Abstract: Mobile robotics is gaining an increasingly important
role in modern society. Several potentially dangerous or laborious
tasks for human are assigned to mobile robots, which are increasingly
capable. Many of these tasks need to be performed within a specified
period, i.e, meet a deadline. Missing the deadline can result in
financial and/or material losses. Mechanisms for predicting the
missing of deadlines are fundamental because corrective actions can
be taken to avoid or minimize the losses resulting from missing the
deadline. In this work we propose a simple but reliable deadline
missing prediction mechanism for mobile robots through the use of
historical data and we use the Pioneer 3-DX robot for experiments
and simulations, one of the most popular robots in academia.
Abstract: We present in this work the performances of a mobile omnidirectional robot through evaluating its management of the redundancy of actuation. Thus we come to the predictive control implemented.
The distribution of the wringer on the robot actions, through the inverse pseudo of Moore-Penrose, corresponds to a « geometric ›› distribution of efforts. We will show that the load on vehicle wheels would not be equi-distributed in terms of wheels configuration and of robot movement.
Thus, the threshold of sliding is not the same for the three wheels of the vehicle. We suggest exploiting the redundancy of actuation to reduce the risk of wheels sliding and to ameliorate, thereby, its accuracy of displacement. This kind of approach was the subject of study for the legged robots.
Abstract: Methods for measuring or estimating ground shape by a laser range finder and a vision sensor (Exteroceptive sensors) have critical weaknesses in terms that these methods need a prior database built to distinguish acquired data as unique surface conditions for driving. Also, ground information by Exteroceptive sensors does not reflect the deflection of ground surface caused by the movement of UGVs. Therefore, this paper proposes a method of recognizing exact and precise ground shape using an Inertial Measurement Unit (IMU) as a proprioceptive sensor. In this paper, firstly this method recognizes the attitude of a robot in real-time using IMU and compensates attitude data of a robot with angle errors through analysis of vehicle dynamics. This method is verified by outdoor driving experiments of a real mobile robot.
Abstract: In this paper, we present a localization of a mobile robot with localization modules which have two ceiling-view cameras in indoor environments. We propose two kinds of localization method. The one is the localization in the local space; we use the line feature and the corner feature between the ceiling and wall. The other is the localization in the large space; we use the natural features such as bulbs, structures on the ceiling. These methods are installed on the embedded module able to mount on the robot. The embedded module has two cameras to be able to localize in both the local space and the large spaces. The experiment is practiced in our indoor test-bed and a government office. The proposed method is proved by the experimental results.
Abstract: This paper presents a predictive model of sensor readings for mobile robot. The model predicts sensor readings for given time horizon based on current sensor readings and velocities of wheels assumed for this horizon. Similar models for such anticipation have been proposed in the literature. The novelty of the model presented in the paper comes from the fact that its structure takes into account physical phenomena and is not just a black box, for example a neural network. From this point of view it may be regarded as a semi-phenomenological model. The model is developed for the Khepera robot, but after certain modifications, it may be applied for any robot with distance sensors such as infrared or ultrasonic sensors.
Abstract: This paper presents the novel Rao-Blackwellised
particle filter (RBPF) for mobile robot simultaneous localization and
mapping (SLAM) using monocular vision. The particle filter is
combined with unscented Kalman filter (UKF) to extending the path
posterior by sampling new poses that integrate the current observation
which drastically reduces the uncertainty about the robot pose. The
landmark position estimation and update is also implemented through
UKF. Furthermore, the number of resampling steps is determined
adaptively, which seriously reduces the particle depletion problem,
and introducing the evolution strategies (ES) for avoiding particle
impoverishment. The 3D natural point landmarks are structured with
matching Scale Invariant Feature Transform (SIFT) feature pairs. The
matching for multi-dimension SIFT features is implemented with a
KD-Tree in the time cost of O(log2
N). Experiment results on real robot
in our indoor environment show the advantages of our methods over
previous approaches.
Abstract: This paper deals with motion planning of multiple
mobile robots. Mobile robots working together to achieve several
objectives have many advantages over single robot system. However,
the planning and coordination between the mobile robots is
extremely difficult. In the present investigation rule-based and rulebased-
neuro-fuzzy techniques are analyzed for multiple mobile
robots navigation in an unknown or partially known environment.
The final aims of the robots are to reach some pre-defined goals.
Based upon a reference motion, direction; distances between the
robots and obstacles; and distances between the robots and targets;
different types of rules are taken heuristically and refined later to find
the steering angle. The control system combines a repelling influence
related to the distance between robots and nearby obstacles and with
an attracting influence between the robots and targets. Then a hybrid
rule-based-neuro-fuzzy technique is analysed to find the steering
angle of the robots. Simulation results show that the proposed rulebased-
neuro-fuzzy technique can improve navigation performance in
complex and unknown environments compared to this simple rulebased
technique.
Abstract: This paper presents a new problem solving approach
that is able to generate optimal policy solution for finite-state
stochastic sequential decision-making problems with high data
efficiency. The proposed algorithm iteratively builds and improves
an approximate Markov Decision Process (MDP) model along with
cost-to-go value approximates by generating finite length trajectories
through the state-space. The approach creates a synergy between an
approximate evolving model and approximate cost-to-go values to
produce a sequence of improving policies finally converging to the
optimal policy through an intelligent and structured search of the
policy space. The approach modifies the policy update step of the
policy iteration so as to result in a speedy and stable convergence to
the optimal policy. We apply the algorithm to a non-holonomic
mobile robot control problem and compare its performance with
other Reinforcement Learning (RL) approaches, e.g., a) Q-learning,
b) Watkins Q(λ), c) SARSA(λ).
Abstract: In this work, a new approach is proposed to control
the manipulators for Humanoid robot. The kinematics of the
manipulators in terms of joint positions, velocity, acceleration and
torque of each joint is computed using the Denavit Hardenberg (D-H)
notations. These variables are used to design the manipulator control
system, which has been proposed in this work. In view of supporting
the development of a controller, a simulation of the manipulator is
designed for Humanoid robot. This simulation is developed through
the use of the Virtual Reality Toolbox and Simulink in Matlab. The
Virtual Reality Toolbox in Matlab provides the interfacing and
controls to an environment which is developed based on the Virtual
Reality Modeling Language (VRML). Chains of bones were used to
represent the robot.
Abstract: In this article we address the problem of mobile robot formation control. Indeed, the most work, in this domain, have studied extensively classical control for keeping a formation of mobile robots. In this work, we design an FLC (Fuzzy logic Controller) controller for separation and bearing control (SBC). Indeed, the leader mobile robot is controlled to follow an arbitrary reference path, and the follower mobile robot use the FSBC (Fuzzy Separation and Bearing Control) to keep constant relative distance and constant angle to the leader robot. The efficiency and simplicity of this control law has been proven by simulation on different situation.
Abstract: In this paper, an automatic guided mobile robot using a new magnetic position meter is described. In order to measure the lateral position of a mobile robot, a new magnetic position meter is developed. The magnetic position meter can detect the position of a magnetic wire on the center of road. A mobile robot in designed with a sensing system, a steering system and a driving system. The designed mobile robot is tested to verify the performance of automatic guidance.
Abstract: This paper presents an optimized algorithm for robot localization which increases the correctness and accuracy of the estimating position of mobile robot to more than 150% of the past methods [1] in the uncertain and noisy environment. In this method the odometry and vision sensors are combined by an adapted well-known discrete kalman filter [2]. This technique also decreased the computation process of the algorithm by DKF simple implementation. The experimental trial of the algorithm is performed on the robocup middle size soccer robot; the system can be used in more general environments.
Abstract: In this paper, the trajectory tracking problem for carlike mobile robots have been studied. The system comprises of a leader and a follower robot. The purpose is to control the follower so that the leader-s trajectory is tracked with arbitrary desired clearance to avoid inter-robot collision while navigating in a terrain with obstacles. A set of artificial potential field functions is proposed using the Direct Method of Lyapunov for the avoidance of obstacles and attraction to their designated targets. Simulation results prove the efficiency of our control technique.
Abstract: This paper presents a sensing system for 3D sensing
and mapping by a tracked mobile robot with an arm-type sensor
movable unit and a laser range finder (LRF). The arm-type sensor
movable unit is mounted on the robot and the LRF is installed at the
end of the unit. This system enables the sensor to change position and
orientation so that it avoids occlusions according to terrain by this
mechanism. This sensing system is also able to change the height of
the LRF by keeping its orientation flat for efficient sensing. In this kind
of mapping, it may be difficult for moving robot to apply mapping
algorithms such as the iterative closest point (ICP) because sets of the
2D data at each sensor height may be distant in a common surface. In
order for this kind of mapping, the authors therefore applied
interpolation to generate plausible model data for ICP. The results of
several experiments provided validity of these kinds of sensing and
mapping in this sensing system.
Abstract: Mobile robots are used in a large field of scenarios,
like exploring contaminated areas, repairing oil rigs under water,
finding survivors in collapsed buildings, etc. Currently, there is no
unified intuitive user interface (UI) to control such complex mobile
robots. As a consequence, some scenarios are done without the
exploitation of experience and intuition of human teleoperators.
A novel framework has been developed to embed a flexible and
modular UI into a complete 3-D virtual reality simulation system.
This new approach wants to access maximum benefits of human
operators. Sensor information received from the robot is prepared for
an intuitive visualization. Virtual reality metaphors support the
operator in his decisions. These metaphors are integrated into a real
time stereo video stream. This approach is not restricted to any
specific type of mobile robot and allows for the operation of different
robot types with a consistent concept and user interface.
Abstract: Markov games can be effectively used to design
controllers for nonlinear systems. The paper presents two novel
controller design algorithms by incorporating ideas from gametheory
literature that address safety and consistency issues of the
'learned' control strategy. A more widely used approach for
controller design is the H∞ optimal control, which suffers from high
computational demand and at times, may be infeasible. We generate
an optimal control policy for the agent (controller) via a simple
Linear Program enabling the controller to learn about the unknown
environment. The controller is facing an unknown environment and
in our formulation this environment corresponds to the behavior rules
of the noise modeled as the opponent. Proposed approaches aim to
achieve 'safe-consistent' and 'safe-universally consistent' controller
behavior by hybridizing 'min-max', 'fictitious play' and 'cautious
fictitious play' approaches drawn from game theory. We empirically
evaluate the approaches on a simulated Inverted Pendulum swing-up
task and compare its performance against standard Q learning.
Abstract: This paper presents an Extended Kaman Filter
implementation of a single-camera Visual Simultaneous Localization
and Mapping algorithm, a novel algorithm for simultaneous
localization and mapping problem widely studied in mobile robotics
field. The algorithm is vision and odometry-based, The odometry
data is incremental, and therefore it will accumulate error over time,
since the robot may slip or may be lifted, consequently if the
odometry is used alone we can not accurately estimate the robot
position, in this paper we show that a combination of odometry and
visual landmark via the extended Kalman filter can improve the robot
position estimate. We use a Pioneer II robot and motorized pan tilt
camera models to implement the algorithm.
Abstract: In this paper, we study the formation control problem
for car-like mobile robots. A team of nonholonomic mobile robots navigate in a terrain with obstacles, while maintaining a desired
formation, using a leader-following strategy. A set of artificial potential field functions is proposed using the direct Lyapunov
method for the avoidance of obstacles and attraction to their designated targets. The effectiveness of the proposed control laws to verify the feasibility of the model is demonstrated through computer simulations
Abstract: In this work a software simulation model has been
proposed for two driven wheels mobile robot path planning; that can
navigate in dynamic environment with static distributed obstacles.
The work involves utilizing Bezier curve method in a proposed N
order matrix form; for engineering the mobile robot path. The Bezier
curve drawbacks in this field have been diagnosed. Two directions:
Up and Right function has been proposed; Probability Recursive
Function (PRF) to overcome those drawbacks.
PRF functionality has been developed through a proposed;
obstacle detection function, optimization function which has the
capability of prediction the optimum path without comparison
between all feasible paths, and N order Bezier curve function that
ensures the drawing of the obtained path.
The simulation results that have been taken showed; the mobile
robot travels successfully from starting point and reaching its goal
point. All obstacles that are located in its way have been avoided.
This navigation is being done successfully using the proposed PRF
techniques.
Abstract: This paper suggests a calibration method to reduce
errors occurring due to mobile robot sliding during location estimation
using the Dead-reckoning. Due to sliding of the mobile robot caused
between its wheels and the road surface while on free run, location
estimation can be erroneous. Sliding especially occurs during
cornering of mobile robot. Therefore, in order to reduce these frequent
sliding errors in cornering, we calibrated the mobile robot-s heading
values using a vision camera and templates of the ceiling.