Design of an adaptive neural predictive nonlinear controller for nonholonomic mobile robot system based on posture identifier in the presence of disturbance

This paper proposes an adaptive neural predictive nonlinear controller to guide a nonholonomic wheeled mobile robot during continuous and non-continuous gradients trajectory tracking. The structure of the controller consists of two models that describe the kinematics and dynamics of the mobile robot system and a feedforward neural controller. The models are modified Elman neural network and feedforward multi-layer perceptron respectively. The modified Elman neural network model is trained off-line and on-line stages to guarantee the outputs of the model accurately represent the actual outputs of the mobile robot system. The trained neural model acts as the position and orientation identifier. The feedforward neural controller is trained off-line and adaptive weights are adapted on-line to find the reference torques, which controls the steady-state outputs of the mobile robot system. The feedback neural controller is based on the posture neural identifier and quadratic performance index optimization algorithm to find the optimal torque action in the transient state for N-step-ahead prediction. General back propagation algorithm is used to learn the feedforward neural controller and the posture neural identifier. Simulation results show the effectiveness of the proposed adaptive neural predictive control algorithm; this is demonstrated by the minimised tracking error and the smoothness of the torque control signal obtained with bounded external disturbances

Neural Network Controller Design for a Mobile Robot Navigation; a Case Study

Mobile robot are widely applied in various aspect of human  life. The main issue of this type of robot is how to navigate safely to reach the goal or finish the assigned task  when applied autonomously in dynamic and uncertain environment. The  ap- plication of artificial intelligence, namely neural   network,  can provide a ”brain” for the robot to navigate safely in completing the assigned task. By applying neural network, the complexity of mobile robot control can be  reduced by choosing the right model of the system, either   from mathematical modeling or directly taken from the input of sensory data  information. In this study, we compare the presented methods of previous  researches that applies neural network to mobile robot navigation. The comparison  is started  by considering  the right  mathematical model for the robot, getting the Jacobian  matrix  for online training, and giving the achieved input model to  the designed neural network layers in order to get the estimated position of the robot. From this literature study, it  is concluded that the consideration of both kinematics and dynamics modeling  of the robot will result in better performance since the exact parameters of the system are known

Levenberg-Marquardt optimised neural networks for trajectory tracking of autonomous ground vehicles

Trajectory tracking is an essential capability of robotics operation in industrial automation. In this article, an artificial neural controller is proposed to tackle trajectory-tracking problem of an autonomous ground vehicle (AGV). The controller is implemented based on fractional order proportional integral derivative (FOPID) control that was already designed in an earlier work. A non-holonomic model type of AGV is analysed and presented. The model includes the kinematic, dynamic characteristics and the actuation system of the VGA. The artificial neural controller consists of two artificial neural networks (ANNs) that are designed to control the inputs of the AGV. In order to train the two artificial neural networks, Levenberg-Marquardt (LM) algorithm was used to obtain the parameters of the ANNs. The validation of the proposed controller has been verified through a given reference trajectory. The obtained results show a considerable improvement in term of minimising trajectory tracking error over the FOPID controller

Mobile Robot Navigation in Static and Dynamic Environments using Various Soft Computing Techniques

The applications of the autonomous mobile robot in many fields such as industry, space, defence and transportation, and other social sectors are growing day by day. The mobile robot performs many tasks such as rescue operation, patrolling, disaster relief, planetary exploration, and material handling, etc. Therefore, an intelligent mobile robot is required that could travel autonomously in various static and dynamic environments. The present research focuses on the design and implementation of the intelligent navigation algorithms, which is capable of navigating a mobile robot autonomously in static as well as dynamic environments. Navigation and obstacle avoidance are one of the most important tasks for any mobile robots. The primary objective of this research work is to improve the navigation accuracy and efficiency of the mobile robot using various soft computing techniques. In this research work, Hybrid Fuzzy (H-Fuzzy) architecture, Cascade Neuro-Fuzzy (CN-Fuzzy) architecture, Fuzzy-Simulated Annealing (Fuzzy-SA) algorithm, Wind Driven Optimization (WDO) algorithm, and Fuzzy-Wind Driven Optimization (Fuzzy-WDO) algorithm have been designed and implemented to solve the navigation problems of a mobile robot in different static and dynamic environments. The performances of these proposed techniques are demonstrated through computer simulations using MATLAB software and implemented in real time by using experimental mobile robots. Furthermore, the performances of Wind Driven Optimization algorithm and Fuzzy-Wind Driven Optimization algorithm are found to be most efficient (in terms of path length and navigation time) as compared to rest of the techniques, which verifies the effectiveness and efficiency of these newly built techniques for mobile robot navigation. The results obtained from the proposed techniques are compared with other developed techniques such as Fuzzy Logics, Genetic algorithm (GA), Neural Network, and Particle Swarm Optimization (PSO) algorithm, etc. to prove the authenticity of the proposed developed techniques

Force and impedance control for hydraulically driven hexapod robot walking on uneven terrain

A variety approach of multi-legged robot designs, especially on a large scale design with hydraulically driven actuators exist, but most of it still unsolved and used primitive techniques on control solutions. This made this area of research still far from demonstrating the scientific solutions, which is more towards developing and optimizing the algorithm, control technique and software engineering for practical locomotion (flexibility and reliability). Therefore in this thesis,the study is done to propose two categories of solution for statically stable and hydraulically driven hexapod robot, named COMET-IV, which are dynamic walking trajectory generation and force/impedance control implementation (during body start patching), in order to solve the stability problems (horizontal) that encountered when walking on extremely uneven terrains.Only three sensors are used for control feedback; potentiometers (each leg joint), pressure sensors (hydraulic cylinders) and attitude sensor (center of body). For dynamic walking trajectory generation, the fixed/determined of tripod walking trajectory is modified with force threshold-based, named as environment trailed trajectory (ETT),on each first step of foot during support phase (preliminary sensing uneven terrain surfaces). Moreover,the proposed dynamic trajectory generation is then upgraded with capability of omni-directional walking with a proposed center of body rotational-based method. The instability of using the ETT module alone and with proposed hybrid force/position control in the previous progress, during body patching on walking session is then solved using the proposed pull-back position-based force control (PPF). PPF controller is derived from the ETT module itself and supported by proposed compliant (switching) mechanism, logical attitude control and dynamic swing rising control. The limitation of PPF controller applied with ETT module for walking on uneven terrain contains extreme soft surface makes the study narrowed to the impedance control approaches as a replacement of PPF controller. Three new adaptive impedance controller are designed and proposed: Optimal single leg impedance control based on body inertia, Optimal center of mass—based impedance control based on body inertia and Single leg impedance control with self-tuning stiffness. To reduce the hard swinging/shaking of the robot's body in motion that arise after applying the proposed impedance controllers, fuzzy logic control via Takagaki-Sugeno-Kang (TSK) model is proposed to be cascaded on the input feedback of the controller.The study has verified the effectiveness of both categories of control unit (dynamic trajectory,force controller and impedance controllers) combination throughout several experiments of COMET-IV walking on uneven/unstructured terrains

Design, Construction, Energy Modeling, and Navigation of a Six-Wheeled Differential Drive Robot to Deliver Medical Supplies inside Hospitals

Differential drive mobile robots have been the most ubiquitous kind of robots for the last few decades. As each of the wheels of a differential drive mobile robot can be controlled, it provides additional flexibility to the end-users in creating new applications. These applications include personal assistance, security, warehouse and distribution applications, ocean and space exploration, etc. In a clinic or hospital, the delivery of medicines and patients’ records are frequently needed activities. Medical personnel often find these activities repetitive and time-consuming. Our research was to design, construct, produce an energy model, and develop a navigation control method for a six-wheeled differential drive robot designed to deliver medical supplies inside the hospital. Such a robot is expected to lessen the workload of medical staff. Therefore, the design and implementation of a six-wheeled differential drive robot with a password-protected medicine carrier were presented. This password-protected medicine carrier ensures that only the authorized medical personnel can receive medical supplies. The low-cost robot base and the medicine carrier were built in real life. Besides the actual robot design and fabrication, a kinematic model for the robot was developed, and a navigation control algorithm to avoid obstacles was implemented using MATLAB/Simulink. The kinematic modeling is helpful for the robot to achieve better energy optimization. To develop the object avoidance algorithm, we investigated the use of the Robot Operating System (ROS) and the Simultaneous Localization and Mapping (SLAM) algorithm for the implementation of the mapping and navigation of a robotic platform named TurtleBot 2. Finally, using the Webot robot simulator, the navigation of the six-wheeled mobile robot was demonstrated in a hospital-like simulation environment

Navigation and Control of Automated Guided Vehicle using Fuzzy Inference System and Neural Network Technique

Automatic motion planning and navigation is the primary task of an Automated Guided Vehicle (AGV) or mobile robot. All such navigation systems consist of a data collection system, a decision making system and a hardware control system. Artificial Intelligence based decision making systems have become increasingly more successful as they are capable of handling large complex calculations and have a good performance under unpredictable and imprecise environments. This research focuses on developing Fuzzy Logic and Neural Network based implementations for the navigation of an AGV by using heading angle and obstacle distances as inputs to generate the velocity and steering angle as output. The Gaussian, Triangular and Trapezoidal membership functions for the Fuzzy Inference System and the Feed forward back propagation were developed, modelled and simulated on MATLAB. The reserach presents an evaluation of the four different decision making systems and a study has been conducted to compare their performances. The hardware control for an AGV should be robust and precise. For practical implementation a prototype, that functions via DC servo motors and a gear systems, was constructed and installed on a commercial vehicle

Trajectory Tracking Control of Skid-Steering Mobile Robots with Slip and Skid Compensation using Sliding-Mode Control and Deep Learning

Slip and skid compensation is crucial for mobile robots' navigation in outdoor environments and uneven terrains. In addition to the general slipping and skidding hazards for mobile robots in outdoor environments, slip and skid cause uncertainty for the trajectory tracking system and put the validity of stability analysis at risk. Despite research in this field, having a real-world feasible online slip and skid compensation is still challenging due to the complexity of wheel-terrain interaction in outdoor environments. This paper presents a novel trajectory tracking technique with real-world feasible online slip and skid compensation at the vehicle-level for skid-steering mobile robots in outdoor environments. The sliding mode control technique is utilized to design a robust trajectory tracking system to be able to consider the parameter uncertainty of this type of robot. Two previously developed deep learning models [1], [2] are integrated into the control feedback loop to estimate the robot's slipping and undesired skidding and feed the compensator in a real-time manner. The main advantages of the proposed technique are (1) considering two slip-related parameters rather than the conventional three slip parameters at the wheel-level, and (2) having an online real-world feasible slip and skid compensator to be able to reduce the tracking errors in unforeseen environments. The experimental results show that the proposed controller with the slip and skid compensator improves the performance of the trajectory tracking system by more than 27%