202,471 research outputs found
AGATE: Adversarial Game Analysis for Tactical Evaluation
AGATE generates a set of ranked strategies that enables an autonomous vehicle to track/trail another vehicle that is trying to break the contact using evasive tactics. The software is efficient (can be run on a laptop), scales well with environmental complexity, and is suitable for use onboard an autonomous vehicle. The software will run in near-real-time (2 Hz) on most commercial laptops. Existing software is usually run offline in a planning mode, and is not used to control an unmanned vehicle actively. JPL has developed a system for AGATE that uses adversarial game theory (AGT) methods (in particular, leader-follower and pursuit-evasion) to enable an autonomous vehicle (AV) to maintain tracking/ trailing operations on a target that is employing evasive tactics. The AV trailing, tracking, and reacquisition operations are characterized by imperfect information, and are an example of a non-zero sum game (a positive payoff for the AV is not necessarily an equal loss for the target being tracked and, potentially, additional adversarial boats). Previously, JPL successfully applied the Nash equilibrium method for onboard control of an autonomous ground vehicle (AGV) travelling over hazardous terrain
Coordinated control of networked vehicles: An autonomous underwater system
The specification and design of coordinated control strategies for networked vehicle systems are discussed. The discussion is illustrated with an example of the coordinated operation of two teams of autonomous underwater vehicles collecting data to find the local minimum of a given oceanographic scalar field
Reliable autonomous vehicle control - a chance constrained stochastic MPC approach
In recent years, there is a growing interest in the development of systems capable of performing
tasks with a high level of autonomy without human supervision. This kind of systems are known as
autonomous systems and have been studied in many industrial applications such as automotive,
aerospace and industries. Autonomous vehicle have gained a lot of interest in recent years and have
been considered as a viable solution to minimize the number of road accidents. Due to the
complexity of dynamic calculation and the physical restrictions in autonomous vehicle, for example,
deterministic model predictive control is an attractive control technique to solve the problem of
path planning and obstacle avoidance. However, an autonomous vehicle should be capable of driving
adaptively facing deterministic and stochastic events on the road. Therefore, control design for
the safe, reliable and autonomous driving should consider vehicle model uncertainty as well
uncertain external influences. The stochastic model predictive control scheme provides the
most convenient scheme for the control of autonomous vehicles on moving horizons, where chance
constraints are to be used to guarantee the reliable fulfillment of trajectory constraints and
safety against static and random obstacles. To solve this kind of problems is known as chance
constrained model predictive control. Thus, requires the solution of a chance constrained
optimization on moving horizon. According to the literature, the major challenge for solving chance
constrained optimization is to calculate the value of probability. As a result, approximation
methods have been proposed for solving this task.
In the present thesis, the chance constrained optimization for the autonomous vehicle is solved
through approximation method, where the probability constraint is approximated by using a smooth
parametric function. This methodology presents two approaches that allow the solution of chance
constrained optimization problems in inner approximation and outer approximation. The aim of this
approximation methods is to reformulate the chance constrained optimizations problems as a sequence
of nonlinear programs. Finally, three case studies of autonomous vehicle for tracking and obstacle
avoidance are presented in this work, in which three levels probability of reliability are
considered
for the optimal solution.Tesi
A predictive control for autonomous vehicles using big data analysis
Big data analysis has an increasing importance in the field of the autonomous
vehicles. It is related to vehicular networks and individual control. The paper proposes the
improvement of a lateral autonomous vehicle control design through big data analysis on
the measured signals. Based on the data a decision tree is generated by using the C4.5 and
the MetaCost algorithms. It results in the regions of vehicle dynamic states and guarantees
the tracking of the autonomous vehicle. The lateral control problem is formed in an MPC
(Model Predictive Control) structure, in which the results of the big data analysis are built
as constraints. The efficiency of the proposed method is illustrated through a comparative
simulation example through a high-fidelity vehicle control software
Robust Control For Underwater Vehicle Systems With Time Delays
Presented in this paper is a robust control scheme for
controlling systems with time delays. The scheme is based on the Smith
controller and the LQG/LTR (Linear Quadratic Gaussian/Loop Transfer
Recovery) methodology. The methodology is applicable to undenvater
vehicle systems that exhibit time delays, including tethered vehicles
that are positioned through the movements of a surface ship and
autonomous vehicles that are controlled through an acoustic link. An
example, using full-scale data from the Woods Hole Oceanographic
Institution’s tethered vehicle ARGO, demonstrates the developments
Towards the Implementation of a MPC-based Planner on an Autonomous All-Terrain Vehicle
Planning and control for a wheeled mobile robot are challenging problems when poorly traversable terrains, including dynamic obstacles, are considered. To accomplish a mission, the control system should firstly guarantee the vehicle integrity, for example with respect to possible roll-over/tip-over phenomena. A fundamental contribution to achieve this goal,
however, comes from the planner as well. In fact, computing a path that takes into account the terrain traversability, the kinematic and dynamic vehicle constraints, and the presence of dynamic obstacles, is a first and crucial step towards ensuring the vehicle integrity. The present paper addresses some of the aforementioned issues, describing the hardware/software architecture of the planning and control system of an autonomous All-Terrain Mobile Robot and the implementation of a real-time path planner
Adaptive sampling in autonomous marine sensor networks
Submitted in partial fulfillment of the requirements for the degree of
Doctor of Philosophy at the Massachusetts Institute of Technology and the
Woods Hole Oceanographic Institution June 2006In this thesis, an innovative architecture for real-time adaptive and cooperative control of autonomous sensor platforms in a marine sensor network is described in the context of the autonomous oceanographic network scenario. This architecture has three major components, an intelligent, logical sensor that provides high-level environmental state information to a behavior-based autonomous vehicle control system, a new approach to behavior-based control of autonomous vehicles using multiple objective functions that allows reactive control
in complex environments with multiple constraints, and an approach to cooperative
robotics that is a hybrid between the swarm cooperation and intentional cooperation approaches.
The mobility of the sensor platforms is a key advantage of this strategy, allowing
dynamic optimization of the sensor locations with respect to the classification or localization of a process of interest including processes which can be time varying, not spatially isotropic and for which action is required in real-time.
Experimental results are presented for a 2-D target tracking application in which fully
autonomous surface craft using simulated bearing sensors acquire and track a moving target in open water. In the first example, a single sensor vehicle adaptively tracks a target while simultaneously relaying the estimated track to a second vehicle acting as a classification
platform. In the second example, two spatially distributed sensor vehicles adaptively track a moving target by fusing their sensor information to form a single target track estimate.
In both cases the goal is to adapt the platform motion to minimize the uncertainty of the target track parameter estimates. The link between the sensor platform motion and the target track estimate uncertainty is fully derived and this information is used to develop the
behaviors for the sensor platform control system. The experimental results clearly illustrate the significant processing gain that spatially distributed sensors can achieve over a single sensor when observing a dynamic phenomenon as well as the viability of behavior-based
control for dealing with uncertainty in complex situations in marine sensor networks.Supported by the Office of Naval Research, with a 3-year National Defense Science and Engineering Grant Fellowship and research
assistantships through the Generic Ocean Array Technology Sonar (GOATS) project, contract N00014-97-1-0202 and contract N00014-05-G-0106 Delivery Order 008, PLUSNET: Persistent Littoral Undersea Surveillance Network
Game Theoretic Modeling of Driver and Vehicle Interactions for Verification and Validation of Autonomous Vehicle Control Systems
Autonomous driving has been the subject of increased interest in recent years both in industry and in academia. Serious efforts are being pursued to address legal, technical, and logistical problems and make autonomous cars a viable option for everyday transportation. One significant challenge is the time and effort required for the verification and validation of the decision and control algorithms employed in these vehicles to ensure a safe and comfortable driving experience. Hundreds of thousands of miles of driving tests are required to achieve a well calibrated control system that is capable of operating an autonomous vehicle in an uncertain traffic environment where interactions among multiple drivers and vehicles occur simultaneously. Traffic simulators where these interactions can be modeled and represented with reasonable fidelity can help to decrease the time and effort necessary for the development of the autonomous driving control algorithms by providing a venue where acceptable initial control calibrations can be achieved quickly and safely before actual road tests. In this paper, we present a game theoretic traffic model that can be used to: 1) test and compare various autonomous vehicle decision and control systems and 2) calibrate the parameters of an existing control system. We demonstrate two example case studies, where, in the first case, we test and quantitatively compare two autonomous vehicle control systems in terms of their safety and performance, and, in the second case, we optimize the parameters of an autonomous vehicle control system, utilizing the proposed traffic model and simulation environment. IEE
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