Intelligent strategies for mobile robotics in laboratory automation

In this thesis a new intelligent framework is presented for the mobile robots in laboratory automation, which includes: a new multi-floor indoor navigation method is presented and an intelligent multi-floor path planning is proposed; a new signal filtering method is presented for the robots to forecast their indoor coordinates; a new human feature based strategy is proposed for the robot-human smart collision avoidance; a new robot power forecasting method is proposed to decide a distributed transportation task; a new blind approach is presented for the arm manipulations for the robots

Design and Development of an Automated Mobile Manipulator for Industrial Applications

This thesis presents the modeling, control and coordination of an automated mobile manipulator. A mobile manipulator in this investigation consists of a robotic manipulator and a mobile platform resulting in a hybrid mechanism that includes a mobile platform for locomotion and a manipulator arm for manipulation. The structural complexity of a mobile manipulator is the main challenging issue because it includes several problems like adapting a manipulator and a redundancy mobile platform at non-holonomic constraints. The objective of the thesis is to fabricate an automated mobile manipulator and develop control algorithms that effectively coordinate the arm manipulation and mobility of mobile platform. The research work starts with deriving the motion equations of mobile manipulators. The derivation introduced here makes use of motion equations of robot manipulators and mobile platforms separately, and then integrated them as one entity. The kinematic analysis is performed in two ways namely forward & inverse kinematics. The motion analysis is performed for various WMPs such as, Omnidirectional WMP, Differential three WMP, Three wheeled omni-steer WMP, Tricycle WMP and Two steer WMP. From the obtained motion analysis results, Differential three WMP is chosen as the mobile platform for the developed mobile manipulator. Later motion analysis is carried out for 4-axis articulated arm. Danvit-Hartenberg representation is implemented to perform forward kinematic analysis. Because of this representation, one can easily understand the kinematic equation for a robotic arm. From the obtained arm equation, Inverse kinematic model for the 4-axis robotic manipulator is developed. Motion planning of an intelligent mobile robot is one of the most vital issues in the field of robotics, which includes the generation of optimal collision free trajectories within its work space and finally reaches its target position. For solving this problem, two evolutionary algorithms namely Particle Swarm Optimization (PSO) and Artificial Immune System (AIS) are introduced to move the mobile platform in intelligent manner. The developed algorithms are effective in avoiding obstacles, trap situations and generating optimal paths within its unknown environments. Once the robot reaches its goal (within the work space of the manipulator), the manipulator will generate its trajectories according to task assigned by the user. Simulation analyses are performed using MATLAB-2010 in order to validate the feasibility of the developed methodologies in various unknown environments. Additionally, experiments are carried out on an automated mobile manipulator. ATmega16 Microcontrollers are used to enable the entire robot system movement in desired trajectories by means of robot interface application program. The control program is developed in robot software (Keil) to control the mobile manipulator servomotors via a serial connection through a personal computer. To support the proposed control algorithms both simulation and experimental results are presented. Moreover, validation of the developed methodologies has been made with the ER-400 mobile platform

Advanced Strategies for Robot Manipulators

Amongst the robotic systems, robot manipulators have proven themselves to be of increasing importance and are widely adopted to substitute for human in repetitive and/or hazardous tasks. Modern manipulators are designed complicatedly and need to do more precise, crucial and critical tasks. So, the simple traditional control methods cannot be efficient, and advanced control strategies with considering special constraints are needed to establish. In spite of the fact that groundbreaking researches have been carried out in this realm until now, there are still many novel aspects which have to be explored

Design and Analysis of 7-DOF Human-Link Manipulator Based on Hybrid Intelligent Controller

A manipulator is an alternative to progress profitability in mechanical computerization. The robotic controller executes forms’ assembly operations in hazardous conditions. Since computerized controllers are highly vulnerable nonlinear powerful frameworks, it is hard to provide precise unique conditions at controlling laws’ configuration. Virtual Reality (VR) is a fundamental advance at use in modern biomedical, medical procedures and different fields where a 3D object helps to comprehend complex behavior. This work proposes the interaction with 3D models in VR environment achieved by accurate sensing from feedback, and then reconstructs the instruction according to practical limitation of a real human arm movement. In this work ANFIS played a key role in finding results with optimal values because the combination of Neural Networks (NN) benefits and obscure logic systems research examined the individual defects in both of them. Use of Artificial Neural Networks (ANN) in dynamic systems has quite extensive and accurate results, when adding a training signal system to the mixed learning base implemented at combining the slope proportions technique, a Least Square Error (LSE) preparing the ANFIS organization for any framework to take care of the issue any way. This work presents a keen controller actualization with 7-DOF controller for a model designed with a VR situation that reproduces the system design by associating Matlab/Simulink to connect the VR model with some instruction to execute orders delivered by the hybrid intelligent controller based on ANFIS technique. Palatable outcomes are implemented in reproductions that improve the procedure as an essential utilization of this controller design

Biomimetic Manipulator Control Design for Bimanual Tasks in the Natural Environment

As robots become more prolific in the human environment, it is important that safe operational procedures are introduced at the same time; typical robot control methods are often very stiff to maintain good positional tracking, but this makes contact (purposeful or accidental) with the robot dangerous. In addition, if robots are to work cooperatively with humans, natural interaction between agents will make tasks easier to perform with less effort and learning time. Stability of the robot is particularly important in this situation, especially as outside forces are likely to affect the manipulator when in a close working environment; for example, a user leaning on the arm, or task-related disturbance at the end-effector. Recent research has discovered the mechanisms of how humans adapt the applied force and impedance during tasks. Studies have been performed to apply this adaptation to robots, with promising results showing an improvement in tracking and effort reduction over other adaptive methods. The basic algorithm is straightforward to implement, and allows the robot to be compliant most of the time and only stiff when required by the task. This allows the robot to work in an environment close to humans, but also suggests that it could create a natural work interaction with a human. In addition, no force sensor is needed, which means the algorithm can be implemented on almost any robot. This work develops a stable control method for bimanual robot tasks, which could also be applied to robot-human interactive tasks. A dynamic model of the Baxter robot is created and verified, which is then used for controller simulations. The biomimetic control algorithm forms the basis of the controller, which is developed into a hybrid control system to improve both task-space and joint-space control when the manipulator is disturbed in the natural environment. Fuzzy systems are implemented to remove the need for repetitive and time consuming parameter tuning, and also allows the controller to actively improve performance during the task. Experimental simulations are performed, and demonstrate how the hybrid task/joint-space controller performs better than either of the component parts under the same conditions. The fuzzy tuning method is then applied to the hybrid controller, which is shown to slightly improve performance as well as automating the gain tuning process. In summary, a novel biomimetic hybrid controller is presented, with a fuzzy mechanism to avoid the gain tuning process, finalised with a demonstration of task-suitability in a bimanual-type situation.EPSR