A PUMA 560 industrial robot has been retrofitted with an open-architecture controller. An adaptive control scheme that incorporates actuator dynamics has been implemented on this robot testbed. The stability of the control scheme is proved through a Lyapunov-like analysis. Experiments carried out show that the performance of the robot, with the adaptive control scheme, is significantly improved.
We develop new fundamental methodologies for high performance and provably safe autonomous and collaborative control and operation of autonomous unmanned aerial systems (UAS) in the national airspace (NAS), where both UAS and manned aircraft fly. The proposed framework is model-based, and emphasizes multiple scales in time and space as well as hybrid systems mathematics to capture both the analog and logical components of control functionalities. We develop on-line control laws that allow for multiple UAS agents to reconfigure to different formations with proofs of safety and convergence while navigating in integrated airspace with piloted air vehicles. We demonstrate our results in simulated scenaria with both cooperative and uncooperative air vehicles.
As a non-invasive method, vision-based driver monitoring aims to identify risky maneuvers for intelligent vehicles and it has gained an increasing interest over recent years. However, most existing methods tend to design models for specific tasks, such as head pose or gaze estimation, which results in redundant models hampering real time applications. Besides, most driver facial monitoring methods ignore the correlation of different tasks. In this work, we propose a unified framework based on cascade learning for simultaneous facial landmark detection and head pose estimation, as well as simultaneous eye center detection and gaze estimation. In particular, built upon the key idea that facial landmark locations and 3D face model parameters are implicitly correlated, we introduce a cascade regression framework to achieve these two tasks simultaneously. After coarsely extracting the driver's eye region from the detected facial landmarks, we perform cascade regression for simultaneous eye center detection and gaze estimation. Leveraging the power of cascade learning allows our method to alternatively optimize facial landmark detection, head pose estimation, eye center localization, and gaze prediction. The comparison experiments conducted on benchmark datasets of 300-W, GI4E, BU, MPIIGaze, and driving dataset of SHRP2 demonstrate that our proposed method can achieve state-of-the-art performance with robust effectiveness on the real driver monitoring applications.
In this paper, we propose a reachable set based collision avoidance algorithm for unmanned aerial vehicles (UAVs). UAVs have been deployed for agriculture research and management, surveillance and sensor coverage for threat detection and disaster search and rescue operations. It is essential for the aircraft to have on-board collision avoidance capability to guarantee safety. Instead of the traditional approach of collision avoidance between trajectories, we propose a collision avoidance scheme based on reachable sets and tubes. We then formulate the problem as a convex optimization problem seeking time varying control sets for the ego aircraft given the predicted intruder reachable tube. We have applied the approach on a case study of two quadrotors collision avoidance scenario.
Research supported in part by the NSF under grant CNS-1035655, by the NIST under contract 70NANB11H148 and by a grant from the Lockheed Martin Corporation.
In this paper, we consider the robot motion (or task) planning problem under some given bounded time high level specifications. We use metric interval temporal logic (MITL), a member of the temporal logic family, to represent the task specification and then we provide a constructive way to generate a timed automaton and methods to look for accepting runs on the automaton to find a feasible motion (or path) sequence for the robot to complete the task.
This paper presents a survey on sensor applications in machining, including conventional machining and laser machining. The physical principles, technical features and applications are discussed for the most commonly used sensors.
In this paper, we consider the robot motion (or task) planning problem under some given time bounded high level specifications. We use metric interval temporal logic (MITL), a member of the temporal logic family, to represent the task specification and then we provide a constructive way to generate a timed automaton and methods to look for accepting runs on the automaton to find a feasible motion (or path) sequence for the robot to complete the task.
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