The CLAPPER: A Dual-Drive Mobile Robot with Internal Correction of Dead-Reckoning Errors

This paper presents a newly developed approach for Differential Drive Mobile Robot (DDMR). The main goal is to provide a high dynamic system response in the joint space level, the low level control, as well as to enhance the DDMR localization. The proposed approach depends on a Linear Quadratic Regulator (LQR) for the low level control and an Adaptive LQR for the high level control. The investigated DDMR is considered highly nonlinear system due to uncertainty exhibited by the mobile robot incorporated with actuators nonlinearity. DDMR’s uncertainty leads to erroneous localization. An Extended Kalman Filter (EKF) -based approach with fusion sensors is used to enhance the robot degree of belief for its posture. Intensive simulation results obtained from the developed uncertain model and the proposed approach have shown very good dynamic performance on the low level control and very good convergence to the desired posture of the mobile robot path with the presence of robot uncertainty.

This paper proposes the design of a neural network controller based on a sample controller for controlling the trajectory-tracking motion of a differential drive mobile robot (DDMR). Firstly, the trajectory tracking model for DDMR is established based on position error. Next, a perceptron neural network is designed with three hidden layers to control the trajectory tracking of DDMR. The backpropagation algorithm is used to train the neural network with training data obtained from the PID controller with time-varying parameters. The authors have developed this approach and experimentally verified it with minor tracking errors. The neural network's weight matrix (W) and bias vector (b) are updated in real-time, providing an advantage over other methods. The effectiveness of the proposed controller is demonstrated by the DDMR's NURBS trajectory tracking error, which does not exceed 2.17 cm, and the DDMR's motion error, with linear and angular velocities not exceeding 0.004 m/s and 0.0007 rad/s, respectively. The proposed controller can supplement traditional controllers in controlling the trajectory of autonomous mobile robots, thereby improving the ability to generate local trajectories to avoid dynamic obstacles by the neural network

Odometry is the most widely used method for determining the momentary position of a mobile robot. This paper introduces practical methods for measuring and reducing odometry errors that are caused by the two dominant error sources in differential-drive mobile robots: 1) uncertainty about the effective wheelbase; and 2) unequal wheel diameters. These errors stay almost constant over prolonged periods of time. Performing an occasional calibration as proposed here will increase the odometric accuracy of the robot and reduce operation cost because an accurate mobile robot requires fewer absolute positioning updates. Many manufacturers or end-users calibrate their robots, usually in a time-consuming and nonsystematic trial and error approach. By contrast, the method described in this paper is systematic, provides near-optimal results, and it can be performed easily and without complicated equipment. Experimental results are presented that show a consistent improvement of at least one order of magnitude in odometric accuracy (with respect to systematic errors) for a mobile robot calibrated with our method.

Control of differential-drive mobile robots for soft object deformation

In maze maneuvering, it is needed for a mobile robot to feasibly plan the shortest path from its initial posture to the desired destination in a given environment. To achieve that, the mobile robot is combined with multiple distance sensors to assist the navigation while avoiding obstructing obstacles and following the shortest path toward the target. Additionally, a vision sensor is used to detect and track colored objects. A new algorithm is proposed based on different type of utilized sensors to aid the maneuvering of differential drive mobile robot in an unknown environment. In the proposed algorithm, the robot has the ability to traverse surrounding hindrances and seek for a particular object based on its color. Six infrared sensors are used to detect any located obstacles and one color detection sensor is used to locate the colored object. The Mobile Robotics Simulation Toolbox in Matlab is used to test the proposed algorithm. Three different scenarios are studied to prove the efficiency of the proposed algorithm. The simulation results demonstrate that the mobile robot has successfully accomplished the tracking and locating of a colored object without collision with hurdles.

This document presents the conceptual idea of developing a nonholonomic mobile robot which is the differential drive mobile robot made up of two motor wheels and one castor wheels. The kinematics and dynamics model equation of the mobile robot has been developed using graphical analytical method and Lagrange approach. This modelling equation is then verified on the Gazebo environment to understand their behavior compared to the actual one. Furthermore, the model is interpreted with fuzzy logic control to control the velocity and direction of the mobile robot in rapid changing environment. Mamdani fuzzy logic is chosen to improve the steady state error values in terms of position and orientation errors by controlling the angular velocity and angular orientation of the mobile robot. Simulation has been done in ROS and Gazebo with the rectangular path and triangular path, and it had shown that the robot model is able to follow these desired paths with very minimal error which is close to 0 even though there is some sharp angle turning.

In this paper, we describe the development of a nonlinear exponential control law for a multi-degree of freedom (MDOF) wheeled mobile robot. These vehicles have many advantages over the more classical 2-DOF mobile robots. Indeed, the MDOF vehicles can negotiate tight turns easily and are able to travel sideways. Nevertheless, the MDOF robots are over constrained systems, which are difficult to control. By this reason, MDOF robots are limited to simple applications, like following a guide-wire. In order to obtain an autonomous behavior, we propose the use of a nonlinear control law, which exponentially leads the robot to a desired position with a given orientation. A new set of error variables is defined in order to propose the control vector. This control vector makes the final configuration globally exponentially stable. Results of simulation, which illustrate the effectiveness of the control, are also included.

Accurate and reliable localization is a base requirement for a mobile robot. Landmark localization is a very common technique to do this. The arrangement of landmarks is important for reliable and accurate estimation of localization data. In this paper the optimized arrangement of artificial landmarks is discussed and a solution for building an accurate and reliable localization system based on combining artificial and natural landmarks is presented. Landmark placement is done by definition and optimization of a confidence level describing the expected localization estimation error. Areas in the environment which are ill conditioned by natural landmarks are furnished with additional artificial landmarks. The proposed system has been tested in a simulation and with a mobile robot in a real office environment.

Multi-degree-of-freedom (MDOF) vehicles have many potential advantages over conventional (i.e., 2-DOF) vehicles. For example, MDOF vehicles can travel sideways and they can negotiate tight turns more easily. In addition, some MDOF designs provide better payload capability, better traction, and improved static and dynamic stability. However, MDOF vehicles with more than three degrees-of-freedom are difficult to control because of their overconstrained nature. These difficulties translate into severe wheel slippage or jerky motion under certain driving conditions. In the past, these problems limited the use of MDOF vehicles to applications where the vehicle would follow a guide-wire, which would correct wheel slippage and control errors. By contrast, autonomous or semi-autonomous mobile robots usually rely on dead-reckoning between periodic absolute position updates and their performance is diminished by excessive wheel slippage. This paper introduces a new concept in the kinematic design of MDOF vehicles. This concept is based on the provision of a compliant linkage between drive wheels or drive axles. Simulation results indicate that compliant linkage allows to overcome the control problems found in conventional MDOF vehicles and reduces the amount of wheel slippage to the same level (or less) than the amount of slippage found on a comparable 2-DOF vehicle.

Tracking the pose of a robot has been gaining importance in the field of Robotics, e.g., paving the way for robot navigation. In recent years, monocular visual–inertial odometry (VIO) is widely used to do the pose estimation due to its good performance and low cost. However, VIO cannot estimate the scale or orientation accurately when robots move along straight lines or circular arcs on the ground. To address the problem, in this paper we take the wheel encoder into account, which can provide us with stable translation information as well as small accumulated errors and momentary slippage errors. By jointly considering the kinematic constraints and the planar moving features, an odometry algorithm tightly coupled with monocular camera, IMU, and wheel encoder is proposed to get robust and accurate pose sensing for mobile robots, which mainly contains three steps. First, we present the wheel encoder preintegration theory and noise propagation formula based on the kinematic mobile robot model, which is the basis of accurate estimation in backend optimization. Second, we adopt a robust initialization method to obtain good initial values of gyroscope bias and visual scale in reality, by making full use of the camera, IMU and wheel encoder measurements. Third, we bound the high computation complexity with a marginalization strategy that conditionally eliminates unnecessary measurements in the sliding window. We implement a prototype and several extensive experiments showing that our system can achieve robust and accurate pose estimation, in terms of the scale, orientation and location, compared with the state-of-the-art.

An approach to the design and control of a four-degree-of-freedom (4DOF) vehicle is presented. Existing 4DOF vehicles are difficult to control because of their overconstrained nature. These difficulties translate into severe wheel slippage or jerky motion under certain driving conditions. The author's approach overcomes these difficulties by introducing compliant linkage in the control and kinematic design of 4DOF mobile robots. Compliant linkage provides compliance between the drive wheels or drive axles of a vehicle in order to accommodate control errors which would otherwise cause wheel slippage. A three-level control system comprising of a wheel-level, a vehicle-level, and a trajectory-level controller is described. Experimental results are presented and show that control errors are effectively absorbed by the compliant linkage, resulting in smooth and precise motion.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Autonomous motion of mobile robots is an open problem in robotics. Challenges in this regard involve the proper interpretation of the information coming from the sensors, and the adequate motion of the robot based on that information to reach a goal without collisions. In this work, we propose a framework that smoothly drives a mobile robot through a collision-free trajectory. The generation of trajectories is based on motion planning using Artificial Potential Fields and the sensed depth information from the environment. The generated path is then followed by an iterative closed-loop feedback controller based on polar coordinates which is guided by the potential field. With this framework the robot can autonomously move to a desired goal avoiding obstacles online. The framework continuously plans its trajectory, being able to avoid obstacles online. Results were obtained using a dynamic simulator and a differential-drive mobile robot that uses an onboard Lidar.

In this paper, a new method on simultaneous calibration of odometry and camera extrinsic for a two-wheel differential driven mobile robot equipped with a fixed pinhole camera, is proposed. By observing several vision landmarks in the environment and recording the measurements from both encoders and camera during an arbitrary robot motion, the calibration could be automatically done. Neither a special trajectory need to be followed, nor other devices are used during the calibration process. Compared with other current solutions, non-holonomic constraint of the mobile robot kinematics is utilized to estimate the wheel axle and simplify the calibration problem. Experiment is conducted to validate the method.

The usage areas of mobile robots continue to increase due to the wide variety of advantages they provide. In particular, differential drive mobile robots (DDMR) have been widely preferred in the last decade due to the benefits they offer in current studies and the potential they promise. Before using them in advanced studies, it is quite useful in many ways to conduct basic kinematic investigation that can provide key information about these robots. This not only saves time and money, but also directly affects the accuracy of these studies. In this study, kinematic analysis of a small DDMR driven by servo motors was carried out with basic locomotion tests and system identification. The mobility of the robot along the predefined and geometrically expressible routes was examined and its kinematic limits were tried to be determined. Through the system identification tests, the consistency of the robot's response to the input commands was investigated and the possible causes of errors were discussed. Moreover, the behavior of the robot was modeled and compared with parametric ARX (autoregressive with external input) model due to its simplicity and efficiency. As a result of the motion tests, it has been observed that the robot has some limitations in rotational movements. With the system diagnostic tests, it has been determined that the two main wheels of the robot produce different outputs to the system inputs.

In this paper, we adopt a control-theoretic approach for navigation of a differential drive mobile robot (DDMR) while offloading vision data to be processed in a scalable cluster of fog computing nodes. The approach comprises developing a linear parameter varying (LPV) framework for modeling and a linear matrix inequality (LMI) based control design for navigation of DDMR and auto-scaling compute nodes in fog cluster independently. We validate the developed theory for mobile robot navigation in the application environment with service time guarantees in processing offloaded vision data for object detection.