39 research outputs found
MS
thesisIn this research, a computerized motion planning and control system for multiple robots is presented. Medium scale wheeled mobile robot couriers move wireless antennas within a semicontrolled environment. The systems described in this work are integrated as components within Mobile Emulab, a wireless research testbed. This testbed is publicly available to users remotely via the Internet. Experimenters use a computer interface to specify desired paths and configurations for multiple robots. The robot control and coordination system autonomously creates complex movements and behaviors from high level instructions. Multiple trajectory types may be created by Mobile Emulab. Baseline paths are comprised of line segments connecting waypoints, which require robots to stop and pivot between each segment. Filleted circular arcs between line segments allow constant motion trajectories. To avoid curvature discontinuities inherent in line-arc segmented paths, higher order continuous polynomial spirals and splines are constructed in place of the constant radius arcs. Polar form nonlinear state feedback controllers executing on a computer system connected to the robots over a wireless network accomplish posture stabilization, path following and trajectory tracking control. State feedback is provided by an overhead camera based visual localization system integrated into the testbed. Kinematic control is used to generate velocity commands sent to wheel velocity servo loop controllers built into the robots. Obstacle avoidance in Mobile Emulab is accomplished through visibility graph methods. The Virtualized Phase Portrait Method is presented as an alternative. A virtual velocity field overlay is created from workspace obstacle zone data. Global stability to a single equilibrium point, with local instability in proximity to obstacle regions is designed into this system
Research on a semiautonomous mobile robot for loosely structured environments focused on transporting mail trolleys
In this thesis is presented a novel approach to model, control, and planning the motion of
a nonholonomic wheeled mobile robot that applies stable pushes and pulls to a
nonholonomic cart (York mail trolley) in a loosely structured environment. The method is
based on grasping and ungrasping the nonholonomic cart, as a result, the robot changes its
kinematics properties. In consequence, two robot configurations are produced by the task
of grasping and ungrasping the load, they are: the single-robot configuration and the
robot-trolley configuration. Furthermore, in order to comply with the general planar
motion law of rigid bodies and the kinematic constraints imposed by the robot wheels for
each configuration, the robot has been provided with two motorized steerable wheels in
order to have a flexible platform able to adapt to these restrictions. [Continues.
Collision-free path planning for robots using B-splines and simulated annealing
This thesis describes a technique to obtain an optimal collision-free path for an automated guided vehicle (AGV) and/or robot in two and three dimensions by synthesizing a B-spline curve under geometric and intrinsic constraints. The problem is formulated as a combinatorial optimization problem and solved by using simulated annealing. A two-link planar manipulator is included to show that the B-spline curve can also be synthesized by adding kinematic characteristics of the robot. A cost function, which includes obstacle proximity, excessive arc length, uneven parametric distribution and, possibly, link proximity costs, is developed for the simulated annealing algorithm. Three possible cases for the orientation of the moving object are explored: (a) fixed orientation, (b) orientation as another independent variable, and (c) orientation given by the slope of the curve. To demonstrate the robustness of the technique, several examples are presented. Objects are modeled as ellipsoid type shapes. The procedure to obtain the describing parameters of the ellipsoid is also presented
Secured force guidance of an omnidirectional non-holonomic platform
For robots to operate in real life settings, they must be able to physically interact with the environment, and for instance be able to react to force-guidance interactions. However, only a few research projects have addressed such capabilities, developing prototypes that have to be pushed from their handle bars. AZIMUT-3 is a novel omnidirectional non-holonomic mobile robot developed at IntRoLab (Intelligent, Interactive and Interdisciplinary Robot Lab, Université de Sherbrooke) with force-controlled active steering. This results in a horizontal suspension effect for which the mechanical impedance of the steering actuators can be controlled. This makes the platform ideal for developing physical guidance algorithms. One such algorithm is secured shared-control, making the platform go in the direction of the user pushing the robot while still making it move safely by avoiding obstacles. Such capability is somewhat novel in the field, and the objective is to provide safe navigation with maximum control to the user. This Master's thesis has two important contributions: an algorithm to estimate the applied efforts on AZIMUT-3 from torque measurements on its wheels; an algorithm to use these efforts with obstacle detection using laser range finder data to implement a safe, shared-control approach. Experimental results using the real platform demonstrate feasibility and safe control of the system, with performances similar to using a six degrees of freedom force sensor but at lower cost and with a broader area for shared control. Our implementation also resulted in coupling the simulation environment Webots with the ROS (Robot Operating System) library from Willow Garage, to help develop our approach in simulation before using AZIMUT-3. Overall, our work is the first in demonstrating how it is possible to naturally interact by physically moving or positioning a mobile platform in real life settings, a capability which could be useful for instance in the design of powered shopping carts or active walkers
Underwater Vehicles
For the latest twenty to thirty years, a significant number of AUVs has been created for the solving of wide spectrum of scientific and applied tasks of ocean development and research. For the short time period the AUVs have shown the efficiency at performance of complex search and inspection works and opened a number of new important applications. Initially the information about AUVs had mainly review-advertising character but now more attention is paid to practical achievements, problems and systems technologies. AUVs are losing their prototype status and have become a fully operational, reliable and effective tool and modern multi-purpose AUVs represent the new class of underwater robotic objects with inherent tasks and practical applications, particular features of technology, systems structure and functional properties
Recommended from our members
Motion control of unmanned ground vehicle using artificial intelligence
The aim of this thesis is to solve two problems: the. trajectory tracking and navigation, for controlling the motion of unmanned ground vehicles (UGV). Such vehicles are usually used in industry for assisting automated production process or delivery services to improve and enhance the quality and efficiency.
With regard to the trajectory tracking problem, the main task is to design a new method that is capable of minimising trajectory-tracking errors in UGV. To achieve this, a comprehensive mathematical model needs to be established that contains kinematic and dynamic characteristics beside actuators. In addition, different trajectories need to be generated and applied individually as a reference input, i.e. continuous gradient trajectories such as linear, circular and lemniscuses or a non-continuous gradient trajectory such as a square trajectory. The design method is based on a novel fractional order proportional integral derivative (FOPID) control strategy, which is proposed to control the movement of UGV to track given trajectories. Two FOPID controllers are required in this design. The first FOPID is constructed in order to control the orientation of UGV. The second FOPID controller is to control the speed of UGV. The particle swarm optimization (PSO) algorithm is used to obtain the optimal parameters for both controllers. The significance of the proposed method is that an observable improvement has been achieved in terms of minimising trajectory-tracking errors and reducing control efforts, especially in continuous gradient trajectories. The stability of the proposed controllers is investigated based upon Nyquist stability criterion. Moreover, the robustness of the controllers is examined in the presence of disturbances to demonstrate the effectiveness of the controllers under certain harsh conditions. The influence from external disturbances has been represented by square pulses and sinusoidal waves. The drawback of this method, however, a highly trajectory tracking error is observed in non-continuous gradient trajectories due to the sharpness of the rotation at the corners of a square trajectory.
To overcome this drawback, a new controller, abbreviated as (NN-FOPID), has been proposed based on a combination of neural networks and the FOPID. The purpose is to minimise the trajectory tracking error of non-continuous trajectories, in particular. The Levenberg-Marquardt (LM) algorithm is used to train the NN-FOPID controller. The neural networks’ cognitive capacities have made the system adaptable to respond effectively to the variants in trajectories. The obtained results by using NN-FOPID have shown a significant improvement of reducing errors of trajectory tracking and increasing control efforts over the results by FOPID.
The other task is to solve the navigation problem of UGV in static and dynamic environments. This can be conducted by firstly constructing workspace environments that contain multiple dynamic and static obstacles. The dynamic obstructing obstacles can move in different velocities. The static obstacles can be randomly positioned in the workspace and all obstacles are allowed to have different sizes and shapes. Secondly, a UGV can be placed in any initial posture on the condition that it has to reach a given destination within the boundaries of the workspace. Thirdly, a method based on fuzzy inference systems (FIS) is proposed to control the motion of the UGV. The design of FIS is based on fuzzification, inference engine and defuzzification processes. The navigation task is divided into obstacle avoidance and target reaching tasks. Consequently, two individual FIS controllers are required to drive the actuators of the UGV, one is to avoid obstacles and the other is to reach a target. Both FIS controllers are combined through a switching mechanism to select the obstacle avoidance FIS controller if there is an obstacle, otherwise choosing reaching target FIS. The simulation results have confirmed the effectiveness of the proposed design in terms of obtaining optimal paths with shortest elapsed time.
Similarly, a new method is proposed based on an adaptive neurofuzzy inference system (ANFIS) to guide the UGV in unstructured environments. This method combines the advantages of adaptive leaning and inference fuzzy system. The simulation results have demonstrated adequate achievements in terms of obtaining shortest and feasible paths whilst avoiding static obstructing obstacles and hence reaching the specified targets speedily.
Finally, a UGV is constructed to investigate the overall performance of the proposed FIS controllers practically. The architecture of the UGV consists of three ultrasonic sensors, a magnetic compass and two quadratic decoders that they are interfaced with an Arduino microcontroller to read the sensory information. The Arduino, who acts as a slave microcontroller is serially connected with a master Raspberry Pi microcontroller. Raspberry Pi and Arduino communicate with each other based on a proposed hierarchical algorithm. Three case studies are introduced to demonstrate the effectiveness and the validation of the proposed FIS controllers and the UGV’s platform in real-time
Recent Advances in Multi Robot Systems
To design a team of robots which is able to perform given tasks is a great concern of many members of robotics community. There are many problems left to be solved in order to have the fully functional robot team. Robotics community is trying hard to solve such problems (navigation, task allocation, communication, adaptation, control, ...). This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field. It is focused on the challenging issues of team architectures, vehicle learning and adaptation, heterogeneous group control and cooperation, task selection, dynamic autonomy, mixed initiative, and human and robot team interaction. The book consists of 16 chapters introducing both basic research and advanced developments. Topics covered include kinematics, dynamic analysis, accuracy, optimization design, modelling, simulation and control of multi robot systems
Proceedings of the NASA Conference on Space Telerobotics, volume 3
The theme of the Conference was man-machine collaboration in space. The Conference provided a forum for researchers and engineers to exchange ideas on the research and development required for application of telerobotics technology to the space systems planned for the 1990s and beyond. The Conference: (1) provided a view of current NASA telerobotic research and development; (2) stimulated technical exchange on man-machine systems, manipulator control, machine sensing, machine intelligence, concurrent computation, and system architectures; and (3) identified important unsolved problems of current interest which can be dealt with by future research
INTELLIGENT VISION-BASED NAVIGATION SYSTEM
This thesis presents a complete vision-based navigation system that can plan and
follow an obstacle-avoiding path to a desired destination on the basis of an internal map
updated with information gathered from its visual sensor.
For vision-based self-localization, the system uses new floor-edges-specific filters
for detecting floor edges and their pose, a new algorithm for determining the orientation of
the robot, and a new procedure for selecting the initial positions in the self-localization
procedure. Self-localization is based on matching visually detected features with those
stored in a prior map.
For planning, the system demonstrates for the first time a real-world application of
the neural-resistive grid method to robot navigation. The neural-resistive grid is modified
with a new connectivity scheme that allows the representation of the collision-free space of
a robot with finite dimensions via divergent connections between the spatial memory layer
and the neuro-resistive grid layer.
A new control system is proposed. It uses a Smith Predictor architecture that has
been modified for navigation applications and for intermittent delayed feedback typical of
artificial vision. A receding horizon control strategy is implemented using Normalised
Radial Basis Function nets as path encoders, to ensure continuous motion during the delay
between measurements.
The system is tested in a simplified environment where an obstacle placed
anywhere is detected visually and is integrated in the path planning process.
The results show the validity of the control concept and the crucial importance of a
robust vision-based self-localization process