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Model predictive control with haptic feedback for robot manipulation in cluttered scenariosKillpack, Marc Daniel 13 January 2014 (has links)
Current robot manipulation and control paradigms have largely been developed for static or highly structured environments such as those common in factories. For most techniques in robot trajectory generation, such as heuristic-based geometric planning, this has led to putting a high cost on contact with the world. This approach and methodology can be prohibitive to robots operating in many unmodeled and dynamic environments. This dissertation presents work on using haptic based feedback (torque and tactile sensing) to formulate a controller for robot manipulation in clutter. We define “clutter” as any environment in which we expect the robot to make both incidental and purposeful contact while maneuvering and manipulating. The controllers developed in this dissertation take the form of single or multi-time step Model Predictive Control (a form of optimal control which incorporates feedback) which attempts to regulate contact forces at multiple locations on a robot arm while reaching to a goal. The results and conclusions in this dissertation are based on extensive testing in simulation (tens of thousands of trials) and testing in realistic scenarios with real robots incorporating tactile sensing. The approach is novel in the sense that it allows contact and explicitly incorporate the contact and predictive model of the robot arm in calculating control effort at every time step. The expected broader impact of this research is progress towards a new foundation of reactive feedback controllers that will include a higher likelihood of success in many constrained and dynamic scenarios such as reaching into containers without line of sight, maneuvering in cluttered search and rescue situations or working with unpredictable human co-workers.
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Predictive Control of Electric Motors Drives for Unmanned Off-road Wheeled VehiclesMohammed, Mostafa Ahmed Ismail 02 April 2013 (has links)
Starting a few decades ago, the unmanned wheeled vehicle research has drawn
lately more attention, especially for off-road environment. As the demand to use
electric vehicles increased, the need to conceptualize the use of electrically driven
vehicles in autonomous operations became a target. That is because in addition to the
fact that they are more environmentally friendly, they are also easier to control. This
also gives another reason to enhance further the energy economy of those unmanned
electric vehicles. Off-road vehicles research was always challenging, but in the
present work the nature of the off-road land is utilized to benefit from in order to
enhance the energy consumption of those vehicles. An algorithm for energy
consumption optimization for electrically driven unmanned wheeled vehicles is
presented. The algorithm idea is based on the fact that in off-road conditions, when
the vehicle passes a ditch or a hole, the kinetic energy gained while moving downhill
could be utilized to reduce the energy consumption for moving uphill if the
dimensions of the ditch/hole were known a distance ahead. Two manipulated
variables are evaluated: the wheels DC motors supply voltage and the DC armature
current. The developed algorithm is analysed and compared to the PID speed
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controller and to the open-loop control of DC motors. The developed predictive
controller achieved encouraging results compared to the PID speed control and also
compared to the open-loop control. Also, the use of the DC armature current as a
manipulated variable showed more noticeable improvement over using the DC input
voltage. Experimental work was carried out to validate the predictive control
algorithm. A mobile robot with two DC motor driven wheels was deployed to
overcome a ditch-like hindrance. The experimental results verified the simulation
results. A parametric study for the predictive control is conducted. The effect of
changing the downhill angle and the uphill angle as well as the size of the prediction
horizon on the consumed electric energy by the DC motors is addressed. The
simulation results showed that, when using the proposed approach, the larger the
prediction horizon, the lower the energy consumption is.
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Towards Fault Reactiveness in Wireless Sensor Networks with Mobile Carrier RobotsFalcon Martinez, Rafael Jesus 04 April 2012 (has links)
Wireless sensor networks (WSN) increasingly permeate modern societies nowadays. But in spite of their plethora of successful applications, WSN are often unable to surmount many operational challenges that unexpectedly arise during their lifetime. Fortunately, robotic agents can now assist a WSN in various ways. This thesis illustrates how mobile robots which are able to carry a limited number of sensors can help the network react to sensor faults, either during or after its deployment in the monitoring region.
Two scenarios are envisioned. In the first one, carrier robots surround a point of interest
with multiple sensor layers (focused coverage formation). We put forward the first known algorithm
of its kind in literature. It is energy-efficient, fault-reactive and aware of the bounded
robot cargo capacity. The second one is that of replacing damaged sensing units with spare,
functional ones (coverage repair), which gives rise to the formulation of two novel combinatorial
optimization problems. Three nature-inspired metaheuristic approaches that run at a centralized location are proposed. They are able to find good-quality solutions in a short time. Two frameworks for the identification of the damaged nodes are considered. The first one leans upon diagnosable systems, i.e. existing distributed detection models in which individual units perform tests upon each other. Two swarm intelligence algorithms are designed to quickly and reliably spot faulty sensors in this context. The second one is an evolving risk management framework for WSNs that is entirely formulated in this thesis.
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Design Of A Mobile Robot To Move On Rough TerrainKirmizigul, Ugur 01 December 2005 (has links) (PDF)
In this thesis work, a mobile robot is designed to be used in search and rescue
operations to help the human rescue workers. The difficult physical conditions in the
ruins obstruct the movement. Therefore, it is aimed to design a search and rescue robot
which can move easily on rough terrain and climb over the obstacles. The designed
robot is made up of three modules. A connecting unit is designed that is situated
between each module. This connecting unit which is composed of two universal
and one revolute joint gives 5 DOF relative motions to the modules. On the other
hand, the wheel&rsquo / s continuous contact with the ground is important while moving on
rough terrain. In order to increase the adaptation of the robot to the rough terrain the
rear axle is connected to the body with a revolute joint. Besides, skid steering system
is used in the design of the robot to attain a compact and light solution which requires
few parts.
In the study, kinematic equations and dynamic equations of the robot are obtained
to be used by the control program. The dynamic equations are obtained by using
the Newton &ndash / Euler formulation. The forces, which are transmitted by the connecting
unit to the modules, and the reaction forces formed between the wheels and
the ground are derived by using these equations.
&ldquo / Follow-the-Leader approach&rdquo / is used as a control strategy to make the
modules move in formation and to reduce the tracking problem. In this approach, the
first module is the leader and the second and third modules follow it. A Matlab program
is written to control the robot by using the constructed mathematical model of
the robot. The reaction forces between the wheels and the ground are calculated
through using the Matlab program written. Moreover to make the simulations of the
robot for some cases, a model is constructed in ADAMS program.
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Probabilistic topological mapsRanganathan, Ananth 04 March 2008 (has links)
Topological maps are light-weight, graphical representations of environments
that are scalable and amenable to symbolic manipulation. Thus, they are well-
suited for basic robot navigation applications, and also provide a representational
basis for the procedural and semantic information needed for higher-level robotic
tasks. However, their widespread use has been impeded in part by the lack of
reliable, general purpose algorithms for their construction.
In this dissertation, I present a probabilistic framework for the construction of
topological maps that addresses topological ambiguity, is failure-aware, computa-
tionally efficient, and can incorporate information from various sensing modalities.
The framework addresses the two major problems of topological mapping, namely
topological ambiguity and landmark detection.
The underlying idea behind overcoming topological ambiguity is that the com-
putation of the Bayesian posterior distribution over the space of topologies is an
effective means of quantifying this ambiguity, caused due to perceptual aliasing
and environment variability. Since the space of topologies is combinatorial, the
posterior on it cannot be computed exactly. Instead, I introduce the concept of
Probabilistic Topological Maps (PTMs), a sample-based representation that ap-
proximates the posterior distribution over topologies given the available sensor
measurements. Sampling algorithms for the efficient computation of PTMs are
described.
The PTM framework can be used with a wide variety of landmark detection
schemes under mild assumptions. As part of the evaluation, I describe a novel
landmark detection technique that makes use of the notion of "surprise" in mea-
surements that the robot obtains, the underlying assumption being that landmarks
are places in the environment that generate surprising measurements. The com-
putation of surprise in a Bayesian framework is described and applied to various
sensing modalities for the computation of PTMs.
The PTM framework is the first instance of a probabilistic technique for topo-
logical mapping that is systematic and comprehensive. It is especially relevant
for future robotic applications which will need a sparse representation capable of
accomodating higher level semantic knowledge. Results from experiments in real environments demonstrate that the framework can accomodate diverse sensors such
as camera rigs and laser scanners in addition to odometry. Finally, results are pre-
sented using various landmark detection schemes besides the surprise-based one.
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The role of passive joint stiffness and active knee control in robotic leg swinging: applications to dynamic walkingMigliore, Shane A. 04 January 2008 (has links)
The field of autonomous walking robots has been dominated by the trajectory-control approach, which rigidly dictates joint angle trajectories at the expense of both energy efficiency and stability, and the passive dynamics approach, which uses no actuators, relying instead on natural mechanical dynamics as the sole source of control. Although the passive dynamics approach is energy efficient, it lacks the ability to modify gait or adapt to disturbances. Recently, minimally actuated walkers, or dynamic walkers, have been developed that use hip or ankle actuators---knees are always passive---to regulate mechanical energy variations through the timely application of joint torque pulses. Despite the improvement minimal actuation has provided, energy efficiency remains below target values and perturbation rejection capability (i.e., stability) remains poor. In this dissertation, we develop and analyze a simplified robotic system to assess biologically inspired methods of improving energy efficiency and stability in dynamic walkers. Our system consists of a planar, dynamically swinging leg with hip and knee actuation. Neurally inspired, nonlinear oscillators provide closed-loop control without overriding the leg's natural dynamics. We first model the passive stiffness of muscles by applying stiffness components to the joints of a hip-actuated swinging leg. We then assess the effect active knee control has on unperturbed and perturbed leg swinging. Our results indicate that passive joint stiffness improves energy efficiency by reducing the actuator work required to counter gravitational torque and by promoting kinetic energy transfer between the shank and thigh. We also found that active knee control 1) is detrimental to unperturbed leg swinging because it negatively affects energy efficiency while producing minimal performance improvement and 2) is beneficial during perturbed swinging because the perturbation rejection improvement outweighs the reduction in energy efficiency. By analyzing the effects of applying passive joint stiffness and active knee control to dynamic walkers, this work helps to bridge the gap between the performance capability of trajectory-control robots and the energy-efficiency of passive dynamic robots.
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Control of reconfigurability and navigation of a wheel-legged robot based on active visionBrooks, Douglas Antwonne 31 July 2008 (has links)
The ability of robotic units to navigate various terrains is critical to the advancement of robotic operation in real world environments. Next generation robots will need to adapt to their environment in order to accomplish tasks that are either too hazardous, too time consuming, or physically impossible for human-beings. Such tasks may include accurate and rapid explorations of various planets or potentially dangerous areas on planet Earth. This research investigates a navigation control methodology for a wheel-legged robot based on active vision. The method presented is designed to control the reconfigurability of the robot (i.e. control the usage of the wheels and legs), depending upon the obstacle/terrain, based on perception. Surface estimation for robot reconfigurability is implemented using a region growing method and a characterization and traversability assessment generated from camera data. As a result, a mathematical approach that directs necessary navigation behavior is implemented to control robot mobility. The hybrid wheeled-legged rover possesses a four-legged or six-legged walking system as well as a four-wheeled mobility system.
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Visual place categorizationWu, Jianxin 06 July 2009 (has links)
Knowing the semantic category of a robot's current position not only facilitates the robot's navigation, but also greatly improves its ability to serve human needs and to interpret the scene. Visual Place Categorization (VPC) is addressed in this dissertation, which refers to the problem of predicting the semantic category of a place using visual information collected from an autonomous robot platform.
Census Transform (CT) histogram and Histogram Intersection Kernel (HIK) based visual codebooks are proposed to represent an image. CT histogram encodes the stable spatial structure of an image that reflects the functionality of a location. It is suitable for categorizing places and has shown better performance than commonly used descriptors such as SIFT or Gist in the VPC task.
HIK has been shown to work better than the Euclidean distance in classifying histograms. We extend it in an unsupervised manner to generate visual codebooks for the CT histogram descriptor. HIK codebooks help CT histogram to deal with the huge variations in VPC and improve system accuracy. A computational method is also proposed to generate HIK codebooks in an efficient way.
The first significant VPC dataset in home environments is collected and is made publicly available, which is also used to evaluate the VPC system based on the proposed techniques. The VPC system achieves promising results for this challenging problem, especially for important categories such as bedroom, bathroom, and kitchen. The proposed techniques achieved higher accuracies than competing descriptors and visual codebook generation methods.
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Robust execution of robot task-plans : a knowledge-based approachBouguerra, Abdelbaki January 2008 (has links)
Autonomous mobile robots are being developed with the aim of accomplishing complex tasks in different environments, including human habitats as well as less friendly places, such as distant planets and underwater regions. A major challenge faced by such robots is to make sure that their actions are executed correctly and reliably, despite the dynamics and the uncertainty inherent in their working space. This thesis is concerned with the ability of a mobile robot to reliably monitor the execution of its plans and detect failures. Existing approaches for monitoring the execution of plans rely mainly on checking the explicit effects of plan actions, i.e., effects encoded in the action model. This supposedly means that the effects to monitor are directly observable, but that is not always the case in a real-world environment. In this thesis, we propose to use semantic domain-knowledge to derive and monitor implicit expectations about the effects of actions. For instance, a robot entering a room asserted to be an office should expect to see at least a desk, a chair, and possibly a PC. These expectations are derived from knowledge about the type of the room the robot is entering. If the robot enters a kitchen instead, then it should expect to see an oven, a sink, etc. The major contributions of this thesis are as follows. • We define the notion of Semantic Knowledge-based Execution Monitoring SKEMon, and we propose a general algorithm for it based on the use of description logics for representing knowledge. • We develop a probabilistic approach of semantic Knowledge-based execution monitoring to take into account uncertainty in both acting and sensing. Specifically, we allow for sensing to be unreliable and for action models to have more than one possible outcome. We also take into consideration uncertainty about the state of the world. This development is essential to the applicability of our technique, since uncertainty is a pervasive feature in robotics. • We present a general schema to deal with situations where perceptual information relevant to SKEMon is missing. The schema includes steps for modeling and generating a course of action to actively collect such information. We describe approaches based on planning and greedy action selection to generate the information-gathering solutions. The thesis also shows how such a schema can be applied to respond to failures occurring before or while an action is executed. The failures we address are ambiguous situations that arise when the robot attempts to anchor symbolic descriptions (relevant to a plan action) in perceptual information. The work reported in this thesis has been tested and verified using a mobile robot navigating in an indoor environment. In addition, simulation experiments were conducted to evaluate the performance of SKEMon using known metrics. The results show that using semantic knowledge can lead to high performance in monitoring the execution of robot plans.
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Mapping of indoor environments by robots using low-cost vision sensorsTaylor, Trevor January 2009 (has links)
For robots to operate in human environments they must be able to make their own maps because it is unrealistic to expect a user to enter a map into the robot’s memory; existing floorplans are often incorrect; and human environments tend to change. Traditionally robots have used sonar, infra-red or laser range finders to perform the mapping task. Digital cameras have become very cheap in recent years and they have opened up new possibilities as a sensor for robot perception. Any robot that must interact with humans can reasonably be expected to have a camera for tasks such as face recognition, so it makes sense to also use the camera for navigation. Cameras have advantages over other sensors such as colour information (not available with any other sensor), better immunity to noise (compared to sonar), and not being restricted to operating in a plane (like laser range finders). However, there are disadvantages too, with the principal one being the effect of perspective. This research investigated ways to use a single colour camera as a range sensor to guide an autonomous robot and allow it to build a map of its environment, a process referred to as Simultaneous Localization and Mapping (SLAM). An experimental system was built using a robot controlled via a wireless network connection. Using the on-board camera as the only sensor, the robot successfully explored and mapped indoor office environments. The quality of the resulting maps is comparable to those that have been reported in the literature for sonar or infra-red sensors. Although the maps are not as accurate as ones created with a laser range finder, the solution using a camera is significantly cheaper and is more appropriate for toys and early domestic robots.
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