2,269 research outputs found
Dynamic Control of Mobile Multirobot Systems: The Cluster Space Formulation
The formation control technique called cluster space control promotes simplified specification and monitoring of the motion of mobile multirobot systems of limited size. Previous paper has established the conceptual foundation of this approach and has experimentally verified and validated its use for various systems implementing kinematic controllers. In this paper, we briefly review the definition of the cluster space framework and introduce a new cluster space dynamic model. This model represents the dynamics of the formation as a whole as a function of the dynamics of the member robots. Given this model, generalized cluster space forces can be applied to the formation, and a Jacobian transpose controller can be implemented to transform cluster space compensation forces into robot-level forces to be applied to the robots in the formation. Then, a nonlinear model-based partition controller is proposed. This controller cancels out the formation dynamics and effectively decouples the cluster space variables. Computer simulations and experimental results using three autonomous surface vessels and four land rovers show the effectiveness of the approach. Finally, sensitivity to errors in the estimation of cluster model parameters is analyzed.Fil: Mas, Ignacio Agustin. Instituto Tecnológico de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Kitts, Christopher. Santa Clara University; Estados Unido
Performance improvement in VSLAM using stabilized feature points
Simultaneous localization and mapping (SLAM) is the main prerequisite for the autonomy of a mobile robot. In this paper, we present a novel method that enhances the consistency of the map using stabilized corner features. The proposed method integrates template matching based video stabilization and Harris corner detector. Extracting Harris corner features from stabilized video consistently increases the accuracy of the localization. Data coming from a video camera and odometry are fused in an Extended Kalman Filter (EKF) to determine the pose of the robot and build the map of the environment. Simulation results validate the performance improvement obtained by the proposed technique
Gesture Recognition Aplication based on Dynamic Time Warping (DTW) FOR Omni-Wheel Mobile Robot
This project presents of the movement of omni-wheel robot moves in the trajectory obtained from the gesture recognition system based on Dynamic Time Warping. Single camera is used as the input of the system, which is also a reference to the movement of the omni-wheel robot. Some
systems for gesture recognition have been developed using various methods and different approaches. The movement of the omni-wheel robot using the method of Dynamic Time Wrapping (DTW) which has the advantage able to calculate the distance of two data vectors with different lengths. By using this method we can measure the similarity between two sequences at different times and speeds. Dynamic Time
Warping to compare the two parameters at varying times and speeds. Application of DTW widely applied in video, audio, graphics, etc. Due to data that can be changed in a linear manner so that it can be analyzed with DTW. In short can find the most suitable value by minimizing the difference between two multidimensional signals that have been compressed. DTW method is expected to gesture recognition
system to work optimally, have a high enough value of accuracy and processing time is realtime
Optimization based solutions for control and state estimation in non-holonomic mobile robots: stability, distributed control, and relative localization
Interest in designing, manufacturing, and using autonomous robots has been rapidly growing
during the most recent decade. The main motivation for this interest is the wide range
of potential applications these autonomous systems can serve in. The applications include,
but are not limited to, area coverage, patrolling missions, perimeter surveillance, search
and rescue missions, and situational awareness. In this thesis, the area of control and
state estimation in non-holonomic mobile robots is tackled. Herein, optimization based
solutions for control and state estimation are designed, analyzed, and implemented to such
systems. One of the main motivations for considering such solutions is their ability of
handling constrained and nonlinear systems such as non-holonomic mobile robots. Moreover,
the recent developments in dynamic optimization algorithms as well as in computer
processing facilitated the real-time implementation of such optimization based methods
in embedded computer systems.
Two control problems of a single non-holonomic mobile robot are considered first; these
control problems are point stabilization (regulation) and path-following. Here, a model
predictive control (MPC) scheme is used to fulfill these control tasks. More precisely, a
special class of MPC is considered in which terminal constraints and costs are avoided.
Such constraints and costs are traditionally used in the literature to guarantee the asymptotic
stability of the closed loop system. In contrast, we use a recently developed stability
criterion in which the closed loop asymptotic stability can be guaranteed by appropriately
choosing the prediction horizon length of the MPC controller. This method is based on finite time controllability as well as bounds on the MPC value function.
Afterwards, a regulation control of a multi-robot system (MRS) is considered. In this
control problem, the objective is to stabilize a group of mobile robots to form a pattern.
We achieve this task using a distributed model predictive control (DMPC) scheme based
on a novel communication approach between the subsystems. This newly introduced
method is based on the quantization of the robots’ operating region. Therefore, the
proposed communication technique allows for exchanging data in the form of integers
instead of floating-point numbers. Additionally, we introduce a differential communication
scheme to achieve a further reduction in the communication load.
Finally, a moving horizon estimation (MHE) design for the relative state estimation
(relative localization) in an MRS is developed in this thesis. In this framework, robots
with less payload/computational capacity, in a given MRS, are localized and tracked
using robots fitted with high-accuracy sensory/computational means. More precisely,
relative measurements between these two classes of robots are used to localize the less
(computationally) powerful robotic members. As a complementary part of this study, the
MHE localization scheme is combined with a centralized MPC controller to provide an
algorithm capable of localizing and controlling an MRS based only on relative sensory
measurements. The validity and the practicality of this algorithm are assessed by realtime
laboratory experiments.
The conducted study fills important gaps in the application area of autonomous navigation
especially those associated with optimization based solutions. Both theoretical as
well as practical contributions have been introduced in this research work. Moreover, this
thesis constructs a foundation for using MPC without stabilizing constraints or costs in
the area of non-holonomic mobile robots
Learning visual docking for non-holonomic autonomous vehicles
This paper presents a new method of learning visual docking skills for non-holonomic vehicles by direct interaction with the environment. The method is based on a reinforcement algorithm, which speeds up Q-learning by applying memorybased sweeping and enforcing the “adjoining property”, a filtering mechanism to only allow transitions between states that satisfy a fixed distance. The method overcomes some limitations of reinforcement learning techniques when they are employed in applications with continuous non-linear systems, such as car-like vehicles. In particular, a good approximation to the optimal
behaviour is obtained by a small look-up table. The algorithm is tested within an image-based visual servoing framework on a docking task. The training time was less than 1 hour on the real vehicle. In experiments, we show the satisfactory performance of the algorithm
Comprehensive review on controller for leader-follower robotic system
985-1007This paper presents a comprehensive review of the leader-follower robotics system. The aim of this paper is to find and elaborate on the current trends in the swarm robotic system, leader-follower, and multi-agent system. Another part of this review will focus on finding the trend of controller utilized by previous researchers in the leader-follower system. The controller that is commonly applied by the researchers is mostly adaptive and non-linear controllers. The paper also explores the subject of study or system used during the research which normally employs multi-robot, multi-agent, space flying, reconfigurable system, multi-legs system or unmanned system. Another aspect of this paper concentrates on the topology employed by the researchers when they conducted simulation or experimental studies
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