In this paper, we present the design and fabrication of a bio-inspired caudal fin actuator for propulsion and maneuvering purposes in a fish-like robot. Shape memory alloy (SMA) composite actuator is customized to pro...
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ISBN:
(纸本)9789897581236
In this paper, we present the design and fabrication of a bio-inspired caudal fin actuator for propulsion and maneuvering purposes in a fish-like robot. Shape memory alloy (SMA) composite actuator is customized to provide the necessary work out for the caudal fin. The pocket holes guide, electrical wiring and attachment pads for SMA actuators are all embedded in a single layer of cellulose acetate film, sandwiched between two layers of silicone rubber. Instead of using joints, four SMAs are fixed along the soft structure of the caudal fin and bend this to a certain mode shape. The caudal fin actuator was inspired by Largemouth Bass, which uses sub-carangiform mode swimming and the caudal fin during steady swimming and maneuvering.
A radically new approach will be described for the fully distributed and dynamic management of advanced crisis relief operations and missions. It is based on the installation of a universal "social" module i...
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ISBN:
(纸本)9728865597
A radically new approach will be described for the fully distributed and dynamic management of advanced crisis relief operations and missions. It is based on the installation of a universal "social" module in many existing and massively used data processing and control devices, including (but not limited to) internet hosts, laptops, mobile robots and mobile phones. These modules can collectively interpret a special scenario language while exchanging higher-level program code with accompanying data and control in parallel. This can dynamically integrate any scattered post-disaster human and technical resources into an operable distributed system which, from one side, is effectively supervised externally, and from the other side, is capable of solving complex self-analysis, coordination, survivability, relief, and reconstruction problems autonomously.
This paper proposes a Particle-Filter approach and a set of motion strategies to cooperatively localize a team of three robots. The allocated mission consists on the path following of a closed trajectory and obstacle ...
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ISBN:
(纸本)9728865600
This paper proposes a Particle-Filter approach and a set of motion strategies to cooperatively localize a team of three robots. The allocated mission consists on the path following of a closed trajectory and obstacle avoidance in isolated and unstructured scenarios. The localization methodology required for the correct path following relies on distance and orientation measurements among the robots and the robots and a fixed active beacon. Simulation results are presented.
Refined algorithms for solving continuous-time algebraic Riccati equations using Newton's method with or without line search are discussed. Their main properties are briefly presented. Algorithmic details incorpor...
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ISBN:
(纸本)9789897580390
Refined algorithms for solving continuous-time algebraic Riccati equations using Newton's method with or without line search are discussed. Their main properties are briefly presented. Algorithmic details incorporated in the developed solver are described. The results of an extensive performance investigation on a large collection of examples are summarized. Several numerical difficulties and observed unexpected behavior are reported. These algorithms are strongly recommended for improving the solutions computed by other solvers.
This paper presents the application of Wavelet based State Dependent Parameter (WSDP) models to the identification of a Continuous Stirred-Tank Reactor (CSTR). Here, in order to characterize the process dynamics which...
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ISBN:
(纸本)9788993215038
This paper presents the application of Wavelet based State Dependent Parameter (WSDP) models to the identification of a Continuous Stirred-Tank Reactor (CSTR). Here, in order to characterize the process dynamics which is highly nonlinear and multi-variable dependent, 2-D WSDP model is used. The simulation results demonstrate the effectiveness and advantages of the identified model.
The study investigates the effectiveness of 2 variations of Particle Swarm Optimization (PSO) called Area Extended PSO (AEPSO) and Cooperative AEPSO (CAEPSO) in simulated robotic environments affected by a combinatori...
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ISBN:
(纸本)9789898565709
The study investigates the effectiveness of 2 variations of Particle Swarm Optimization (PSO) called Area Extended PSO (AEPSO) and Cooperative AEPSO (CAEPSO) in simulated robotic environments affected by a combinatorial noise. Knowledge Transfer, the use of the expertise and knowledge gained from previous experiments, can improve the robots decision making and reduce the number of wrong decisions in such uncertain environments. This study investigates the impact of transfer learning on robots' performance in such hostile environment. The results highlight the feasibility of CAEPSO to be used as the controller and decision maker of a swarm of robots in the simulated uncertain environment when gained expertise from past training is transferred to the robots in the testing phase.
Deep Learning experiments commonly require hundreds of trials to properly train neural networks, often labeled as Big Data, while Bayesian learning leverages scarce data points to infer next iterations, also known as ...
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ISBN:
(纸本)9789897585227
Deep Learning experiments commonly require hundreds of trials to properly train neural networks, often labeled as Big Data, while Bayesian learning leverages scarce data points to infer next iterations, also known as Micro Data. Deep Bayesian Learning combines the complexity from multi-layered neural networks to probabilistic inferences, and it allows a robot to learn good policies within few trials in the real world. In here we propose, for the first time, an application of Deep Bayesian Reinforcement Learning (RL) on a realworld multi-robot confrontation game, and compare the algorithm with a model-free Deep RL algorithm, Deep Q-Learning. Our experiments show that DBRL significantly outperforms DRL in learning efficiency and scalability. The results of this work point to the advantages of Deep Bayesian approaches in bypassing the Reality Gap and sim-to-real implementations, as the time taken for real-world learning can quickly outperform data-intensive Deep alternatives.
To date, most autonomous micro air vehicles (MAV-s) operate in a controlled environment, where the location of and attitude of the aircraft are measured be dedicated high-power computers with IR tracking capability. I...
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ISBN:
(纸本)9789897581496
To date, most autonomous micro air vehicles (MAV-s) operate in a controlled environment, where the location of and attitude of the aircraft are measured be dedicated high-power computers with IR tracking capability. If MAV-s are to ever exit the lab and carry out autonomous missions, their flight control systems needs to utilize on-board sensors and high-efficiency attitude determination algorithms. To address this need, we investigate the feasibility of using body schemas to carry out path planning in the vision space of the MAV. Body schemas are a biologically-inspired approach, emulating the plasticity of the animal brains, allowing efficient representation of non-linear mapping between the body configuration space, i.e. its generalized coordinates and the resulting sensory outputs. This paper presents a numerical experiment of generating landing trajectories of a miniature rotor-craft using the notion of body and image schemas. More specifically, we demonstrate how a trajectory planning can be executed in the image space using a pseudo-potential functions and a gradient-based maximum seeking algorithm. It is demonstrated that a neural-gas type neural network, trained through Hebbian-type learning algorithm can learn a mapping between the rotor-craft position/attitude and the output of its vision sensors. Numerical simulations of the landing performance of a physical model is also presented, The resulting trajectory tracking errors are less than 8 %.
The main contribution of this article the implementation of a new method for the usage of submaping algorithms, which surpasses the limitations arising from the need of statistical independence between the submaps, wh...
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ISBN:
(纸本)9781467307024
The main contribution of this article the implementation of a new method for the usage of submaping algorithms, which surpasses the limitations arising from the need of statistical independence between the submaps, when mapping large scale areas. This method relies on the commonly used structure of SLAM (Simultaneous Localization and Mapping) mandatory for building submaps which share information, but stay conditionally independent. This approach is able to recover the final map, without introducing other approximations ignoring the ones already introduced by the EKF (Extended Kalman Filter), all this in a linear time. The algorithm has been tested for the case of local submaps because the effects of linearization errors are smaller in this way. The obtained maps are more accurate and consistent compared with ones obtained by EKF or EIF (Extended Information Filter) methods, which are not using local coordinates.
An overview is given on the integration of product modeling with robot control. A method to enhance application of geometric and other product and production equipment related information in robot control is introduce...
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An overview is given on the integration of product modeling with robot control. A method to enhance application of geometric and other product and production equipment related information in robot control is introduced. Focus is on creation and application of model data information understandable by robot controls, on communication of model data with robot environments through the Internet, and finally on some related telerobotics issues.
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