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In this paper, we present a method to improve the navigation of tethered underwater vehicles by computing optimal paths that prevent their tethers from becoming entangled in obstacles. To accomplish this, we define the Non-Entangling Travelling Salesperson Problem (NE-TSP) as an extension of the Travelling Salesperson Problem with a non-entangling constraint. We compute the optimal solution to the...
This paper mainly studied the online MWA (Mould Width Adjustment) speed curve algorithm. It's based on the variable speed and taper movement trajectory. It analyses the defect of current adjustable width trajectory and puts forward the adjustable movement trajectory automatically according to the adjustment range. Online MWA speed curve presetting simulation system is developed against the proposed...
Autonomous robot navigation through unknown, cluttered environments at high-speeds is still an open problem. Quadrotor platforms with this capability have only begun to emerge with the advancements in light-weight, small form factor sensing and computing. Many of the existing platforms, however, require excessive computation time to perform collision avoidance, which ultimately limits the vehicle's...
Understanding human behavior is crucial for planning evacuation strategies when an emergency occurs. The social force model, which is a successful quantitative model, has been widely used in investigating human behavior. In this paper, we propose a gradient descent based parameter optimization method to learn the parameters of the social force model from experimental data. Although the original social...
A novel learning Model Predictive Control technique is applied to the autonomous racing problem. The goal of the controller is to minimize the time to complete a lap. The proposed control strategy uses the data from previous laps to improve its performance while satisfying safety requirements. A system identification technique is proposed to estimate the vehicle dynamics. Simulation results with the...
Model Predictive planning and control algorithms based on A*-type graph search techniques achieve computationally fast and nearly optimal solutions when they use a cost-to-goal (or “heuristic”) function, i.e. an estimate of the cost from the current state to the goal state, that correlates well with the actual optimal cost-to-goal values. Compared to search methods without a cost-to-goal estimate,...
This paper investigates the trajectory control of a very flexible flying wing model, which is derived from geometrically-nonlinear beam theory using intrinsic structural description in [1]. This model is coupled with structural dynamics, aeroelastic dynamics and flight dynamics. The control design is using a two-loop LADRC (linear active disturbance rejection control) and H∞ scheme in both the longitudinal...
Motion planning for multi-target autonomous search requires efficiently gathering as much information over an area as possible with an imperfect sensor. In disaster scenarios and contested environments the spatial connectivity may unexpectedly change (due to aftershock, avalanche, flood, building collapse, adversary movements, etc.) and the flight envelope may evolve as a known function of time to...
We present an approach to learning control policies for physical robots that achieves high efficiency by adjusting existing policies that have been learned on similar source systems, such as a similar robot with different physical parameters, or an approximate dynamics model simulator. This can be viewed as calibrating a policy learned on a source system, to match a desired behaviour in similar target...
Robotic agents that do everyday manipulation tasks can hugely benefit from being able to predict consequences of their actions just before the execution. However, such a simulation technique is usually computationally-expensive and may not be achieved with agents' self computing power. For this problem, cloud robotics may offer a solution. Cloud robotics is an emerging field in the intersection of...
Understanding visual attention has always been a topic of great interest in the graphics, image/video processing, robotics and human-computer interaction communities. By understanding salient image regions, the compression, transmission and rendering algorithms can be optimized. This is particularly important in omnidirectional images (ODIs) viewed with a head-mounted display (HMD), where only a fraction...
Accidents at intersections are highly related to the driver's mis-decision while performing turning and merging maneuvers. This paper proposes a merging/turning controller for an automated vehicle, called the ego vehicle, which avoids collisions with surrounding (target) vehicles. An optimization-based control problem is defined based on receding horizon control, that parameterizes the system trajectory...
Among the modern requirements for complex object control simulators is to provide the properties of adaptability, flexibility, extensibility, reliability, security and reconfigurability. The use of semantic description as one of the ways to solve the problem of increasing adaptability and efficiency (productivity, reliability, security, reconfigurability) in the development of simulators with human-machine...
Recent years witnessed popular use of various mobile devices, e.g., smart phones, vehicle networks and wearable watches. Such mobile devices generate massive trajectory data, and literature have proposed various algorithms to leverage the trajectory data for map inference. Unfortunately, such algorithms are hard to achieve both high map quality and computation efficiency. In this paper, we propose...
Studying transient properties of nonlinear systems is an important problem for safety applications. Computationally, it is a very challenging problem to verify that a nonlinear system satisfies a safety specification. Therefore, in many cases, engineers try to solve a related problem, i.e., they try to find a system behavior that does not satisfy a given specification. This problem is called specification...
Acquiring Atomic Force Microscope images using compressed sensing requires the piezo X−Y stage to track a sequence of step commands. To achieve fast tracking of such commands requires a precise system model. We show that once such a model is obtained, standard linear feedback can be used to achieve excellent tracking of step inputs. The system under consideration has a significant amount of time delay,...
Nonlinear model structures based on multiple linear models, which are overblended by validity functions (Local Model Networks) have proven to be successful for many examples in nonlinear system identification. Here a novel method for computing a suboptimal solution of the model predictive control (MPC) problem for local model networks with a hierarchical structure is developed. Therefore a representation...
Traditional location-based service profiles user's traits by looking for patterns in historical mobility behaviors. Yet, from time to time, people are adventurous and would often like to go to unvisited places, or follow new transition paths. At that time, their next movements will be inconsistent with any previous patterns, making location-based recommendations inaccurate and irrelevant to user's...
This paper presents experimental results of a high-level trajectory planning algorithm for autonomous quadrotors based on Model Predictive Control (MPC) tuned with machine learning. Time-varying planar inequality constraints are used to avoid obstacles. The nonlinear plant dynamics are linearized around a hover condition. Learning Automata is used to select the relative weights of the objective function...
This article delves into time scaling for a high-order and very fast dynamic system. A very fast system responds to a test input (e.g. a unit step input) with a very small settling time or rise time in the transient time evolution and its settling time or rise time could be in the scale of milliseconds or microseconds, and the positioning or tracking of a very fast system could be challenging. In...
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