An efficient strategy based on an artificial neural network is presented to enable a mobile robot to move autonomously and build a rough map of its environment. The paper describes a technique based on a neural networ...
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An efficient strategy based on an artificial neural network is presented to enable a mobile robot to move autonomously and build a rough map of its environment. The paper describes a technique based on a neural network which uses data from sonars to recognize features online. Our method is systematic, simple and yields very good results. Moreover, it is generalizable in the sense that one can train the robot to recognize particular features of interest. We have implemented and tested this technique successfully on-board a mobile robot which uses a 68332 processor and does not have support for floating point arithmetic and file structure. Once trained, the robot can recognize the features on the fly and react to them appropriately.
In this paper, we propose a method implementing real-time scheduling in the Unix, a general-purpose operating system (GPOS), by considering the deadline of real-time tasks. Our approach employs a special task called t...
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In this paper, we propose a method implementing real-time scheduling in the Unix, a general-purpose operating system (GPOS), by considering the deadline of real-time tasks. Our approach employs a special task called the scheduling daemon, which deals with all the real-time tasks in a system and makes just one task become a ready-to-run state and other sleep states. This causes the Unix scheduler to deal with only one real-time task as a rule, and thus bypasses time sharing-based scheduling strategy of the Unix. Therefore, all the real-time tasks are scheduled by the strategy of the scheduling daemon. We implement the scheduling daemon on the Unix, and build a real-time platform in order that application programmers develop real-timesystems more effectively. Moreover, we show the superiority of our approach through extensive experiments.
This paper proposes a genetic-based algorithm for surface reconstruction of three-dimension (3-D) objects from a group of contours representing its section plane lines. The algorithm can optimize the triangulation of ...
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This paper proposes a genetic-based algorithm for surface reconstruction of three-dimension (3-D) objects from a group of contours representing its section plane lines. The algorithm can optimize the triangulation of the surface of 3-D objects with a multi-objective optimization function to meet the needs of a wide range of applications. Further, a new crossover operator for triangulation and a new 3-D quadrilateral mutation operator are also introduced.
Generalisation is a non-trivial problem in machine learning and more so with neural networks which have the capabilities of inducing varying degrees of freedom. It is influenced by many factors in network design, such...
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Controllers for embedded dynamic systems require models with continuous behavior evolution and discrete configuration changes. These changes may cause fast continuous transients in state variables. time scale and para...
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Generalisation is a non-trivial problem in machine learning and more so with neural networks which have the capabilities of inducing varying degrees of freedom. It is influenced by many factors in network design, such...
Generalisation is a non-trivial problem in machine learning and more so with neural networks which have the capabilities of inducing varying degrees of freedom. It is influenced by many factors in network design, such as network size, initial conditions, learning rate, weight decay factor, pruning algorithms, and many more. In spite of continuous research efforts, we could not arrive at a practical solution which can offer a superior generalisation. We present a novel approach for handling complex problems of machine learning. A multiobjective genetic algorithm is used for identifying (near-) optimal subspaces for hierarchical learning. This strategy of explicitly partitioning the data for subsequent mapping onto a hierarchical classifier is found both to reduce the learning complexity and the classification time. The classification performance of various algorithms is compared and it is argued that the neural modules are superior for learning the localised decision surfaces of such partitions and offer better generalisation.
Presents a technique to accurately estimate the state of a robot helicopter using a combination of gyroscopes, accelerometers, inclinometers and GPS. Simulation results of state estimation of the helicopter are presen...
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Presents a technique to accurately estimate the state of a robot helicopter using a combination of gyroscopes, accelerometers, inclinometers and GPS. Simulation results of state estimation of the helicopter are presented using Kalman filtering based on sensor modeling. The number of estimated states of helicopter is nine : three attitudes(/spl theta/,/spl phi/,/spl psi/) from the gyroscopes, three accelerations(x/spl ***/,y/spl ***/,z/spl ***/) and three positions (x, y, z) from the accelerometers. Two Kalman filters were used, one for the gyroscope data and the other for the accelerometer data. Our approach is unique because it explicitly avoids dynamic modeling of the system and allows for can elegant combination of sensor data available at different frequencies. We also describe the larger context in which this work is embedded, namely the design and implementation of an autonomous robot helicopter.
Task graphs have been used for the specification and analysis of realtimesystems for some time. We are currently developing a mobile code management system that uses such a representation for specification, analysis...
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Task graphs have been used for the specification and analysis of realtimesystems for some time. We are currently developing a mobile code management system that uses such a representation for specification, analysis, and execution time scheduling, dispatch, and resource allocation decisions. We describe our three level limited hierarchical task graph model. We discuss graph topology, the use of task graphs for both specification and description, and planned software engineering tools that will take advantage of our model. We concentrate on the use of task graphs to model iteration, branching structures, optional execution, and parallelism within a function execution.
The design of an assembly line for a mechanical assembly has to satisfy all the geometrical and the physical constraints presented in the assembly such as blocking relation and contact forces. Given a desired final as...
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The design of an assembly line for a mechanical assembly has to satisfy all the geometrical and the physical constraints presented in the assembly such as blocking relation and contact forces. Given a desired final assembly, determining an appropriate assembly sequence which satisfies not only the blocking constraints but also the force constraints needs a proper model to directly provide the information needed to reason about assembly. We introduce a representation, the directional force graph, which explicitly describes how parts interact in the assembly. This representation is used to determine disassemblable parts that are naturally broken apart from an assembly by a decomposition force. A decomposition force is an external force applied to a held part(s) against a fixture part(s). The proposed approach provides a mechanism to explicitly incorporate the real-world constraints of assembly directions, fixturing, and tooling requirements into planning. We describe a complete planning algorithm and report the results.
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