This article presents a novel algorithm for multiple odor source localization by a multi-robot system based on a virtual cancelation plume approach. The proposed method is based on rendering a previously declared odor...
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(纸本)9781467363563
This article presents a novel algorithm for multiple odor source localization by a multi-robot system based on a virtual cancelation plume approach. The proposed method is based on rendering a previously declared odor source invisible to the robots so that the declared source and the odor plume it generates do not interfere with the effects of other existing plumes, allowing the localization of the remaining sources. Exploration and plume tracking by the robots is achieved using a decentralized asynchronous particle swarm optimization algorithm. The divergence operator is used to declare the odor sources. A set of simulations and real world experiments are performed on two different scenarios on a controlled environment using a swarm of 5 robots to validate the proposed methodology. Results show that the virtual plume cancelation algorithm can be successfully used to find multiple odor sources, even when two plumes overlap. It can also extend the operation of many odor source localization algorithms developed for single source localization.
The paper describes a substantial extension of Norm Optimal Iterative Learning Control (NOILC) that permits tracking of a class of finite dimensional reference signals whilst simultaneously minimizing a quadratic cost...
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This paper investigates several control strategies that potential to perform well in regulating and tracking set point of pneumatic actuator system and able to reject disturbance. The system consists of 5-port proport...
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This paper investigates several control strategies that potential to perform well in regulating and tracking set point of pneumatic actuator system and able to reject disturbance. The system consists of 5-port proportional valve with the dead-band flow and double rod cylinders that exhibit significant friction. Two control strategies of PID and NPID controllers with four different configurations with and without dead-zone compensators (DZC) are simulated. Three different input signals including step, sinusoidal and random waveforms are used to evaluate the performance of the proposed techniques. The effectiveness of NPID+DZC has been successfully demonstrated and proved through simulation and experimental studies.
The Iterative Learning Control (ILC) problem in which tracking is only required at a subset of isolated time points along the trial duration has recently gained significant attention since it addresses the practical n...
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The paper describes a substantial extension of Norm Optimal Iterative Learning Control (NOILC) that permits tracking of a class of finite dimensional reference signals whilst simultaneously minimizing a quadratic cost...
The paper describes a substantial extension of Norm Optimal Iterative Learning Control (NOILC) that permits tracking of a class of finite dimensional reference signals whilst simultaneously minimizing a quadratic cost function. Motivated by practical problems in automation and control, this enables point-to-point motion tasks to be performed whilst reducing effects such as payload spillage, vibration tendencies and actuator wear. Solutions combine feedforward and feedback actions, and are experimentally tested using a robotic arm.
The Iterative Learning Control (ILC) problem in which tracking is only required at a subset of isolated time points along the trial duration has recently gained significant attention since it addresses the practical n...
The Iterative Learning Control (ILC) problem in which tracking is only required at a subset of isolated time points along the trial duration has recently gained significant attention since it addresses the practical needs of many *** paper extends the framework by embedding simultaneous iterative convergence of subsets of outputs to reference trajectories on subintervals. This enables it to tackle tasks which mix ‘point to point’ movements with linear tracking requirements, which substantially broadens the application domain (e.g. to include automation tasks which include welding or cutting movements, or human motion control where the movement is restricted by the task to straight line and/or planar segments). A solution to the problem is presented in the framework of Norm Optimal ILC (NOILC), providing well-defined convergence properties, design guidelines and supporting experimental results.
Recently, haptics has begun to impact consumer products, e.g., mobile phones and automobiles. In this paper, we introduce one such new application, that is, haptic simulation of refrigerator operation, and present an ...
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Robots and robotics are becoming more complex and flexible, due to technological advancement, improved sensing capabilities and machine intelligence. Service robots target a wide range of applications, relying on adva...
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Robots and robotics are becoming more complex and flexible, due to technological advancement, improved sensing capabilities and machine intelligence. Service robots target a wide range of applications, relying on advanced Human-Robot Interaction. Medical robotics is becoming a leading application area within, and the number of surgical, rehabilitation and hospital assistance robots is rising rapidly. However, the complexity of the medical environment has been a major barrier, preventing a wider use of robotic technology, thus mostly teleoperated, human-in-the-loop control solutions emerged so far. Providing smarter and better medical robots requires a systematic approach in describing and translating human processes for the robots. It is believed that ontologies can bridge human cognitive understanding and robotic reasoning (machine intelligence). Besides, ontologies serve as a tool and method to assess the added value robotic technology brings into the medical environment. The purpose of this paper is to identify relevant ontology research in medical robotic, and to review the state-of-the-art. It focuses on the surgical domain, fundamental terminology and interactions are described for two example applications in neurosurgery and orthopaedics.
We propose an approach for dependence tree structure learning via copula. A nonparametric algorithm for copula estimation is presented. Then a Chow-Liu like method based on dependence measure via copula is proposed to...
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We propose an approach for dependence tree structure learning via copula. A nonparametric algorithm for copula estimation is presented. Then a Chow-Liu like method based on dependence measure via copula is proposed to estimate maximum spanning bivariate copula associated with bivariate dependence relations. The main advantage of the approach is that learning with empirical copula focuses on dependence relations among random variables, without the need to know the properties of individual variables as well as without the requirement to specify parametric family of entire underlying distribution for individual variables. Experiments on two real-application data sets show the effectiveness of the proposed method.
In this paper, an efficient real-time autonomous driving motion planner with trajectory optimization is proposed. The planner first discretizes the plan space and searches for the best trajectory based on a set of cos...
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In this paper, an efficient real-time autonomous driving motion planner with trajectory optimization is proposed. The planner first discretizes the plan space and searches for the best trajectory based on a set of cost functions. Then an iterative optimization is applied to both the path and speed of the resultant trajectory. The post-optimization is of low computational complexity and is able to converge to a higher-quality solution within a few iterations. Compared with the planner without optimization, this framework can reduce the planning time by 52% and improve the trajectory quality. The proposed motion planner is implemented and tested both in simulation and on a real autonomous vehicle in three different scenarios. Experiments show that the planner outputs high-quality trajectories and performs intelligent driving behaviors.
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