Showing papers on "Autonomous system (mathematics) published in 2005"

MPC-Based Approach to Active Steering for Autonomous Vehicle Systems

Abstract: In this paper a novel approach to autonomous steering systems is presented. A model predictive control (MPC) scheme is designed in order to stabilize a vehicle along a desired path while fulfilling its physical constraints. Simulation results show the benefits of the systematic control methodology used. In particular we show how very effective steering manoeuvres are obtained as a result of the MPC feedback policy. Moreover, we highlight the trade off between the vehicle speed and the required preview on the desired path in order to stabilize the vehicle. The paper concludes with highlights on future research and on the necessary steps for experimental validation of the approach.

Convergence Properties of the Gravitational Algorithm in Asynchronous Robot Systems

Abstract: This paper considers the convergence problem in autonomous mobile robot systems. A natural algorithm for the problem requires the robots to move towards their center of gravity. This paper proves the correctness of the gravitational algorithm in the fully asynchronous model. It also analyzes its convergence rate and establishes its convergence in the presence of crash faults.

Power-steering control architecture for automatic driving

Abstract: The unmanned control of the steering wheel is, at present, one of the most important challenges facing researchers in autonomous vehicles within the field of intelligent transportation systems (ITSs). In this paper, we present a two-layer control architecture for automatically moving the steering wheel of a mass-produced vehicle. The first layer is designed to calculate the target position of the steering wheel at any time and is based on fuzzy logic. The second is a classic control layer that moves the steering bar by means of an actuator to achieve the position targeted by the first layer. Real-time kinematic differential global positioning system (RTK-DGPS) equipment is the main sensor input for positioning. It is accurate to about 1 cm and can finely locate the vehicle trajectory. The developed systems are installed on a Citroe/spl uml/n Berlingo van, which is used as a testbed vehicle. Once this control architecture has been implemented, installed, and tuned, the resulting steering maneuvering is very similar to human driving, and the trajectory errors from the reference route are reduced to a minimum. The experimental results show that the combination of GPS and artificial-intelligence-based techniques behaves outstandingly. We can also draw other important conclusions regarding the design of a control system derived from human driving experience, providing an alternative mathematical formalism for computation, human reasoning, and integration of qualitative and quantitative information.

Method of constructing a backup path in an autonomous system

Abstract: A method of constructing a backup path in an autonomous system (AS) for failure of an inter-AS link is described. The method comprises identifying an alternate inter-AS path and constructing a tunnel to an end point on the alternate path.

Method of implementing a backup path in an autonomous system

Abstract: A method of implementing a backup path in an autonomous system (AS) for failure of an inter-AS link is described. The method comprises forwarding data elements destined for the failed link via a backup path and including a loop prevention attribute in the packet.

Turning Segways into soccer robots

Abstract: Purpose – To adapt the segway RMP, a dynamically balancing robot base, to build robots capable of playing soccer autonomously.Design/methodology/approach – Focuses on the electro‐mechanical mechanisms required to make the Segway RMP autonomous, sensitive, and able to control a football.Findings – Finds that turning a Segway RMP into a soccer‐playing robot requires a combined approach to the mechanics, electronics and software control.Research implications – Although software algorithms necessary for autonomous operation and infrastructure supplying logging and debugging facilities have been developed, the scenario of humans and robots playing soccer together has yet to be addressed.Practical implications – Turning the model into a soccer playing robot demonstrates the technique of combining mechanics, electronics and software control.Originality/value – Shows how the model as a base platform can be developed into a fully functional, autonomous, soccer‐playing robot.

A completely autonomous system that learns anomalous movements in advanced videosurveillance applications

Abstract: This paper describes an automatic real-time video surveillance system, capable of autonomously learning and signaling anomalous activities of moving objects To obtain these capabilities, an improved version of the altruistic vector quantization algorithm (AVQ) is proposed The modified AVQ automatically evaluates the number of trajectory prototypes, and improves the representativeness of the prototypes themselves, so the visual events can be easily and accurately classified Anomalous behaviors are detected if visual trajectories deviate from the self-learned representations of "typical" behaviors The system has been implemented by means of standard PCs and TV cameras, and has been tested in many real outdoor contexts in different conditions (night and day) Currently it is used to monitor the storage areas of British Airways at the airport of Peretola (Florence, Italy), and some access gates of Autostrade per FItalia SpA (the main Italian highways company) If the camera field-of-view is changed, the system automatically re-learns new "typical" behaviors and accurately detects anomalous events

Hierarchical fuzzy control for autonomous navigation of wheeled robots

Abstract: The autonomous navigation wheeled robots (WR) requires integrated kinematic and dynamic control to perform trajectory tracking, path following and stabilisation Considering a WR is a nonholonomic dynamic system with intrinsic nonlinearity, unmodelled disturbance and unstructured unmodelled dynamics, fuzzy logic system based control is appropriate and practical However, the multivariable control structure of the WR results in the curse of dimensionality of the fuzzy design and prevents a domain expert from building the linguistic rules for autonomous navigation Hierarchical fuzzy design decomposes the controller into three low-dimensionality fuzzy systems: fuzzy steering, fuzzy linear velocity control and fuzzy angular velocity control, so that manual construction of each rule base becomes feasible and easy The proposed design enables a WR to perform position control in trajectory tracking and velocity profile tracking in continuous drive The coupling effect between linear and angular motion dynamics is considered in the fuzzy steering by building appropriate linguistic rules To facilitate the autonomous navigation design and verification, a prototype and the model of a kind of WR have been developed, and equipped with the hierarchical fuzzy control system The simulation and experimental results are shown and compared

User Modelling for Principled Sliding Autonomy in Human-Robot Teams

Abstract: The complexity of heterogeneous robotic teams and the domains in which they are deployed is fast outstripping the ability of autonomous control software to handle the myriad failure modes inherent in such systems. As a result, remote human operators are being brought into the teams as equal members via sliding autonomy to increase the robustness and effectiveness of such teams. A principled approach to deciding when to request help from the human will benefit such systems by allowing them to efficiently make use of the human partner. We have developed a cost-benefit analysis framework and models of both autonomous system and user in order to enable such principled decisions. In addition, we have conducted user experiments to determine the proper form for the learning curve component of the human’s model. The resulting automated analysis is able to predict the performance of both the autonomous system and the human in order to assign responsibility for tasks to one or the other.

Controller area network based distributed control for autonomous vehicles

Abstract: The goal of this project was to evaluate the potential of a controller area network (CAN bus) to be used as the communication network for a distributed control system on an autonomous agricultural vehicle. The prototype system utilized microcontroller-driven nodes to act as control points along a CAN bus. Messages were transferred to the steering, transmission, and hitch control nodes via a task computer. The task computer utilized global positioning system data to generate appropriate control commands. Laboratory and field testing demonstrated that each of the control nodes could function simultaneously over the CAN bus. Results showed that the task computer adequately applied a feedback control model to the system and achieved guidance accuracy levels well within the desired range. Testing also demonstrated the system's ability to complete normal field operations, such as headland turning and implement control. ver the past several years, technology has contin- ued to play an increasing role in agriculture. The industry has recently seen the advent and develop- ment of many types of automated vehicles ranging from planters to sprayers to harvesters. These vehicles have all sustained different levels of automation. Some were capa- ble of fully autonomous field operations, while others were developed for specific control operations such as autosteer- ing (Reid et al., 2000). Commercialization of these technolo- gies has come at a substantial cost to the farmer. Autosteer systems range in cost from $10,000 to $50,000 depending on their accuracy and functionality. The largest component of the system price is the level of global positioning system (GPS) accuracy desired. There has also been a recent increase in the number of electronic components on agricultural equipment. During normal field situations, operators must interact with spray rate controllers, variable rate controllers, and implement system controllers, as well as controls for normal vehicle operation. Attempts have been made to create a standard communication link within all agricultural equipment but have thus far failed within the U.S. The most common

Autonomous agents and multi –agent systems (AAMAS) for the military – issues and challenges

Abstract: The military domain is a very challenging environment and human endeavour in this domain is characterized by uncertainty and the need to be able to deal with significant and disruptive dynamic changes. In addition, activities are driven by human decision-makers who need support in making sense of the environment and with reasoning about, and effecting, possible futures. Hence, various unique factors need to be taken into account when considering the provision of applications, tools, devices and infrastructure for the military domain. This paper will itemize and discuss some of these factors in the context of autonomous agents and multi-agent systems. This paper is a desiderata for the research space.

Rough sets in perception-based computing

Abstract: Intelligent systems for many real life problems can be modeled by systems of complex objects and their parts changing and interacting over time. The objects are usually linked by certain dependencies, can cooperate between themselves and are able to perform complex and flexible actions (operations) in an autonomous manner. Such systems are identified as complex dynamical systems [2,40], autonomous multiagent systems [20,40], or swarm intelligent systems (see, e.g., [28,7]).

Development of self-organising emergent applications with simulation-based numerical analysis

Abstract: The goal of engineering self-organising emergent systems is to acquire a macroscopic system behaviour solely from autonomous local activity and interaction. Due to the non-deterministic nature of such systems, it is hard to guarantee that the required macroscopic behaviour is achieved and maintained. Before even considering a self-organising emergent system in an industrial context, e.g. for Automated Guided Vehicle (AGV) transportation systems, such guarantees are needed. An empirical analysis approach is proposed that combines realistic agent-based simulations with existing scientific numerical algorithms for analysing the macroscopic behaviour. The numerical algorithm itself obtains the analysis results on the fly by steering and accelerating the simulation process according to the algorithm's goal. The approach is feasible, compared to formal proofs, and leads to more reliable and valuable results, compared to mere observation of simulation results. Also, the approach allows to systematically analyse the macroscopic behaviour to acquire macroscopic guarantees and feedback that can be used by an engineering process to iteratively shape a self-organising emergent solution.

Autonomous agent based distributed fault-tolerant intrusion detection system

Abstract: Because all vulnerabilities of a network cannot be realized, and penetration of the system cannot always be prevented, intrusion detection systems have become necessary to ensure the security of a network. The intrusion detection systems need to be accurate, adaptive, and extensible. Given these requirements and the complexities of today's network environments, the design of an intrusion detection system has become a very challenging task. A great deal of research has been conducted on intrusion detection in a distributed environment to circumvent the problems of centralized approaches. However, distributed intrusion detection systems suffer from a number of drawbacks e.g., high rates of false positives, low efficiency etc. In this paper, we propose the architecture of a fully distributed intrusion detection system that uses a set of autonomous but cooperating agents. The system has also the capability of isolating compromised nodes from intrusion detection activity thereby ensuring fault-tolerance in computation.

3D position tracking for all-terrain robots

Abstract: Rough terrain robotics is a fast evolving field of research and a lot of effort is deployed towards enabling a greater level of autonomy for outdoor vehicles. Such robots find their application in scientific exploration of hostile environments like deserts, volcanoes, in the Antarctic or on other planets. They are also of high interest for search and rescue operations after natural or artificial disasters. The challenges to bring autonomy to all terrain rovers are wide. In particular, it requires the development of systems capable of reliably navigate with only partial information of the environment, with limited perception and locomotion capabilities. Amongst all the required functionalities, locomotion and position tracking are among the most critical. Indeed, the robot is not able to fulfill its task if an inappropriate locomotion concept and control is used, and global path planning fails if the rover loses track of its position. This thesis addresses both aspects, a) efficient locomotion and b) position tracking in rough terrain. The Autonomous System Lab developed an off-road rover (Shrimp) showing excellent climbing capabilities and surpassing most of the existing similar designs. Such an exceptional climbing performance enables an extension in the range of possible areas a robot could explore. In order to further improve the climbing capabilities and the locomotion efficiency, a control method minimizing wheel slip has been developed in this thesis. Unlike other control strategies, the proposed method does not require the use of soil models. Independence from these models is very significant because the ability to operate on different types of soils is the main requirement for exploration missions. Moreover, our approach can be adapted to any kind of wheeled rover and the processing power needed remains relatively low, which makes online computation feasible. In rough terrain, the problem of tracking the robot's position is tedious because of the excessive variation of the ground. Further, the field of view can vary significantly between two data acquisition cycles. In this thesis, a method for probabilistically combining different types of sensors to produce a robust motion estimation for an all-terrain rover is presented. The proposed sensor fusion scheme is flexible in that it can easily accommodate any number of sensors, of any kind. In order to test the algorithm, we have chosen to use the following sensory inputs for the experiments: 3D-Odometry, inertial measurement unit (accelerometers, gyros) and visual odometry. The 3D-Odometry has been specially developed in the framework of this research. Because it accounts for ground slope discontinuities and the rover kinematics, this technique results in a reasonably precise 3D motion estimate in rough terrain. The experiments provided excellent results and proved that the use of complementary sensors increases the robustness and accuracy of the pose estimate. In particular, this work distinguishes itself from other similar research projects in the following ways: the sensor fusion is performed with more than two sensor types and sensor fusion is applied a) in rough terrain and b) to track the real 3D pose of the rover. Another result of this work is the design of a high-performance platform for conducting further research. In particular, the rover is equipped with two computers, a stereovision module, an omnidirectional vision system, an inertial measurement unit, numerous sensors and actuators and electronics for power management. Further, a set of powerful tools has been developed to speed up the process of debugging algorithms and analyzing data stored during the experiments. Finally, the modularity and portability of the system enables easy adaptation of new actuators and sensors. All these characteristics speed up the research in this field.

Rehabilitation robot in intelligent home environment - software architecture and implementation of a distributed system

Abstract: Rehabilitation robots (e.g. FRIEND as intelligent wheelchair mounted manipulator) are being developed to gain their user's autonomy within daily life environment. To prevent a high cognitive load onto the user, task input on a high level of abstraction is mandatory. State-of-the-art rehabilitation robots are still not capable to integrate fragments of intelligent behavior into an overall context and to solve complex tasks. A basic problem is how to cope with system complexity as well as computational complexity that evolve during task planning. A compromise towards feasibility is to equip the system's environment with smart components that provide own intelligence and thus reduce the complexity of the robotic system. However, a structured approach is necessary to fuse the distributed intelligence. This paper is about the concept and realization of a software-framework being able to execute autonomous system operations together with information retrieving capabilities and user interactions within a distributed system. Key aspects of development have been to provide robust run-time behavior of the system along with the inclusion and resolving of redundant sensor information as well as to reduce the effort of system programming to a minimum. The application of the developed framework is demonstrated on base of sample steps of its integration with the FRIEND II rehabilitation robotic system within an intelligent home environment.

Experiments in emergent programming using self-organizing multi-agent systems

Abstract: We propose to investigate the concept of an Emergent Programming Environment enabling the development of complex adaptive systems. For this we use as a foundation the concept of emergence and a multi-agent system technology based on cooperative self-organizing mechanisms. The general objective is then to develop a complete programming language in which each instruction is an autonomous agent trying to be in a cooperative state with the other agents of the system, as well as with the environment of the system. The work presented here aims at showing the feasibility of such a concept by specifying, and experimenting with, a core of instruction-agents needed for a sub-set of mathematical calculus.

Knowledge representation and unmanned aerial vehicles

Abstract: Knowledge representation technologies play a fundamental role in any autonomous system that includes deliberative capability and that internalizes models of its internal and external environments Integrating both high- and low-end autonomous functionality seamlessly in autonomous architectures is currently one of the major open problems in robotics research UAVs offer especially difficult challenges in comparison with ground robotic systems due to the often tight time constraints and safety considerations that must be taken into account This article provides an overview of some of the knowledge representation technologies and deliberative capabilities developed for a fully deployed autonomous unmanned aerial vehicle system to meet some of these challenges

Agent-Based computing and programming of agent systems

Abstract: The concepts of autonomous agent and multi-agent system provide appropriate levels of abstraction for the design, implementation and simulation of many complex, distributed computational systems, particularly those systems open to external participants. Programming such agent systems presents many difficult challenges, both conceptually and practically, and addressing these challenges will be crucial for the development of agent technologies. We discuss, at a general level, some of the issues involved in programming multi-agent and open, distributed systems, drawing on the recently-published AgentLink III Roadmap of Agent Based Computing Technologies.

Intelligent autonomous navigation for mobile robots: spatial concept acquisition and object discrimination

Abstract: An autonomous system able to construct its own navigation strategy for mobile robots is proposed. The navigation strategy is molded from navigation experiences (succeeding as the robot navigates) according to a classical reinforcement learning procedure. The autonomous system is based on modular hierarchical neural networks. Initially, the navigation performance is poor (many collisions occur). Computer simulations show that after a period of learning, the autonomous system generates efficient obstacle avoidance and target seeking behaviors. Experiments also offer support for concluding that the autonomous system develops a variety of object discrimination capability and of spatial concepts.

Intelligent Autonomous Navigation forMobile Robots: Spatial Concept Acquisition andObject Discrimination*

Abstract: Anautonomous system able toconstruct its own navigation strategy formobilerobotsisproposed. The navigation strategy ismoldedfromnavigation experiences (succeeding astherobotnavigates) according toa classical reinforcement learning procedure. Theautonomous system is basedonmodular hierarchical neural networks. Initially the navigation performance ispoor(manycollisions occur). Computer simulations showthatafter aperiod oflearning the autonomous system generates efficient obstacle avoidance and target seeking behaviors. Experiments alsooffer support for concluding thattheautonomous system develops avariety of object discrimination capability andofspatial concepts.

An Adaptive Control of an Autonomous Guided Vehicle System Using Cell-Mediated Immune Algorithm Controller and Vision Sensor

Abstract: In this paper, we proposed an adaptive control method which is named CMIA (cell-mediated immune algorithm) controller with PID scheme. It is based on specific immune response of the biological immune system which is the cell-mediated immune response. It is also applied for the autonomous guided vehicle (AGV) system which is manufactured in this paper. The AGV is used for the port automation to carry container without human and to overcome uncertainty and nonlinearity because of running in the outdoor. To verify the performance of the proposed CMIA controller, some experiments for the AGV system are performed. Finally, the experimental results for the control of steering and speed of an AGV system illustrate the effectiveness of the proposed control scheme. These results for the proposed method also show that it has better performance than other conventional controller design methods.

A standard intelligent system ontology

Abstract: The level of automation in combat vehicles being developed for the Army's objective force is greatly increased over the Army's legacy force. This automation is taking many forms in emerging vehicles; varying from operator decision aides to fully autonomous unmanned systems. The development of these intelligent vehicles requires a thorough understanding of all of the intelligent behavior that needs to be exhibited by the system so that designers can allocate functionality to humans and/or machines. Traditional system specification techniques focused heavily on the functional description of the major systems and implicitly assumed that a well-trained crew would operate these systems in a manner to accomplish the tactical mission assigned to the vehicle. In order to allocate some or all of these intelligent behaviors to machines in future vehicles it is necessary to be able to identify and describe these intelligent behaviors in detail. In this paper, we describe an effort to develop an intelligent systems (IS) ontology using Protege. The goal of this effort is to develop a common, implementation-independent, extendable knowledge source for researchers and developers in the intelligent vehicle community that will: * Provide a standard set of domain concepts along with their attributes and inter-relations * Allow for knowledge capture and reuse * Facilitate systems specification, design, and integration, and * Accelerate research in the field. This paper describes the methodology we have used to identify knowledge in this domain and an approach to capture and visualize the knowledge in the ontology.

Autonomous welding of large steel fabrications

Abstract: Purpose – Development and demonstration of an autonomous, mobile welding robot capable of fabricating large‐scale customised structures. Design/methodology/approach – An autonomous welding robot has been developed under the EC Framework V Growth program. The system comprises a global vision system for part location and orientation, and a robot transport vehicle (RTV) which carries a 6‐axis robot, robot controller, welding equipment, and local sensors at the welding torch. The RTV path, robot arm motion and weld process programming are performed automatically using sensors and specially customised simulation software. Findings – The technology developed within the project was demonstrated, in November 2004, to be capable of identifying and welding large scale customised structures as found in the earth moving equipment and bridge fabrication industries. Research limitations/implications – The project demonstrated that current sensor technology is capable of being applied successfully to autonomous robots, but further developments in sensor technology are required to improve accuracy and joint access. Practical implications – The NOMAD concept of autonomous mobile robots provides an alternative solution to welding mass customised structures. Originality/value – This project demonstrated, for the first time, the capability of autonomous robots to weld large scale customised structures.

Développement autonome des comportements de base d'un agent

Abstract: The problem addressed in this article is that of automatical ly designing autonomous agents having to solve complex tasks involving several -andpossibly concurrent- objectives. We propose a modular approach based on the principles of acti on selection where the actions recommanded by several basic behaviors are combined in a glo bal decision. In this framework, our main contribution is a method making an agent able to auto matically define and build the basic behaviors it needs through incremental reinforcemen t learning methods. This way, we obtain a very autonomous architecture requiring very few ha nd-coding. This approach is tested and discussed on a representative problem taken from the "ti le-world".

FPGA implementation of vision algorithms for small autonomous robots

Abstract: The use of on-board vision with small autonomous robots has been made possible by the advances in the field of Field Programmable Gate Array (FPGA) technology. By connecting a CMOS camera to an FPGA board, on-board vision has been used to reduce the computation time inherent in vision algorithms. The FPGA board allows the user to create custom hardware in a faster, safer, and more easily verifiable manner that decreases the computation time and allows the vision to be done in real-time. Real-time vision tasks for small autonomous robots include object tracking, obstacle detection and avoidance, and path planning. Competitions were created to demonstrate that our algorithms work with our small autonomous vehicles in dealing with these problems. These competitions include Mouse-Trapped-in-a-Box, where the robot has to detect the edges of a box that it is trapped in and move towards them without touching them; Obstacle Avoidance, where an obstacle is placed at any arbitrary point in front of the robot and the robot has to navigate itself around the obstacle; Canyon Following, where the robot has to move to the center of a canyon and follow the canyon walls trying to stay in the center; the Grand Challenge, where the robot had to navigate a hallway and return to its original position in a given amount of time; and Stereo Vision, where a separate robot had to catch tennis balls launched from an air powered cannon. Teams competed on each of these competitions that were designed for a graduate-level robotic vision class, and each team had to develop their own algorithm and hardware components. This paper discusses one team's approach to each of these problems.