Real Time Path Planning of Industrial Robots in an Unknown Environment Using Vision

Industrial manipulators are machines that perform pre-programmed tasks with great accuracy and speed. The addition of intelligence through sensory perceptions would make them highly flexible in terms of performance and application. The objective of this research is to improve the performance of a six-degree of freedom industrial manipulator by introducing vision feedback and by using the data to generate its path in real time. This would enable the robot to generate its own path and alter it dynamically, based on the visual feedback. This technique will be finally used to catch a ball or pick a ball rolling on the floor. One of the industrial applications could be robotic recycling where a robot is required to pick up different objects from a moving conveyor.https://ecommons.udayton.edu/stander_posters/1014/thumbnail.jp

Minimum Distance and Minimum Time Optimal Path Planning with Bioinspired Machine Learning Algorithms for Impaired Unmanned Air Vehicles

Unmanned air vehicles operate in highly dynamic and unknown environments where they can encounter unexpected and unseen failures. In the presence of emergencies, autonomous unmanned air vehicles should be able to land at a minimum distance or minimum time. Impaired unmanned air vehicles define actuator failures and this impairment changes their unstable and uncertain dynamics; henceforth, path planning algorithms must be adaptive and model-free. In addition, path planning optimization problems must consider the unavoidable actuator saturations, kinematic and dynamic constraints for successful real-time applications. Therefore, this paper develops 3D path planning algorithms for quadrotors with parametric uncertainties and various constraints. In this respect, this paper constructs a multi-dimensional particle swarm optimization and a multi-dimensional genetic algorithm to plan paths for translational, rotational, and Euler angles and generates the corresponding control signals. The algorithms are assessed and compared both in the simulation and experimental environments. Results show that the multi-dimensional genetic algorithm produces shorter minimum distance and minimum time paths under the constraints. The real-time experiments prove that the quadrotor exactly follows the produced path utilizing the available maximum rotor speeds

Efficient Navigation and Motion Control for Autonomous Forklifts in Smart Warehouses: LSPB Trajectory Planning and MPC Implementation

The rise of smart factories and warehouses has ushered in an era of intelligent manufacturing, with autonomous robots playing a pivotal role. This study focuses on improving the navigation and control of autonomous forklifts in warehouse environments. It introduces an innovative approach that combines a modified Linear Segment with Parabolic Blends (LSPB) trajectory planning with Model Predictive Control (MPC) to ensure efficient and secure robot movement. To validate the performance of our proposed path-planning method, MATLAB-based simulations were conducted in various scenarios, including rectangular and warehouse-like environments, to demonstrate the feasibility and effectiveness of the proposed method. The results demonstrated the feasibility of employing Mecanum wheel-based robots in automated warehouses. Also, to show the superiority of the proposed control algorithm performance, the navigation results were compared with the performance of a system using the PID control as a lower-level controller. By offering an optimized path-planning approach, our study enhances the operational efficiency and effectiveness of Mecanum wheel robots in real-world applications such as automated warehousing systems

Industrial robot trajectory optimization- a review

This paper is the result of the literature review on the trajectory optimization of the serial industrial robots, a study developed in a wider research in the field of the trajectory generation mechanisms of the serial industrial robots. After a short presentation of the importance of the industrial robots in the current context and future challenges, are presented the main optimal trajectory planning criteria approached in several specialized scientific papers, especially in the last few years

MUSME 2011 4 th International Symposium on Multibody Systems and Mechatronics

El libro de actas recoge las aportaciones de los autores a través de los correspondientes artículos a la Dinámica de Sistemas Multicuerpo y la Mecatrónica (Musme). Estas disciplinas se han convertido en una importante herramienta para diseñar máquinas, analizar prototipos virtuales y realizar análisis CAD sobre complejos sistemas mecánicos articulados multicuerpo. La dinámica de sistemas multicuerpo comprende un gran número de aspectos que incluyen la mecánica, dinámica estructural, matemáticas aplicadas, métodos de control, ciencia de los ordenadores y mecatrónica. Los artículos recogidos en el libro de actas están relacionados con alguno de los siguientes tópicos del congreso: Análisis y síntesis de mecanismos ; Diseño de algoritmos para sistemas mecatrónicos ; Procedimientos de simulación y resultados ; Prototipos y rendimiento ; Robots y micromáquinas ; Validaciones experimentales ; Teoría de simulación mecatrónica ; Sistemas mecatrónicos ; Control de sistemas mecatrónicosUniversitat Politècnica de València (2011). MUSME 2011 4 th International Symposium on Multibody Systems and Mechatronics. Editorial Universitat Politècnica de València. http://hdl.handle.net/10251/13224Archivo delegad