A Hybrid Visual-Model Based Robot Control Strategy for Micro Ground Robots

This paper proposed a hybrid vision-based robot control strategy for micro ground robots by mediating two vision models from mixed categories: a bio-inspired collision avoidance model and a segmentation based target following model. The implemented model coordination strategy is described as a probabilistic model using finite state machine (FSM) that allows the robot to switch behaviours adapting to the acquired visual information. Experiments demonstrated the stability and convergence of the embedded hybrid system by real robots, including the studying of collective behaviour by a swarm of such robots with environment mediation. This research enables micro robots to run visual models with more complexity. Moreover, it showed the possibility to realize aggregation behaviour on micro robots by utilizing vision as the only sensing modality from non-omnidirectional cameras

The role of neural synchrony and rate in high-dimensional input systems. The Antennal Lobe: a case study

International audienceDealing with high-throughput information systems is becoming an everyday problem in many fields of science, as technological advances improve our ability to gather data. In particular, the information encoding problem in highdimensional spaces is a crucial aspect to consider. In fact, biological systems are known to be very efficient at encoding and processing high-dimensional information. Here we propose a biologically-based solution that mimics the neural processing performed by the Antennal Lobe of insects. Based on our understanding of this system, our model exploits plausible neural mechanisms to transform the massive and high-dimensional spatial and temporal input of the olfactory receptor neurons into a neural population encoding based on synchrony and frequency, consistent with known physiology. We demonstrate the capabilities of our Antennal Lobe model in the context of a classification task of different olfactory stimuli of varying concentrations. We show that the generated neural representation conveys both the identity and the concentration of each stimuli

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A fly-locust based neuronal control system applied to an unmanned aerial vehicle: the invertebrate neuronal principles for course stabilization, altitude control and collision avoidanc

Design, development, and evaluation of an interactive personalized social robot to monitor and coach post-stroke rehabilitation exercises

Ministry of Education, Singapore under its Academic Research Funding Tier

Exploring quantified self attitudes

In recent years there is a growing optimism that health interventions may become more effective through the use of self-tracking. Related efforts are hampered by the short-lived compliance to self-tracking schemes. This paper examines the attitudes and motivations of self-trackers that could guide the design of self-tracking applications. Based on a questionnaire survey and follow up interviews a set of three personas of self trackers is proposed, in addition, design requirements are proposed for improving adherence to self-tracking technologies