Predictive visuo-motor communication through neural oscillations

The mechanisms coordinating action and perception over time are poorly understood. The sensory cortex needs to prepare for upcoming changes contingent on action, and this requires temporally precise communication that takes into account the variable delays between sensory and motor processing. Several theorists^1,2 have proposed synchronization of the endogenous oscillatory activity observed in most regions of the brain³ as the basis for an efficient and flexible communication protocol between distal brain areas,^2,4 a concept known as "communication through coherence." Synchronization of endogenous oscillations^5,6 occurs after a salient sensory stimulus, such as a flash or a sound,^7-11 and after a voluntary action,^12-18 and this directly impacts perception, causing performance to oscillate rhythmically over time. Here we introduce a novel fMRI paradigm to probe the neural sources of oscillations, based on the concept of perturbative signals, which overcomes the low temporal resolution of BOLD signals. The assumption is that a synchronized endogenous rhythm will modulate cortical excitability rhythmically, which should be reflected in the BOLD responses to brief stimuli presented at different phases of the oscillation cycle. We record rhythmic oscillations of V1 BOLD synchronized by a simple voluntary action, in phase with behaviorally measured oscillations in visual sensitivity in the theta range. The functional connectivity between V1 and M1 also oscillates at the same rhythm. By demonstrating oscillatory temporal coupling between primary motor and sensory cortices, our results strongly implicate communication through coherence to achieve precise coordination and to encode sensory-motor timing.

A Framework for Multiple Ground Target Finding and Inspection Using a Multirotor UAS

Small unmanned aerial systems (UASs) now have advanced waypoint-based navigation capabilities, which enable them to collect surveillance, wildlife ecology and air quality data in new ways. The ability to remotely sense and find a set of targets and descend and hover close to each target for an action is desirable in many applications, including inspection, search and rescue and spot spraying in agriculture. This paper proposes a robust framework for vision-based ground target finding and action using the high-level decision-making approach of Observe, Orient, Decide and Act (OODA). The proposed framework was implemented as a modular software system using the robotic operating system (ROS). The framework can be effectively deployed in different applications where single or multiple target detection and action is needed. The accuracy and precision of camera-based target position estimation from a low-cost UAS is not adequate for the task due to errors and uncertainties in low-cost sensors, sensor drift and target detection errors. External disturbances such as wind also pose further challenges. The implemented framework was tested using two different test cases. Overall, the results show that the proposed framework is robust to localization and target detection errors and able to perform the task.

Vision-Based Target Finding and Inspection of a Ground Target Using a Multirotor UAV System

In this paper, a system that uses an algorithm for target detection and navigation and a multirotor Unmanned Aerial Vehicle (UAV) for finding a ground target and inspecting it closely is presented. The system can also be used for accurate and safe delivery of payloads or spot spraying applications in site-specific crop management. A downward-looking camera attached to a multirotor is used to find the target on the ground. The UAV descends to the target and hovers above the target for a few seconds to inspect the target. A high-level decision algorithm based on an OODA (observe, orient, decide, and act) loop was developed as a solution to address the problem. Navigation of the UAV was achieved by continuously sending local position messages to the autopilot via Mavros. The proposed system performed hovering above the target in three different stages: locate, descend, and hover. The system was tested in multiple trials, in simulations and outdoor tests, from heights of 10 m to 40 m. Results show that the system is highly reliable and robust to sensor errors, drift, and external disturbance.

Beyond the tangent point: gaze targets in naturalistic driving

Moving in natural environments is guided by looking where you are going. When entering a bend, car drivers direct their gaze toward the inside of the curve, in the region of the curve apex. This behavior has been analyzed in terms of both "tangent point models," which posit that drivers are looking at the tangent point (TP), and "future path models," which posit that drivers are visually targeting a point on the desired trajectory or future path (FP). This issue remains unresolved, partly due to the challenge of representing the changing visual projection of the trajectory into the driver's field of view. This paper reports a study of naturalistic driving, in which the FP in the field of view is explicitly modeled, and the TP and reference points on the FP are simultaneously analyzed as potential gaze targets. We argue that traditional area-of-interest methods commonly interpreted as supporting the TP hypothesis are problematic when the interest is contrasting multiple gaze targets. This prompts a critical reassessment of the empirical case for the ubiquity of looking at the TP and the generality of the TP hypothesis as an account of where people look when they steer. As a basis for representing driver gaze behavior, the FP is an equally valid point of departure. There are no overwhelming theoretical or empirical reasons for favoring the TP models over the FP models.