Mobile Robot Pioneer Research Articles

LAMDA Control Approaches Applied to Trajectory Tracking for Mobile Robots

This paper presents the application of different approaches of intelligent controllers based on LAMDA (Learning Algorithm for Multivariate Data Analysis) to perform the trajectory tracking task of a mobile robot Pioneer 3-DX simulated in the Virtual Robot Experimentation Platform V-REP environment. The paper proposes the design of a cascade control strategy composed of two controllers, the external controller based on a feedback linearization for the robot kinematics and the internal controller applied to the robot dynamics where the LAMDA approaches are applied. In this work, it is considered an uncertain dynamic model. Thus, its identification and control are based on a reduced-order model for designing purposes. Finally, the dynamic controller is designed using different controllers based on LAMDA, such as LAMDA-PID, LAMDA-Sliding Mode Control, and Adaptive LAMDA. To perform a comparative analysis between them that includes a conventional PID, SMC, and Fuzzy-PID, different trajectories are examined to evaluate the methods. The obtained results are compared qualitatively and quantitatively to determine which proposal performs the most accurate control and presents the best results in terms of performance.

Autonomous Mobile Robot based on BehaviourBased Robotic using V-REP Simulator–Pioneer P3-DX Robot

This article describes the design and implementation of behavior-based robotic (BBR) algorithm on a wheeled mobile robot (WMR) Pioneer P3-DX in a maze exploration mission using V-REP simulator. This robot must trace and search for targets placed randomly on a labyrinth. After successfully meeting the objective, robot runs back to home position using the nearest path. Robot navigation system applies BBR algorithm to reach the target using behavior modules which work simultaneously to obtain the desired robot’s trajectory. The most fundamental behavior which is highly affordable to build on the robot system is a wall-following behavior. To make the robot could follow the wall in a safe, smooth and responsive condition, proportional-integral-derivative (PID) controller is applied. PID controller runs by utilizing the reading of sixteen proximity sensors carried on Pioneer P3-DX robot toward the expected wall distance while the robot is exploring the labyrinth. To ensure the designed system works properly, several tests were conducted, including BBR test and PID controller test. BBR test shows that the system can choose the shortest track when returning to home position. The PID controller test produces robot movement with maximum deviation and settling time for about 0.013 m and 30 seconds, respectively.

Enhancing Path Quality of Real-Time Path Planning Algorithms for Mobile Robots: A Sequential Linear Paths Approach

The traditional trade-off between execution speed and path quality has forced real-time robotic path planning algorithms to sacrifice path quality in order to execute in real-time. Producing a path planning algorithm that targets enhancing both, the path quality and swiftness is a challenging problem. However, this article proposes a novel path planning strategy that aims to break this traditional trade-off, by targeting both, increasing the swiftness, and enhancing the path quality represented by the path length and smoothness. The proposed strategy is based on the observation that most path planning algorithms waste the processing efforts in less critical areas of the map. Therefore, the proposed path planning strategy tends to focus on critical areas such as the areas around obstacles and areas around the goal point, and exhausts the processing power on these critical areas. This is done by neglecting all static obstacles that do not lie between the robot and the destination. For obstacles that intersect with the linear line from the robot to the destination, a basis traditional path planning algorithm such as A*, D* or the Probabilistic RoadMap (PRM) technique is only implemented around the obstacles in order to find a feasible path around each selected obstacle. This procedure would minimize the computational efforts compared to applying the basis algorithm on the whole map. Finally, the path quality is enhanced by finding any linear shortcuts between any two points in the path and fix these shortcuts as the final path from the starting point to the goal point. The proposed path planning strategy was tested on a P3-DX Pioneer mobile robot using a kinematic controller. The experimental results have proven that the path planning strategy was able to show a superior advantage over other path planning techniques in both aspects, computational time (reached up to 97.05% improvement) and path quality (reached up to 16.21% improvement for path length and 98.50% for smoothness).

A novel control structure for a pioneer mobile robot: simulation and practical implementation

This paper presents a new control structure for a non-holonomic robot. It consists of two controllers in series: a nonlinear trajectory tracking controller based on robot kinematics and a multivariable quasi-linear controller based on robot dynamics. The Lyapunov theory is used to obtain the nonlinear control law and a frequency domain design approach is employed to find the transfer functions of the quasi-linear controller. The stability and sensitivity analysis criteria are considered to increase the performance of the robot. The stabilisation time and overshoot are considerably reduced. The proposed controller is simple and insensitive to external disturbances. Simulation and real-time test results are used to evaluate the effectiveness of this new control structure.

A novel control structure for a pioneer mobile robot: simulation and practical implementation

This paper presents a new control structure for a non-holonomic robot. It consists of two controllers in series: a nonlinear trajectory tracking controller based on robot kinematics and a multivariable quasi-linear controller based on robot dynamics. The Lyapunov theory is used to obtain the nonlinear control law and a frequency domain design approach is employed to find the transfer functions of the quasi-linear controller. The stability and sensitivity analysis criteria are considered to increase the performance of the robot. The stabilisation time and overshoot are considerably reduced. The proposed controller is simple and insensitive to external disturbances. Simulation and real-time test results are used to evaluate the effectiveness of this new control structure.

Simultaneous localization, mapping, and path planning for unmanned vehicle using optimal control

Among the huge number of functionalities that are required for autonomous navigation, the most important are localization, mapping, and path planning. In this article, investigation of the path planning problem of unmanned ground vehicle is based on optimal control theory and simultaneous localization and mapping. A new approach of optimal simultaneous localization, mapping, and path planning is proposed. Our approach is mainly affected by vehicle’s kinematics and environment constraints. Simultaneous localization, mapping, and path planning algorithm requires two main stages. First, the simultaneous localization and mapping algorithm depends on the robust smooth variable structure filter estimate accurate positions of the unmanned ground vehicle. Then, an optimal path is planned using the aforementioned positions. The aim of the simultaneous localization, mapping, and path planning algorithm is to find an optimal path planning using the Shooting and Bellman methods which minimizes the final time of the unmanned ground vehicle path tracking. The simultaneous localization, mapping, and path planning algorithm has been approved in simulation, experiments, and including real data employing the mobile robot Pioneer [Formula: see text]. The obtained results using smooth variable structure filter–simultaneous localization and mapping positions and the Bellman approach show path generation improvements in terms of accuracy, smoothness, and continuity compared to extended Kalman filter–simultaneous localization and mapping positions.

Chance-constrained target tracking using sensors with bounded fan-shaped sensing regions

We present a robust target tracking algorithm for a mobile robot. It is assumed that a mobile robot carries a sensor with a fan-shaped field of view and finite sensing range. The goal of the proposed tracking algorithm is to minimize the probability of losing a target. If the distribution of the next position of a moving target is available as a Gaussian distribution from a motion prediction algorithm, the proposed algorithm can guarantee the tracking success probability. In addition, the proposed method minimizes the moving distance of the mobile robot based on the chosen bound on the tracking success probability. While the considered problem is a non-convex optimization problem, we derive a closed-form solution when the heading is fixed and develop a real-time algorithm for solving the considered target tracking problem. We also present a robust target tracking algorithm for aerial robots in 3D. The performance of the proposed method is evaluated extensively in simulation. The proposed algorithm has been successful applied in field experiments using Pioneer mobile robot with a Microsoft Kinect sensor for following a pedestrian.

Human interaction dynamics for its use in mobile robotics: Impedance control for leader-follower formation

A complete characterization of the behavior in human-robot interactions U+0028 HRI U+0029 includes both: the behavioral dynamics and the control laws that characterize how the behavior is regulated with the perception data. In this way, this work proposes a leader-follower coordinate control based on an impedance control that allows to establish a dynamic relation between social forces and motion error. For this, a scheme is presented to identify the impedance based on fictitious social forces, which are described by distance-based potential fields. As part of the validation procedure, we present an experimental comparison to select the better of two different fictitious force structures. The criteria are determined by two qualities: least impedance errors during the validation procedure and least parameter variance during the recursive estimation procedure. Finally, with the best fictitious force and its identified impedance, an impedance control is designed for a mobile robot Pioneer 3AT, which is programmed to follow a human in a structured scenario. According to results, and under the hypothesis that moving like humans will be acceptable by humans, it is believed that the proposed control improves the social acceptance of the robot for this kind of interaction.

Design and Evaluation of a Teleoperated Robotic 3-D Mapping System using an RGB-D Sensor

In this correspondence paper, we develop a teleoperated robotic 3-D mapping (TeRoM) system which enables efficient human-guided mapping of remote environments for realistic rendering and visualization. First, the hardware design of the TeRoM system is proposed which is based on a Pioneer mobile robot platform and a rotating RGB-D camera. A client/server architecture is developed to allow the data to be processed in a remote server, which makes it possible to implement 3-D mapping on robots with limited resources. Second, a 3-D map is created in real-time while an operator controls the robot and the pan-tilt unit remotely using a joystick. Then the map is converted into a mesh using the marching cubes algorithm and optimized to reduce the data volume. Finally the mesh is imported and rendered in a 3-D rendering engine for interactive and intuitive display. We evaluate the performance of the TeRoM system in terms of accuracy, processing speed, reliability, and manipulability.

Vision-Based Real-Time Positioning and Autonomous Navigation System Using Mobile Robot in Indoor Environments

Research toward unmanned mobile robot navigation has gained significant importance in the last decade due to its potential applications in the location-based services industry. The increase in construction of large space indoor buildings has made difficulty for humans to operate within such environments. In this study, a mobile robot's indoor navigation algorithm is developed with vision cameras. Using two monocular cameras (one looking forward and one looking downward), the developed algorithms make use of the salient features of the environments to estimate rotational and translational motions for real-time positioning of the mobile robot. At the same time, an algorithm based on artificial landmark recognition is developed. The artificial landmark is shaped arrow based signboards with different colors representing different paths. These algorithms are integrated into a designed framework for mobile robot real-time positioning and autonomous navigation. Experiments are performed to validate the designed system using the mobile robot PIONEER P3-AT. The developed algorithm was able to detect and extract artificial landmark information up to 3 m distance for the mobile robot guidance. Experiment results show an average error of 0.167 m deviation from the ideal path, signified the good ability and performance of the development autonomous navigation algorithm.

A ToF-Camera as a 3D Vision Sensor for Autonomous Mobile Robotics

The aim of this paper is to deploy a time-of-flight (ToF) based photonic mixer device (PMD) camera on an Autonomous Ground Vehicle (AGV) whose overall target is to traverse from one point to another in hazardous and hostile environments employing obstacle avoidance without human intervention. The hypothesized approach of applying a ToF Camera for an AGV is a suitable approach to autonomous robotics because, as the ToF camera can provide three-dimensional (3D) information at a low computational cost, it is utilized to extract information about obstacles after their calibration and ground testing and is mounted and integrated with the Pioneer mobile robot. The workspace is a two-dimensional (2D) world map which has been divided into a grid/cells, where the collision-free path defined by the graph search algorithm is a sequence of cells the AGV can traverse to reach the target. PMD depth data is used to populate traversable areas and obstacles by representing a grid/cells of suitable size. These camera data are converted into Cartesian coordinates for entry into a workspace grid map. A more optimal camera mounting angle is needed and adopted by analysing the camera's performance discrepancy, such as pixel detection, the detection rate and the maximum perceived distances, and infrared (IR) scattering with respect to the ground surface. This mounting angle is recommended to be half the vertical field-of-view (FoV) of the PMD camera. A series of still and moving tests are conducted on the AGV to verify correct sensor operations, which show that the postulated application of the ToF camera in the AGV is not straightforward. Later, to stabilize the moving PMD camera and to detect obstacles, a tracking feature detection algorithm and the scene flow technique are implemented to perform a real-time experiment.

Approximate Dynamic Programming in the Sensor-Based Navigation of the Wheeled Mobile Robot

The article presents a new approach to the sensor-based navigation of wheeled mobile robot Pioneer 2-DX in the unknown 2-D environment with static obstacles. The navigation task has been developed using a discrete hierarchical control system with a path planning layer and a tracking control layer designed using approximate dynamic programming algorithms. The navigator realises a behavioural control approach to the path planning process using the adaptive coordination of two simple behaviours: “goal-seeking” and “obstacle avoiding”. The main part of the navigator is the Action-Dependant Heuristic Dynamic Programming structure realised in a form of the actor and critic neural networks. To avoid the time consuming trial and error learning, additional proportional controllers generating signals that prompt the direction of the sub-optimal control law seeking process at the beginning of the NNs adaptation process are arranged in the navigator. The tracking control layer is composed of a PD controller, the Dual Heuristic Dynamic Programming algorithm and a supervisory term. It generates control signal for DC motors of the robot. The performance of the proposed discrete control system was verified by a series of experiments conducted using wheeled mobile robot Pioneer 2-DX equipped with one laser and eight ultrasonic range finders that provide object detection.

LRF-Based Human Following Robot by Tracking Legs

This paper presents a method to track and follow target person. A laser range finder (LRF) is used to measure highly accurate distance of objects with the range of 180 degrees. First of all, the erroneous data are excluded due to the error of LRF. Then all the raw sensor data are separated into many groups when the difference of the measuring distances of two adjacent laser points are beyond a limited value. For each group, the width is calculated, and it is considered as human legs if the defined conditions are satisfied. Finally, a real-time human following experiment with a SICK LRF and PIONEER mobile robot is done to validate our proposed method.

Discrete Globalised Dual Heuristic Dynamic Programming in Control of the Two-Wheeled Mobile Robot

Network-based control systems have been emerging technologies in the control of nonlinear systems over the past few years. This paper focuses on the implementation of the approximate dynamic programming algorithm in the network-based tracking control system of the two-wheeled mobile robot, Pioneer 2-DX. The proposed discrete tracking control system consists of the globalised dual heuristic dynamic programming algorithm, the PD controller, the supervisory term, and an additional control signal. The structure of the supervisory term derives from the stability analysis realised using the Lyapunov stability theorem. The globalised dual heuristic dynamic programming algorithm consists of two structures: the actor and the critic, realised in a form of neural networks. The actor generates the suboptimal control law, while the critic evaluates the realised control strategy by approximation of value function from the Bellman’s equation. The presented discrete tracking control system works online, the neural networks’ weights adaptation process is realised in every iteration step, and the neural networks preliminary learning procedure is not required. The performance of the proposed control system was verified by a series of computer simulations and experiments realised using the wheeled mobile robot Pioneer 2-DX.

Efficient plane detection in multilevel surface maps

An automatic system aimed at producing a compact tridimensional description of indoor environments using a mobile 3D laser scanner is described in this paper. The resulting description is made up of a Multi-Level Map (ML map) and a series of planar patches extracted from the map. We propose a novel plane detection algorithm, based on the efficient RANSAC algorithm, that operates directly over the data structures of an ML map and does not need to rely on the low level laser data cloud. The mobile 3D scanner is built from a Hokuyo laser range sensor attached to a 2DOF pan-tilt, which is installed on top of a 3DX Pioneer mobile robot. The 3D spatial information acquired by the laser sensor from different poses is used to build a large single map of the environment using the SLAM 6D library. Experimental results demonstrate that the system described is capable of efficiently building compact and accurate 3D representations of complex large indoor environments at multiple semantic levels.

Monocular Vision Based Mobile Robot 3D Map Building

A robust dense 3D feature map is built only with monocular vision and odometry. Monocular vision mounted on the robot front-end tracks the 3D natural landmarks, which are structured with matching Scale Invariant Feature Transform (SIFT) feature matching pairs. SIFT features are highly distinctive and invariant to image scaling, rotation, and change in 3D viewpoints. A fast SIFT feature matching algorithm is implemented with the KD-Tree based nearest search approach in the time cost of O(log2N), and matches with large error are eliminated by epipolar line restriction. A map building algorithm based on 3D spatial SIFT landmarks is designed and implemented. Experiment results on Pioneer mobile robot in a real indoor environment show the superior performance of our proposed method.

REAL-TIME SLIDING-MODE ADAPTIVE CONTROL OF A MOBILE PLATFORM WHEELED MOBILE ROBOT

This paper presents parameter identification and discrete-time adaptive sliding-mode controller applied to control the mobile platform Pioneer 3-DX wheeled mobile robot (WMR) with 5-DOF manipulator. The dynamic model for mobile robot with two driving wheels, time–varying mass and moment of inertia have been used in sliding-mode control. Two closed-loop, on-line parameter estimators have been used in order to achieve robustness against parameter uncertainties (robot mass and moment of inertia). Two sliding-mode adaptive controllers have been designed corresponding to angular and linear motion. This paper presents closed-loop, circular trajectory tracking real-time control for the mobile robot Pioneer 3-DX.

Dynamical model parameters identification of a wheeled mobile robot

Even though the commercial wheeled mobile robot Pioneer is very important to the scientific community, there is not much information available about its dynamical model. In this paper this model is deduced, including the effect of the free wheel. The result is a complex equation of motion that depends on several physical parameters. Some of them are provided by the manufacturer; others can be directly measured and the rest of them are identified applying the Least Square Method to the integral of the uncoupled motion equation for each degree of freedom. After few assumptions, the whole model is obtained.

Edge, Shade and Mixed Range Detection by Fuzzy Gaussian Filter for an Autonomous Robot

Extraction of edge, shade and mixed ranges are the first stage in the process of image understanding. This paper highlights the difficulties for a mobile robot to find out these features from a video stream of images transmitted from the robot. As the robot moves through the uneven lighting condition in its surrounding, the image quality differs from point to point. The paper proposes a recursive formulation of fuzzy Gaussian filter, by varying its parameters, i.e., window size and σ value to get the dominant features of the online video images. The method has been implemented on a client machine for the video images transmitted by the mobile robot Pioneer DX-2. The result shows that the method is suitable for the online images taken in varying lighting conditions.