Path Following for an Omnidirectional Robot Using a Non-Linear Model Predictive Controller for Intelligent Warehouses

Abstract

This paper presents results coming from a non-linear model predictive controller used to generate optimized trajectories specifically for an omnidirectional robot equipped with a spraying unit to mark on the floor the perimeter of dangerous areas or to move large palletized goods inside warehouses. Results on different trajectories and with moving obstacles are provided along with considerations on the controller performance.

Synchronization Control for a Mobile Manipulator Robot (MMR) System: A First Approach Using Trajectory Tracking Master–Slave Configuration

“…See Figure 3. KUKA youBot wheels are of the Mecanum type; these kind of wheels are composed of a series of rollers placed at 45 degrees from the rotation axis and allow for longitudinal and transversal motion; however, when the two wheels of each side rotate at the same angular speed and direction, the transversal velocity is zero, and the omnidirectional base can only have longitudinal and angular velocities (see the kinematic model for an omnidirectional platfom in [16]).…”

Automated Symbolic Processes for Dynamic Modeling of Redundant Manipulator Robots

“…Many of these applications require, for example, effective trajectory tracking capacities that-to improve their performance-should consider a dynamic model of a manipulator robot [9][10][11]. Knowledge and modeling of a manipulator robot's dynamics are crucial for the optimal performance of its control strategies (based on the robot model), such as inverse dynamic control, calculated torque control, and model predictive control [12][13][14]. An effective dynamic model together with a robust controller not only allow for the optimal design of the trajectory planning scheme but also for the safe and accurate maneuvering of the manipulator robot when getting close to the grip point of an object [15,16].…”

Robotics and AI for Precision Agriculture

“…The proposed approach is tested on the Tiago robot during the fulfillment of four agricultural activities, such as digging, seeding, irrigation and harvesting. A nonlinear model predictive controller is presented in [6] to generate optimal trajectories for an omnidirectional robot. The results are provided on different trajectories and with moving obstacles along with considerations on controller performance.In the field of precision agriculture, the automation of sampling and harvesting operations plays a central role in expanding the possible application scenarios.…”