64 research outputs found
Computational intelligence approaches to robotics, automation, and control [Volume guest editors]
No abstract available
Computational intelligence approaches to robotics, automation, and control [Volume guest editors]
No abstract available
Model Predictive Control of Nonholonomic Mobile Robots
In this work, we investigate the possibility of using model predictive control (MPC) for the motion coordination of nonholonomic mobile robots. The contributions of this dissertation can be summarized as follows.A robust formation controller is developed for the leader-following formation of unmanned aerial vehicles (UAVs). With the assumption that an autopilot operating in holding mode at the low-layer, we present a two-layered hierarchical control scheme which allows a team of UAVs to perform complex navigation tasks under limited inter-vehicle communication. Specifically, the robust control law eliminates the requirement of leader's velocity and acceleration information, which reduces the communication overhead.A dual-mode MPC algorithm that allows a team of mobile robots to navigate in formations is developed. The stability of the formation is guaranteed by constraining the terminal state to a terminal region and switching to a stabilizing terminal controller at the boundary of the terminal region. With this dual-mode MPC implementation, stability is achieved while feasibility is relaxed.A first-state contractive model predictive control (FSC-MPC) algorithm is developed for the trajectory tracking and point stabilization problems of nonholonomic mobile robots. The stability of the proposed MPC scheme is guaranteed by adding a first-state contractive constraint and the controller is exponentially stable. The convergence is faster and no terminal region calculation is required. Tracking a trajectory moving backward is no longer a problem under this MPC controller. Moreover, the proposed MPC controller has simultaneous tracking and point stabilization capability.Simulation results are presented to verify the validity of the proposed control algorithms and demonstrate the performance of the proposed controllers.School of Electrical & Computer Engineerin
Collision Free Navigation of a Multi-Robot Team for Intruder Interception
In this report, we propose a decentralised motion control algorithm for the
mobile robots to intercept an intruder entering (k-intercepting) or escaping
(e-intercepting) a protected region. In continuation, we propose a
decentralized navigation strategy (dynamic-intercepting) for a multi-robot team
known as predators to intercept the intruders or in the other words, preys,
from escaping a siege ring which is created by the predators. A necessary and
sufficient condition for the existence of a solution of this problem is
obtained. Furthermore, we propose an intelligent game-based decision-making
algorithm (IGD) for a fleet of mobile robots to maximize the probability of
detection in a bounded region. We prove that the proposed decentralised
cooperative and non-cooperative game-based decision-making algorithm enables
each robot to make the best decision to choose the shortest path with minimum
local information. Then we propose a leader-follower based collision-free
navigation control method for a fleet of mobile robots to traverse an unknown
cluttered environment where is occupied by multiple obstacles to trap a target.
We prove that each individual team member is able to traverse safely in the
region, which is cluttered by many obstacles with any shapes to trap the target
while using the sensors in some indefinite switching points and not
continuously, which leads to saving energy consumption and increasing the
battery life of the robots consequently. And finally, we propose a novel
navigation strategy for a unicycle mobile robot in a cluttered area with moving
obstacles based on virtual field force algorithm. The mathematical proof of the
navigation laws and the computer simulations are provided to confirm the
validity, robustness, and reliability of the proposed methods
Learning Environmental Models With Multi-Robot Teams Using A Dynamical Systems Approach
Robots monitoring complex, spatiotemporal phenomena require rich, meaningful representations of the environment. This thesis presents methods for representing the environment as a dynamical system with machine learning techniques. Specifically, we formulate machine learning methods that lend to data-driven modeling of the phenomena. The data-driven modeling explicitly leverages theoretical foundations of dynamical systems theory. Dynamical systems theory offers mathematical and physically interpretable intuitions about the environmental representation. The contributions presented include distributed algorithms, online adaptation, uncertainty quantification, and feature extraction to allow for the actualization of these techniques on-board robots. The environmental representations guide robot behavior in developing strategies such as optimal sensing and energy-efficient navigation. The methods and procedures provided in this thesis were verified across complex, spatiotemporal environments and on experimental robots
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