Omnidirectional Mobile Robot Research Articles

Parametric Identification of the Mathematical Model of the Omnidirectional Mobile Robot KUKA youBot

Parametric Identification of the Mathematical Model of the Omnidirectional Mobile Robot KUKA youBot

Instantaneous centre of rotation based motion control for omnidirectional mobile robots with sidewards off-centred wheels

AZIMUT-3 is a nonholonomic omnidirectional platform design using sidewards off-centred compliant wheels. This design makes it possible to experiment with the use of the chassis’ instantaneous centre of rotation (ICR) for motion control. Research on ICR-based motion controllers has focused on handling structural singularities and misses a more general consideration of the chassis’ kinematic and physical constraints like steering, velocity and acceleration constraints. This paper presents the design of an ICR-based motion controller for AZIMUT-3. Leveraging a new parametrization of the motion state space and the associated representation in R3 (collectively referred to as the H representation) and adapting a time scaling principle initially developed for manipulator trajectories, the designed motion controller is able to handle actuators coordination and their physical limits, as well as structural singularities. Results of tests done with the platform are presented, demonstrating the applicability of the proposed motion controller in efficiently handling these issues.

Learning from Demonstration with Gaussian Process Approach for an Omni-directional Mobile Robot

There is great potential in robotics for the application of methodologies related to machine learning, and aiming to the learning from demonstration framework, which helps in a remarkable way to reduce the programming times of the movements of a Robot and in addition to this allows the movements to be more natural and smooth. Machine learning can be found in diverse derivations as the supervised, non-supervised and reinforced learning. One of the most used is supervised learning, which can be divided into two main tasks: classification and regression. To perform regression, one of the methodologies widely used is the Gaussian Process (GP). Learning from Demonstration (LfD) can be seen as a regression problem given a data set. In this article, the development of a learning method based on Gaussian Process and applied to a mobile robotic platform is presented. This implies that the learning has to be performed very fast and incrementally, this because the data arrives frequently and abundantly. The GP offers great precision in learning and meets the characteristics to perform control in robots. This paper investigates the regression methodology using GP as well as its application in demonstration learning.

Motion Improvement of Four-Wheeled Omnidirectional Mobile Robots for Indoor Terrain

The four-wheeled omnidirectional platform is great to use for an indoor mobile robot. It can increasingly move and change heading directions. However, the robot is easy to slip when it is moving. One or more wheels are sometimes not touching the ground. This paper approaches to solving these problems by computational simulation in locomotion. The mathematical models simulate the robot movement. The robot struggles to go to the target with randomly simulated slips and untouched ground circumstances. The robot can estimate the positions using the Kalman filter and readjust itself to the planned path. Consequently, this paper demonstrates the motion improvement and compares the results of decreasing errors.

Tracking and localization for omni-directional mobile industrial robot using reflectors

In this paper, an improved tracking and localization algorithm of an omni-directional mobile industrial robot is proposed to meet the high positional accuracy requirement, improve the robot’s repeatability positioning precision in the traditional trilateral algorithm, and solve the problem of pose lost in the moving process. Laser sensors are used to identify the reflectors, and by associating the reflectors identified at a particular time with the reflectors at a previous time, an optimal triangular positioning method is applied to realize the positioning and tracking of the robot. The experimental results show that positioning accuracy can be satisfied, and the repeatability and anti-jamming ability of the omni-directional mobile industrial robot will be greatly improved via this algorithm.

Trajectory Tracking of an Omni-Directional Wheeled Mobile Robot Using a Model Predictive Control Strategy

This paper addresses trajectory tracking of an omni-directional mobile robot (OMR) with three mecanum wheels and a fully symmetrical configuration. The omni-directional wheeled robot outperforms the non-holonomic wheeled robot due to its ability to rotate and translate independently and simultaneously. A kinematics model of the OMR is established and a model predictive control (MPC) algorithm with control and system constraints is designed to achieve point stabilization and trajectory tracking. Simulation results validate the accuracy of the established kinematics model and the effectiveness of the proposed MPC controller.

Design of a passive concentric steering multi track omnidirectional mobile robot

Design of a passive concentric steering multi track omnidirectional mobile robot

Embedded System for Motion Control of an Omnidirectional Mobile Robot

In this paper, an embedded system for motion control of omnidirectional mobile robots is presented. An omnidirectional mobile robot is a type of holonomic robots. It can move simultaneously and independently in translation and rotation. The RoboCup small-size league, a robotic soccer competition, is chosen as the research platform in this paper. The first part of this research is to design and implement an embedded system that can communicate with a remote server using a wireless link, and execute received commands. Second, a fuzzy-tuned proportional–integral (PI) path planner and a related low-level controller are proposed to attain optimal input for driving a linear discrete dynamic model of the omnidirectional mobile robot. To fit the planning requirements and avoid slippage, velocity, and acceleration filters are also employed. In particular, low-level optimal controllers, such as a linear quadratic regulator (LQR) for multiple-input-multiple-output acceleration and deceleration of velocity are investigated, where an LQR controller is running on the robot with feedback from motor encoders or sensors. Simultaneously, a fuzzy adaptive PI is used as a high-level controller for position monitoring, where an appropriate vision system is used as a source of position feedback. A key contribution presented in this research is an improvement in the combined fuzzy-PI LQR controller over a traditional PI controller. Moreover, the efficiency of the proposed approach and PI controller are also discussed. Simulation and experimental evaluations are conducted with and without external disturbance. An optimal result to decrease the variances between the target trajectory and the actual output is delivered by the onboard regulator controller in this paper. The modeling and experimental results confirm the claim that utilizing the new approach in trajectory-planning controllers results in more precise motion of four-wheeled omnidirectional mobile robots.

Model-Free Detection and Following of Moving Objects by an Omnidirectional Mobile Robot using 2D Range Data

Abstract The ability of mobile robots to detect and track moving targets is frequently considered as an important behavior that is a necessary pre-requisite while developing autonomous robots intended to coexist with humans or other dynamic objects in a shared environment. If the robot can track dynamic objects this is further simplifying the development of autonomous navigation and obstacle avoidance behaviors as well as the robot ability to operate within multi-agent formations. The approach proposed in this investigation allows a KUKA youBot omnidirectional mobile platform, equipped with a 2D LiDAR sensor to detect and follow a moving target (i.e. another mobile robot) in a shared indoor environment. It uses a nonparametric representation of the dynamic objects that is independent of their classes and shapes. The target following holonomic mobile robot is also maintaining a pre-specified relative position and orientation with respect to the tracked dynamic object by controlling its own linear and angular velocity.

Swarm Behavior of Omni-Directional Mobile Robots using only Local Information

Swarm Behavior of Omni-Directional Mobile Robots using only Local Information

Flexible omnidirectional mobile robot with ciliary actuators using hydraulic skeleton

Flexible omnidirectional mobile robot with ciliary actuators using hydraulic skeleton

External force/velocity control for an autonomous rehabilitation robot

Stroke is a primary cause of death and the leading cause of permanent disability in adults. There are many stroke survivors, who live with a variety of levels of disability and always need rehabilitation activities on daily basis. Several studies have reported that usage of rehabilitation robotic devices shows the better improvement outcomes in upper-limb stroke patients than the conventional therapy-nurses or therapists actively help patients with exercise-based rehabilitation. This research focuses on the development of an autonomous robotic trainer designed to guide a stroke patient through an upper-limb rehabilitation task. The robotic device was designed and developed to automate the reaching exercise as mentioned. The designed robotic system is made up of a four-wheel omni-directional mobile robot, an ATI Gamma multi-axis force/torque sensor used to measure contact force and a microcontroller real-time operating system. Proportional plus Integral control was adapted to control the overall performance and stability of the autonomous assistive robot. External force control was successfully implemented to establish the behavioral control strategy for the robot force and velocity control scheme. In summary, the experimental results indicated satisfactorily stable performance of the robot force and velocity control can be considered acceptable. The gain tuning for proportional integral (PI) velocity control algorithms was suitably estimated using the Ziegler-Nichols method in which the optimized proportional and integral gains are 0.45 and 0.11, respectively. Additionally, the PI external force control gains were experimentally tuned using the trial and error method based on a set of experiments which allow a human participant moves the robot along the constrained circular path whilst attempting to minimize the radial force. The performance was analyzed based on the root mean square error (E_RMS) of the radial forces, in which the lower the variation in radial forces, the better the performance of the system. The outstanding performance of the tests as specified by the E_RMS of the radial force was observed with proportional and integral gains of Kp = 0.7 and Ki = 0.75, respectively.

A Study on an Omni-directional Mobile Robot for Moving a Double-parked Car

A Study on an Omni-directional Mobile Robot for Moving a Double-parked Car

Improvement of Hand-gesture-based Interface for Omni-directional Mobile Robot

Improvement of Hand-gesture-based Interface for Omni-directional Mobile Robot

Adaptive Behaviors in Autonomous Navigation with Collision Avoidance and Bounded Velocity of an Omnidirectional Mobile Robot

Integration of Control Theory and Genetic Programming paradigm toward development a family of controllers is addressed in this paper. These controllers are applied for autonomous navigation with collision avoidance and bounded velocity of an omnidirectional mobile robot. We introduce the concepts of natural and adaptive behaviors to relate each control objective with a desired behavior for the mobile robot. Natural behaviors lead the system to fulfill a task inherently. In this work, the motion of the mobile robot to achieve desired position, ensured by applying a Control-Theory-based controller, defines the natural behavior. The adaptive behavior, learned through Genetic-Programming, fits the robot motion in order to avoid collision with an obstacle while fulfilling velocity constraints. Hence, the behavior of the mobile robot is the addition of the natural and the adaptive behaviors. Our proposed methodology achieves the discovery of 9402 behaviors without collisions where asymptotic convergence to desired goal position is demonstrated by Lyapunov stability theory. Effectiveness of proposed framework is illustrated through a comparison between experiments and numerical simulations for a real mobile robot.

Study On The Target Positioning For An Omni-Directional 3 DOF Mobile Manipulator Based On Machine Vision

The omni-directional mobile robot with multi DOF, because the operation posture and operation accuracy of the manipulator can be better controlled in a narrow or crowded workplace compared with the general manipulator, is getting more interested in practical applications. The present problem is to improve its flexibility for operating multiple different targets. Target recognition with image processing is an effective solution. Based on the image processing, the position and posture of the target can be determined. Then the signal will be sent to the arm control system. In this paper, the illumination conditions, distortion, etc. are studied in the target recognition. The target position with image processing, is verified with real coordinates. The experiments show target recognition with image processing can effectively improve the flexibility of our robot.

Position compensation algorithm for omnidirectional mobile robots and its experimental evaluation

Though the basic objective of mobile robots is movement between positions, they have a drawback that position accuracy is not completely guaranteed because of the slip between the wheels and ground. Various strategies employing additional sensors have been adopted to decrease the position error. In this research, multiple ultrasonic distance sensors were used to measure the position and orientation of the mobile robot, and a position compensation algorithm was developed to minimize the position error between the current position and the desired position. In contrast to the conventional mobile robots with wheels that require at least a few steps of path planning to approach a designated position, the Mecanum wheel adopted in this research has a unique structural property that imparts omnidirectionality to the mobile robot; hence, the mobile robot can immediately move in arbitrary directions without any rotation of the body. This enables the proposed algorithm to directly eliminate the position and orientation errors without any complicated path planning. The algorithm was experimentally validated. The performance of the proposed algorithm applied to omnidirectional wheels was experimentally compared to that of independent dead-reckoning control with general wheels from the viewpoints of position accuracy and movement time.

Simultaneous balancing and trajectory tracking control for an omnidirectional mobile robot with a cylinder using switching between two robust controllers

With the growing use of mobile robots in home applications, e-commerce warehouses, and so on it is necessary to pay attention to their object carrying capability for transferring objects from one l...

The Design and Development of an Omni-Directional Mobile Robot Oriented to an Intelligent Manufacturing System.

In order to transport materials flexibly and smoothly in a tight plant environment, an omni-directional mobile robot based on four Mecanum wheels was designed. The mechanical system of the mobile robot is made up of three separable layers so as to simplify its combination and reorganization. Each modularized wheel was installed on a vertical suspension mechanism, which ensures the moving stability and keeps the distances of four wheels invariable. The control system consists of two-level controllers that implement motion control and multi-sensor data processing, respectively. In order to make the mobile robot navigate in an unknown semi-structured indoor environment, the data from a Kinect visual sensor and four wheel encoders were fused to localize the mobile robot using an extended Kalman filter with specific processing. Finally, the mobile robot was integrated in an intelligent manufacturing system for material conveying. Experimental results show that the omni-directional mobile robot can move stably and autonomously in an indoor environment and in industrial fields.

Distributed model predictive formation control with discretization-free path planning for transporting a load

This paper proposes a comprehensive control scheme tailored to the task of letting a formation of omnidirectional mobile robots transport a plate through an unknown environment purely by normal and friction forces between the robots and the plate. The scheme includes a graph-based path planning strategy that can deal with obstacles of arbitrary polygonal shapes without any discretization of the environment. The strategy permits a natural implementation of a memory functionality that allows the robots to navigate through maze-like environments by successively constructing an approximative map of the environment during the motion. Most characteristic for the proposed approach, the robots are controlled using distributed model predictive control which makes it possible to enforce constraints on the movements of the robots to allow for a successful transportation of the plate with only acceptable slipping between the robots and the plate. The proposed scheme is carefully and successfully tested in various simulations posing rather sophisticated challenges both to its navigation and formation control subaspects.