This paper conducts simulations of reformation of mobile agents structure with analysis and provides a primitive but new method for automatic collision control. This dynamics is based on synchronization of coupled cha...
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This paper conducts simulations of reformation of mobile agents structure with analysis and provides a primitive but new method for automatic collision control. This dynamics is based on synchronization of coupled chaotic oscillators. The author applies this for agent simulator and verifies the performance by the agent's standpoint of view.
The job shop scheduling problem is demonstrated to be one of the NP-complete problems. Many meta-heuristics are proposed to solve this problem. In this paper, wolf pack algorithm is applied to this problem. This algor...
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ISBN:
(纸本)9781728133263
The job shop scheduling problem is demonstrated to be one of the NP-complete problems. Many meta-heuristics are proposed to solve this problem. In this paper, wolf pack algorithm is applied to this problem. This algorithm simulates predation behavior and prey allocation mode of wolf pack, providing a new method to solve the job shop scheduling problem. Due to the discrete characteristics of the scheduling solutions, some adjustments are added to develop a discrete wolf pack algorithm (DWPA). Also a computational result shows that DWPA has a good performance on the time efficiency and accuracy compared with other algorithms.
Manufacturing and mining automation, robotics, swarms and smart device networks are often implemented upon distributed embedded systems. These systems are typically statically distributed, coarsely reconfigurable or d...
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ISBN:
(纸本)0889864810
Manufacturing and mining automation, robotics, swarms and smart device networks are often implemented upon distributed embedded systems. These systems are typically statically distributed, coarsely reconfigurable or deployed on homogeneous networks. A conceptual stack can be formed using modelling languages, system performance analysis and optimisation and reconfigurable platform-neutral components to overcome these problems. A framework for computationalintelligence-based applications to be built upon this stack has been proposed. Model-Driven Architecuture has been shown to be a promising standard for the modelling, design and development of embedded applications. The Theory of Constraints is proposed as a potential technique for performance analysis and optimisation of distributed systems. Mobile Agents and code mobility can be used in the component architecture to allow for adaptation and reconfiguration for optimisation.
A binocular vision system having the ability of actively tracking moving objects is designed and implemented. Management software is also developed for further study. Then the crucial technologies concerned with activ...
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We present a model predictive controller (MPC) that automatically discovers collision-free locomotion while simultaneously taking into account the system dynamics, friction constraints, and kinematic limitations. A re...
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ISBN:
(纸本)9781728190778
We present a model predictive controller (MPC) that automatically discovers collision-free locomotion while simultaneously taking into account the system dynamics, friction constraints, and kinematic limitations. A relaxed barrier function is added to the optimization's cost function, leading to collision avoidance behavior without increasing the problem's computational complexity. Our holistic approach does not require any heuristics and enables legged robots to find whole-body motions in the presence of static and dynamic obstacles. We use a dynamically generated euclidean signed distance field for static collision checking. Collision checking for dynamic obstacles is modeled with moving cylinders, increasing the responsiveness to fast-moving agents. Furthermore, we include a Kalman filter motion prediction for moving obstacles into our receding horizon planning, enabling the robot to anticipate possible future collisions. Our experiments) demonstrate collision-free motions on a quadrupedal robot in challenging indoor environments. The robot handles complex scenes like overhanging obstacles and dynamic agents by exploring motions at the robot's dynamic and kinematic limits.
Human interaction involves very sophisticated non-verbal communication skills like understanding the goals and actions of others and coordinating our own actions accordingly. Neuroscience refers to this mechanism as m...
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ISBN:
(纸本)9781728190778
Human interaction involves very sophisticated non-verbal communication skills like understanding the goals and actions of others and coordinating our own actions accordingly. Neuroscience refers to this mechanism as motor resonance, in the sense that the perception of another persons actions and sensory experiences activates the observer's brain as if (s)he would be performing the same actions and having the same experiences. We analyze and model the non-verbal cues exchanged between two humans in handover actions. The contributions of this paper are the following: (i) computational models, using recorded motion data, describing the motor behaviour of each actor in action-in-interaction situations;(ii) a computational model that captures the behaviour of the "giver" and "receiver" during an object handover action, by coupling the wrist kinematic motion of the actors;and (iii) the transfer of these models to the iCub robot for both action execution and recognition. Our results show that: (i) the robot is able to interpret the human wrist motion and infer whether or not the observed action is an "handover";and (ii) use the motor resonance model to coordinate its actions with the human partner, during handover actions.
Present paper continues the researches on cognitive system design. The goal of the paper is to illustrate the variety of models which can be constructed using the Bayesian plausible reasoning theory. The first case st...
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Present paper continues the researches on cognitive system design. The goal of the paper is to illustrate the variety of models which can be constructed using the Bayesian plausible reasoning theory. The first case study develops a classical differential model into a Bayesian model. The second case study solves a geometry problem by plausible reasoning. The third case study models the human reasoning presented by the famous story of Sun Tzu: 'Advance to Chengang by a hidden path'.
Creating an aerodynamic shape, like an airfoil wing, requires many factors to be considered, especially aerodynamic properties such as its lift-to-drag ratio (L/D). Currently, generating feasible airfoil shapes usuall...
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ISBN:
(纸本)9781665476874
Creating an aerodynamic shape, like an airfoil wing, requires many factors to be considered, especially aerodynamic properties such as its lift-to-drag ratio (L/D). Currently, generating feasible airfoil shapes usually requires computationally expensive tools, such as computational Fluid Dynamics (CFD). In recent years, increasing work has been directed to utilizing machine learning algorithms to synthesize accurate airfoil shapes while reducing the required computational cost. Generative Adversarial Network (GAN) is one of many algorithms to see success in airfoil shape optimization and is shown to generate good airfoils given a small set of training examples. This paper focuses on implementing a conditional GAN (cGAN) based framework with various filters for airfoil inverse design problem. By labelling the training dataset with aerodynamic characteristics separated by pre-defined thresholds to lift-to-drag ratio (L/D) and shape area, the class labels will be able to guide the network to generate different classes of airfoils influenced by these characteristics. Together with layers of Savitzky-Golay (SG) filter and B-Spline Interpolation, the developed model was shown to achieve good performance in generating new airfoils. In addition, we explored the viability of adding Wasserstein loss from Wasserstein GAN into the network architecture, forming a cWGAN-GP. Testing results showed that cWGAN-GP was able to achieve better performance for a specific airfoil class.
In this paper, we proposed a novel path replanning algorithm on arbitrary graphs. To avoid computationally heavy preprocessing and to reduce required memory to store the expanded vertices of the previous search, we de...
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ISBN:
(纸本)9781728190778
In this paper, we proposed a novel path replanning algorithm on arbitrary graphs. To avoid computationally heavy preprocessing and to reduce required memory to store the expanded vertices of the previous search, we defined the feature vertices, which are extracted from the previous path by a simple algorithm to compare the costs between adjacent vertices along the path once. Proper additional heuristic functions are designed for these feature vertices to work as local attractors guiding the search toward the previous path's neighbors. To avoid unnecessary expansions and speed up the search, these additional heuristic functions are properly managed to stop intriguing or guiding search toward the feature vertices. The proposed algorithm of Fast Replanning Multi-Heuristic A* is a variation of Shared Multi-Heuristic A* while removing or deactivating the additional heuristic functions during the search. Fast Replanning Multi-Heuristic A* guarantees the bounded suboptimality while efficiently exploring the graph toward the goal vertex. The performance of the proposed algorithm was compared with weighted A* and D* lite by simulating numerous path replanning problems in maze-like maps.
We consider typical scenarios where an autonomous multi-robot team is used for surveying a large region. The desired output is a spatial map of the physical values of interest. Accounting for spatial correlation and u...
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ISBN:
(纸本)9781728190778
We consider typical scenarios where an autonomous multi-robot team is used for surveying a large region. The desired output is a spatial map of the physical values of interest. Accounting for spatial correlation and uncertainty, the map is modeled using a Gaussian Process. Considering real-world constraints such as limited time budget and collision avoidance, we model team's mission as a joint informative path planning problem which is tackled using a leader-follower architecture balancing centralized and fully distributed computation of plans. The leader first identifies a convex containment region that is to be sampled by the team. Next, through a combination of Bayesian optimization and Monte Carlo simulation, distinct sampling locations are identified and assigned to the followers. Each follower independently solves an orienteering problem to find a collision-free path maximizing information gain. A team-level adaptive replanning criterion is designed to keep redirecting sampling towards the most informative regions. The algorithm has been validated in computational experiments for map estimation. Compared to a baseline reference algorithm, it has shown a significantly higher accuracy. Moreover, the approach has shown good ability to support network connectivity, as well as good scalability in computation.
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