A model-based approach to robot kinematics and control using discrete factor graphs with belief propagation

Much of recent researches in robotics have shifted the focus from traditionally-specific industrial tasks to investigations of new types of robots with alternative ways of controlling them. In this paper, we describe the development of a generic method based on factor graphs to model robot kinematics. We focused on the kinematics aspect of robot control because it provides a fast and systematic solution for the robot agent to move in a dynamic environment. We developed neurally-inspired factor graph models that can be applied on two different robotic systems: a mobile platform and a robotic arm. We also demonstrated that we can extend the static model of the robotic arm into a dynamic model useful for imitating natural movements of a human hand. We tested our methods in a simulation environment as well as in scenarios involving real robots. The experimental results proved the flexibility of our proposed methods in terms of remodeling and learning, which enabled the modeled robot to perform reliably during the execution of given tasks

CONCEPTUAL DESIGN OF AUTONOMOUS CART FOLLOWER FOR WHEELCHAIR USER

This paper focuses on kinematics of the cart follower and the system identification of propulsion system by using brushed DC motor. The cart follower uses Ackermann configuration as the steering system. The modeling of kinematics equation takes into account the instantaneous center of rotation (ICR), velocity of each tire, heading angle, and simple movement of the cart. The cart is propelled by transaxle brushed DC motor. It is important to approximate an accurate transfer function to represent the motor as the plant module is unavailable. The motor is simulated by using Arduino hardware package in MATLAB®. Rotary encoder is used to record the angular velocity of the shaft. MATLAB® code is created in order to calculate the linear velocity and tabulate the datasets. System Identification Toolbox determines the transfer function of the motor and its performance. The variables measured in experiment to identify the transfer function of the DC motor system are output angular velocity and input voltage. The parameters taken from the DC motor’s mathematical model are derived based on existing literatures. The graph of output velocity against time is plotted and the transfer function is estimated by using System Identification Toolbox in MATLAB®. From the results, it is demonstrated that the motor exhibits second order system

Model-based and Model-Free Robot Control : A Review

Robot control is one of the key aspects of robotics research. Models are essential tools in robotics, such as the robot’s own body dynamics and kinematics models, actuator/motor models, and the models of external controllable objects. In this paper, we review the latest advances in model-based and model-free ap-proaches with a strong focus on robot control. Based on the designed search strategy, several prevailing control approaches are classified and discussed ac-cording to their control strategies. An insight into the gripper control is also explored. Then the research problems and applicability of the control methods are discussed by investigating their merits and demerits. Based on the discussion, we summarize the challenges and future research trends of robot control