We discuss how to classify an open systems problem and automated action plan determination. In the current situation where problems of open systems are a regular occurrence, there is a strong demand for automation of ...
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ISBN:
(纸本)9781665499248
We discuss how to classify an open systems problem and automated action plan determination. In the current situation where problems of open systems are a regular occurrence, there is a strong demand for automation of failure action plans. First, we propose the way how to evaluate and classify all problems that happens in open systems. Next, we provide a way to link problem classes to unique action plan. This enables automated action plan determination. Finally, we analyze the relation of wrong DOA detection and inappropriate action plan determination.
The Spring-Loaded Inverted Pendulum (SLIP) is one of the simplest models of robot locomotion. SLIP is commonly used to predict the center of mass motion and derive simple control laws for stable locomotion. However, t...
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ISBN:
(纸本)9781665491907
The Spring-Loaded Inverted Pendulum (SLIP) is one of the simplest models of robot locomotion. SLIP is commonly used to predict the center of mass motion and derive simple control laws for stable locomotion. However, the SLIP model is not integrable, which means that no closed-form relation can be derived to understand how the design and control parameters of the SLIP model affect stable locomotion. There exist a number of different analytical approximations to the SLIP model when considering small step lengths and symmetric steps. In this paper, we present a novel approximation to the SLIP model without relying on the small step length and the symmetric step assumption. The model was found to accurately predict the stability of the SLIP model for large and asymmetric steps and was used to design a controller to stabilize the SLIP model in a couple of steps.
Electric Vehicles are replacing the conventional combustion engines vehicles due to a number of advantages like less emissions, use of cleaner fuel etc at a high pace. They work on batteries which are basically operat...
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In today's world, a lot people are searching and applying for jobs. When there is a need for company to recruit new employees, it is difficult for them to select the correct resume according to their job descripti...
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The paper introduces a novel framework for safe and autonomous aerial physical interaction in industrial settings. It comprises two main components: a neural network-based target detection system enhanced with edge co...
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ISBN:
(纸本)9798350357899;9798350357882
The paper introduces a novel framework for safe and autonomous aerial physical interaction in industrial settings. It comprises two main components: a neural network-based target detection system enhanced with edge computing for reduced onboard computational load, and a control barrier function (CBF)-based controller for safe and precise maneuvering. The target detection system is trained on a dataset under challenging visual conditions and evaluated for accuracy across various unseen data with changing lighting conditions. Depth features are utilized for target pose estimation, with the entire detection framework offloaded into low-latency edge computing. The CBF-based controller enables the UAV to converge safely to the target for precise contact. Simulated evaluations of both the controller and target detection are presented, alongside an analysis of real-world detection performance.
Autonomous driving's Planning-and-control (PnC) integration demands alignment in vehicle motion feasibility and motion error predictability, which requires the motion controller to respect realistic vehicle system...
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ISBN:
(纸本)9798350399462
Autonomous driving's Planning-and-control (PnC) integration demands alignment in vehicle motion feasibility and motion error predictability, which requires the motion controller to respect realistic vehicle system constraints and dynamic properties. This paper describes a Model Predictive control (MPC) method that practically handles the system challenges in vehicle longitudinal dynamic control, introduced by complex torque capacity shapes, system switching by gear shifts, and multiple actuation systems. Techniques of constraint local affine approximation, wheel and actuator domain separation, and fuel mapping blending are invented to address the aforementioned challenges, leading to quasi-optimal control solution using minimal computation time. Through formulating the control problem into constrained multi-objective optimizations, product & functional requirements involved in autonomous driving, such as tracking response, safety constraints, fuel economy, ride comfort, are conveniently handled and explicitly satisfied over a wide range of scenarios using a single control core solver. This controller has been sufficiently validated and supports TuSimple's class-8 truck autonomous driving operations in real traffic of Arizona and Texas in USA.
In post-disaster scenarios, field robots and unmanned aerial vehicles (UAVs) are effective tools to provide an ondemand response and guarantee the safety of humans. Sensors, field robots, and the devices equipped by U...
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Deploying edge servers on Unmanned Aerial Vehicles (UAVs) has become a promising strategy to handle the spatiotemporally varying user demands on ground. However, a critical limitation of the UAVs is the limited flight...
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Automatic feature extraction from medical images is one of the most important aspects of early disease detection and treatment. This work presents a novel method for autonomously extracting features from diabetic reti...
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The efficient operation of large-scale Cable-Driven Parallel Robots (CDPRs) relies on precise calibration of kinematic parameters and the simplicity of the calibration process. This paper presents a graph-based self-c...
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ISBN:
(纸本)9798350377712;9798350377705
The efficient operation of large-scale Cable-Driven Parallel Robots (CDPRs) relies on precise calibration of kinematic parameters and the simplicity of the calibration process. This paper presents a graph-based self-calibration framework that explicitly addresses cable sag effects and facilitates the calibration procedure for large-scale CDPRs by only relying on internal sensors. A unified factor graph is proposed, incorporating a catenary cable model to capture cable sagging. The factor graph iteratively refines kinematic parameters, including anchor point locations and initial cable length, by considering jointly onboard sensor data and the robot's kineto-static model. The applicability and accuracy of the proposed technique are demonstrated through Finite Element (FE) simulations, on both large and small-scale CDPRs subjected to significant initialization perturbations.
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