International Society for Intelligence Research

About: International Society for Intelligence Research is a based out in . It is known for research contribution in the topics: Graphene & Robot. The organization has 218 authors who have published 180 publications receiving 2202 citations. The organization is also known as: ISIR.

Introduction to feedback control of underactuated VTOLvehicles: A review of basic control design ideas and principles

Abstract: This article is an introduction to feedback control design for a family of robotic aerial vehicles with vertical take-off and landing (VTOL) capabilities such as quadrotors, ducted-fan tail-sitters, and helicopters. Potential applications for such devices, like surveillance, monitoring, or mapping, are varied and numerous. For these applications to emerge, motion control algorithms that guarantee a good amount of robustness against state measurement/ estimation errors and unmodeled dynamics like, for example, aerodynamic perturbations, are needed. The feedback control methods considered here range from basic linear control schemes to more elaborate nonlinear control solutions. The modeling of the dynamics of these systems is first recalled and discussed. Then several control algorithms are presented and commented upon in relation to implementation issues and various operating modes encountered in practice, from teleoperated to fully autonomous flight. Particular attention is paid to the incorporation of integral-like control actions, often overlooked in nonlinear control studies despite their practical importance to render the control performance more robust with respect to unmodeled or poorly estimated additive perturbations.

Beyond black-box optimization: a review of selective pressures for evolutionary robotics

Abstract: Evolutionary robotics is often viewed as the application of a family of black-box optimization algorithms -- evolutionary algorithms - - to the design of robots, or parts of robots. When considering evolutionary robotics as black-box optimization, the selective pressure is mainly driven by a user-defined, black-box fitness function, and a domain-independent selection procedure. However, most evolutionary robotics experiments face similar challenges in similar setups: the selective pressure, and, in particular, the fitness function, is not a pure user-defined black box. The present review shows that, because evolutionary robotics experiments share common features, selective pressures for evolutionary robotics are a subject of research on their own. The literature has been split into two categories: goal refiners, aimed at changing the definition of a good solution, and process helpers, designed to help the search process. Two sub-categories are further considered: task-specific approaches, which require knowledge on how to solve the task and task-agnostic ones, which do not need it. Besides highlighting the diversity of the approaches and their respective goals, the present review shows that many task-agnostic process helpers have been proposed during the last years, thus bringing us closer to the goal of a fully automated robot behavior design process.

A Model of Reward- and Effort-Based Optimal Decision Making and Motor Control

Abstract: Costs (e.g. energetic expenditure) and benefits (e.g. food) are central determinants of behavior. In ecology and economics, they are combined to form a utility function which is maximized to guide choices. This principle is widely used in neuroscience as a normative model of decision and action, but current versions of this model fail to consider how decisions are actually converted into actions (i.e. the formation of trajectories). Here, we describe an approach where decision making and motor control are optimal, iterative processes derived from the maximization of the discounted, weighted difference between expected rewards and foreseeable motor efforts. The model accounts for decision making in cost/benefit situations, and detailed characteristics of control and goal tracking in realistic motor tasks. As a normative construction, the model is relevant to address the neural bases and pathological aspects of decision making and motor control.

The Curious Incident of Attention in Multisensory Integration: Bottom-up vs. Top-down

Abstract: The role attention plays in our experience of a coherent, multisensory world is still controversial. On the one hand, a subset of inputs may be selected for detailed processing and multisensory integration in a top-down manner, i.e., guidance of multisensory integration by attention. On the other hand, stimuli may be integrated in a bottom-up fashion according to low-level properties such as spatial coincidence, thereby capturing attention. Moreover, attention itself is multifaceted and can be described via both top-down and bottom-up mechanisms. Thus, the interaction between attention and multisensory integration is complex and situation-dependent. The authors of this opinion paper are researchers who have contributed to this discussion from behavioural, computational and neurophysiological perspectives. We posed a series of questions, the goal of which was to illustrate the interplay between bottom-up and top-down processes in various multisensory scenarios in order to clarify the standpoint taken by each author and with the hope of reaching a consensus. Although divergence of viewpoint emerges in the current responses, there is also considerable overlap: In general, it can be concluded that the amount of influence that attention exerts on MSI depends on the current task as well as prior knowledge and expectations of the observer. Moreover stimulus properties such as the reliability and salience also determine how open the processing is to influences of attention.