurally unable to enforce. This paper presents a nonlinear model predictive control (NMPC) scheme for a two-degree-of-freedom (2-DOF) serial robotic arm, implemented within the CasADi symbolic computing environment to leverage automatic differentiation and sparse interior-point solving. The complete set of Lagrangian equations of motion-inertia, Coriolis, and gravity terms-is incorporated directly into the optimizer's prediction model through fourth-order Runge-Kutta (RK4) integration, eliminating the need for linearization. Torque, velocity, and angle bounds are imposed as native hard inequality constraints at every step of the finite-horizon optimization. Systematic simulations pit the proposed NMPC against a Ziegler-Nichols-tuned decentralized PID at two distinct sampling periods. The NMPC achieved a 95% reduction in peak tracking error relative to PID (0.0058 rad vs. 0.1347 rad for Joint 1), with mean error decreases of 64.65% and 57.58% for Joints 1 and 2 respectively, at an average solver time of 0.053 s-comfortably within the 0.1 s control cycle. The findings demonstrate that online NMPC with unabridged nonlinear dynamics is computationally practical for real-time joint control on standard computing hardware.