The throughput of robotized pick and place cells can be improved by scheduling algorithms that determine optimal task sequences using estimates of the robot's cycle times;the latter however depend on factors such ...
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The throughput of robotized pick and place cells can be improved by scheduling algorithms that determine optimal task sequences using estimates of the robot's cycle times;the latter however depend on factors such as the robot's dynamics, the motion planning algorithm, and the specific configuration of the task, which is often not known in advance. Exact evaluation of the task times is therefore a computationally expensive procedure that should be performed as sparingly as possible during real time calculations. In this paper a recently developed architecture for the efficient approximation of a manipulator's task times is applied to a 3-DOF Clavel's Delta robot. Such an architecture is composed of a dynamic and kinematic model of the robot, of an optimizing motion planner, and of a neural network able predict the task times quickly enough to be used in online process-optimizing scheduling algorithms.
Deep reinforcement learning (DRL) is an approach by which an agent can learn to solve complex sequential tasks in a try-and-error manner. As such, it has been widely explored in robotic domain for solving manipulation...
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Autonomous vehicles include driverless, self-driving and robotic cars, and other platforms capable of sensing and interacting with its environment and navigating without human help. On the other hand, semiautonomous v...
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Autonomous vehicles include driverless, self-driving and robotic cars, and other platforms capable of sensing and interacting with its environment and navigating without human help. On the other hand, semiautonomous vehicles achieve partial realization of autonomy with human intervention, for example, in driver-assisted vehicles. Autonomous vehicles first interact with their surrounding using mounted sensors. Typically, visual sensors are used to acquire images, and computer vision techniques, signal processing, machine learning, and other techniques are applied to acquire, process, and extract information. The control subsystem interprets sensory information to identify appropriate navigation path to its destination and action plan to carry out tasks. Feedbacks are also elicited from the environment to improve upon its behavior. To increase sensing accuracy, autonomous vehicles are equipped with many sensors [light detection and ranging (LiDARs), infrared, sonar, inertial measurement units, etc.], as well as communication subsystem. Autonomous vehicles face several challenges such as unknown environments, blind spots (unseen views), non-line-of-sight scenarios, poor performance of sensors due to weather conditions, sensor errors, false alarms, limited energy, limited computational resources, algorithmic complexity, human-machine communications, size, and weight constraints. To tackle these problems, several algorithmic approaches have been implemented covering design of sensors, processing, control, and navigation. The review seeks to provide up-to-date information on the requirements, algorithms, and main challenges in the use of machine vision-based techniques for navigation and control in autonomous vehicles. An application using land-based vehicle as an Internet of Thing-enabled platform for pedestrian detection and tracking is also presented.
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