Map-enhanced visual taxiway extraction for autonomous taxiing of UAVs

In this paper, a map-enhanced method is proposed for vision-based taxiway centreline extraction, which is a prerequisite of autonomous visual navigation systems for unmanned aerial vehicles. Comparing with other sensors, cameras are able to provide richer information. Consequently, vision based navigations have been intensively studied in the recent two decades and computer vision techniques are shown to be capable of dealing with various problems in applications. However, there are signi cant drawbacks associated with these computer vision techniques that the accuracy and robustness may not meet the required standard in some application scenarios. In this paper, a taxiway map is incorporated into the analysis as prior knowledge to improve on the vehicle localisation and vision based centreline extraction. We develop a map updating algorithm so that the traditional map is able to adapt to the dynamic environment via Bayesian learning. The developed method is illustrated using a simulation study

Robots learn to behave: improving human-robot collaboration in flexible manufacturing applications

L'abstract è presente nell'allegato / the abstract is in the attachmen

Semantic embodied navigation : developing agents that navigate from language and vision

Autonomous robotic agents are on their way to becoming in-home personal assistants, construction assistants, and warehouse workers. The degree of autonomy of such systems is reflected by the manner in which we specify goals to them; the abstraction of low-level commands to high-level goals goes hand-in-hand with increased autonomy. In this work, we seek to develop agents that accomplish semantic goals using the lens of semantic embodied navigation. In embodied navigation, agents act in spatial environments to follow a motion path or reach a destination. Semantic refers to how the goal is specified; instead of directly (e.g. coordinates), a goal is implied or its interpretations are constrained. We study two semantic embodied navigation tasks: navigating in response to natural language instructions and navigating to an object specified by an image. Our contributions are highlighted as follows. For both tasks, we establish benchmarks in simulation and evaluate baseline models. For language-based navigation, we develop language-conditioned waypoint prediction networks to study the impact of various action spaces. We then show that high performance on our benchmark can be achieved by transferring an agent from highly-abstract simulation to lower-level simulation. For image-based navigation, we find that a common sensors-to-action policy generalizes poorly to new environments and new goals. We then develop a modular method centered around an object re-identification sub-task that achieves strong performance on our simulation benchmark and successfully transfers to the real world

Asynchronous Visualization of Spatiotemporal Information for Multiple Moving Targets

In the modern information age, the quantity and complexity of spatiotemporal data is increasing both rapidly and continuously. Sensor systems with multiple feeds that gather multidimensional spatiotemporal data will result in information clusters and overload, as well as a high cognitive load for users of these systems. To meet future safety-critical situations and enhance time-critical decision-making missions in dynamic environments, and to support the easy and effective managing, browsing, and searching of spatiotemporal data in a dynamic environment, we propose an asynchronous, scalable, and comprehensive spatiotemporal data organization, display, and interaction method that allows operators to navigate through spatiotemporal information rather than through the environments being examined, and to maintain all necessary global and local situation awareness. To empirically prove the viability of our approach, we developed the Event-Lens system, which generates asynchronous prioritized images to provide the operator with a manageable, comprehensive view of the information that is collected by multiple sensors. The user study and interaction mode experiments were designed and conducted. The Event-Lens system was discovered to have a consistent advantage in multiple moving-target marking-task performance measures. It was also found that participants’ attentional control, spatial ability, and action video gaming experience affected their overall performance

Brain dynamic during landmark-based learning spatial navigation

In the current study, I investigated both human behavior and brain dynamics during spatial navigation to gain a better understanding of human navigational strategies and brain signals that underlie spatial cognition. To this end, a custom-built virtual reality task and a 64-channel scalp electroencephalogram (EEG) were utilized to study participants. At the first step, we presented a novel, straightforward, yet powerful tool to evaluate individual differences during navigation, comprising of a virtual radial-arm maze inspired to the animal experiments. The virtual maze is designed and furnished, similar to an art gallery, to provide a more realistic and exciting environment for subjects’ exploration. We investigated whether a different set of instructions (explicit or implicit) affects subjects’ navigational performance, and we assessed the effect of the set of instructions on exploration strategies during both place learning and recall. We tested 42 subjects and evaluated their way-finding ability. Individual differences were assessed through the analysis of the navigational paths, which permitted the isolation and definition of a few strategies adopted by both subjects who adopted a more explicit strategy, based on explicit instructions, and an implicit strategy, based on implicit instructions. The second step aimed to explore brain dynamics and neurophysiological activity during spatial navigation. More specifically, we aimed to figure out how navigational related brain regions are connected and how their interactions and electrical activity vary according to different navigational tasks and environment. This experiment was divided into two steps: learning phase and test phase. The same virtual maze (art gallery) as the behavioral part of the study was used so that subjects to perform landmark-based navigation. The main task of the experiment was finding and memorizing the position of some goals within the environment during the learning phase and retrieving the spatial information of the goals during the test phase. We recorded EEG signals of 20 subjects during the experiment, and both scalp-level and source-level analysis approaches were employed to figure out how the brain represents the spatial location of landmarks and targets and, more precisely, how different brain regions contribute to spatial orientation and landmark-based learning during navigation

Spatial memories in place recognition

Die Dissertation untersucht die Rolle von Raumgedächtnissen in der Ortserkennung. Die erste Studie fokussiert sich auf das Zusammenspiel zwischen dem räumlichen Arbeitsgedächtnis und der Ortskodierung im Langzeitgedächtnis. Die zweite Studie adressiert die Frage, ob der Abruf von zeitlichem Kontext aus dem episodischen Gedächtnis, der entscheidend für die Integration von vielfachen Ansichten in der Ortserkennung ist, ein charakteristisches Ereignis-korreliertes Potential hervorruft

Spatial-memory control of defensive actions

Adapting anti-predatory defensive strategies to the properties of the environment is critical for survival. Here, I investigated the dependence of mouse instinctive defensive behaviours on memory of the spatial environment, and the neural mechanisms responsible for accurate escape towards refuge. First, using behavioural assays, I show that the choice and execution of defensive behaviours rely on rapidly acquired memory and are promptly updated following acute changes in the environment. In the presence of a known refuge mice escape directly to it, even if this requires approaching the source of threat. Escape is initiated by a memory- guided, accurate head-rotation movement towards the location of the refuge, indicating knowledge of the spatial goal prior to flight start. Second, I demonstrate that the superior colliculus (SC) is essential to accurately orient to shelter during escape, in agreement with its known role in both sensory- and memory- guided head orientation. To identify which upstream areas provide information about refuge location to the SC, I retrogradely traced the SC afferents and performed loss-of- function experiments in candidate areas, which showed that the retrosplenial cortex (RSC) plays a critical role in escape accuracy. Furthermore, channelrhodopsin-2-assisted connectivity studies showed a functional connection between RSC and SC, and chemogenetic inactivation of this projection impaired accurate orientation to refuge during escape. To understand how the RSC and SC control orientation to shelter during escape, I performed simultaneous single-unit recordings from the RSC and SC with Neuropixels probes. Both RSC and SC were found to encode the angular offset between the mouse’s heading and the shelter, at the single-neuron and population levels. Chemogenetic inactivation of SC-projecting RSC neurons disrupts encoding of head-shelter offset in both regions, but it does not compromise the SC motor function during a sensory- orientation task. In summary, I show that escape is a flexible behaviour and its accuracy critically depends on a monosynaptic projection from the RSC to SC. In addition, I show RSC-dependent egocentric encoding of goal direction at SC, an area critical for orientation during escape, providing a possible mechanism for controlling ethologically relevant goal-directed navigation.Boehringer Ingelheim Fellowshi