Resilient visual perception for multiagent systems

There has been an increasing interest in visual sensors and vision-based solutions for single and multi-robot systems. Vision-based sensors, e.g., traditional RGB cameras, grant rich semantic information and accurate directional measurements at a relatively low cost; however, such sensors have two major drawbacks. They do not generally provide reliable depth estimates, and typically have a limited field of view. These limitations considerably increase the complexity of controlling multiagent systems. This thesis studies some of the underlying problems in vision-based multiagent control and mapping. The first contribution of this thesis is a method for restoring bearing rigidity in non-rigid networks of robots. We introduce means to determine which bearing measurements can improve bearing rigidity in non-rigid graphs and provide a greedy algorithm that restores rigidity in 2D with a minimum number of added edges. The focus of the second part is on the formation control problem using only bearing measurements. We address the control problem for consensus and formation control through non-smooth Lyapunov functions and differential inclusion. We provide a stability analysis for undirected graphs and investigate the derived controllers for directed graphs. We also introduce a newer notion of bearing persistence for pure bearing-based control in directed graphs. The third part is concerned with the bearing-only visual homing problem with a limited field of view sensor. In essence, this problem is a special case of the formation control problem where there is a single moving agent with fixed neighbors. We introduce a navigational vector field composed of two orthogonal vector fields that converges to the goal position and does not violate the field of view constraints. Our method does not require the landmarks' locations and is robust to the landmarks' tracking loss. The last part of this dissertation considers outlier detection in pose graphs for Structure from Motion (SfM) and Simultaneous Localization and Mapping (SLAM) problems. We propose a method for detecting incorrect orientation measurements before pose graph optimization by checking their geometric consistency in cycles. We use Expectation-Maximization to fine-tune the noise's distribution parameters and propose a new approximate graph inference procedure specifically designed to take advantage of evidence on cycles with better performance than standard approaches. These works will help enable multi-robot systems to overcome visual sensors' limitations in collaborative tasks such as navigation and mapping

The 1990 progress report and future plans

This document describes the progress and plans of the Artificial Intelligence Research Branch (RIA) at ARC in 1990. Activities span a range from basic scientific research to engineering development and to fielded NASA applications, particularly those applications that are enabled by basic research carried out at RIA. Work is conducted in-house and through collaborative partners in academia and industry. Our major focus is on a limited number of research themes with a dual commitment to technical excellence and proven applicability to NASA short, medium, and long-term problems. RIA acts as the Agency's lead organization for research aspects of artificial intelligence, working closely with a second research laboratory at JPL and AI applications groups at all NASA centers

Control of Formations with Non-rigid and Hybrid Graphs

This thesis studies the problem of control of multi-agent formations, of which the interaction architectures can be modeled by undirected and directed graphs or a mixture of the two (hybrid graphs). The algorithms proposed in this thesis can be applied to control the architectures of multi-agent systems or sensor networks, and the developed control laws can be employed in the autonomous agents of various types within multi-agent systems. This thesis discusses two major issues. The first tackles formations with undirected and directed underlying graphs, more specifically, the problems of rigidity restoration and persistence verification for multi-agent formations are studied. The second discusses the control of formations with both undirected and directed interaction architectures (hybrid formations) by distance-based control methods. The main contributions of this thesis are: definition of spindle agent and basic graphs for non-rigid undirected graphs, development of new operations for the constructions of undirected and directed graphs, design of graph rigidity restoration strategy by merging two or more non-rigid graphs, development of new persistence analysis strategy for arbitrary directed graphs, definition and investigation of hybrid formations and the underlying hybrid graphs, verification of persistence and minimal persistence for hybrid graphs, as well as the control of persistent hybrid formations by distance-based approaches

Emergence of self-organized amoeboid movement in a multi-agent approximation of Physarum polycephalum

The giant single-celled slime mould Physarum polycephalum exhibits complex morphological adaptation and amoeboid movement as it forages for food and may be seen as a minimal example of complex robotic behaviour. Swarm computation has previously been used to explore how spatio-temporal complexity can emerge from, and be distributed within, simple component parts and their interactions. Using a particle-based swarm approach we explore the question of how to generate collective amoeboid movement from simple non-oscillatory component parts in a model of P. polycephalum. The model collective behaves as a cohesive and deformable virtual material, approximating the local coupling within the plasmodium matrix. The collective generates de-novo and complex oscillatory patterns from simple local interactions. The origin of this motor behaviour distributed within the collective rendering is morphologically adaptive, amenable to external influence and robust to simulated environmental insult. We show how to gain external influence over the collective movement by simulated chemo-attraction (pulling towards nutrient stimuli) and simulated light irradiation hazards (pushing from stimuli). The amorphous and distributed properties of the collective are demonstrated by cleaving it into two independent entities and fusing two separate entities to form a single device, thus enabling it to traverse narrow, separate or tortuous paths. We conclude by summarizing the contribution of the model to swarm-based robotics and soft-bodied modular robotics and discuss the future potential of such material approaches to the field. © 2012 IOP Publishing Ltd

Simulation modelling and visualisation: toolkits for building artificial worlds

Simulations users at all levels make heavy use of compute resources to drive computational simulations for greatly varying applications areas of research using different simulation paradigms. Simulations are implemented in many software forms, ranging from highly standardised and general models that run in proprietary software packages to ad hoc hand-crafted simulations codes for very specific applications. Visualisation of the workings or results of a simulation is another highly valuable capability for simulation developers and practitioners. There are many different software libraries and methods available for creating a visualisation layer for simulations, and it is often a difficult and time-consuming process to assemble a toolkit of these libraries and other resources that best suits a particular simulation model. We present here a break-down of the main simulation paradigms, and discuss differing toolkits and approaches that different researchers have taken to tackle coupled simulation and visualisation in each paradigm

The brainstem reticular formation is a small-world, not scale-free, network

Recently, it has been demonstrated that several complex systems may have simple graph-theoretic characterizations as so-called ‘small-world’ and ‘scale-free’ networks. These networks have also been applied to the gross neural connectivity between primate cortical areas and the nervous system of Caenorhabditis elegans. Here, we extend this work to a specific neural circuit of the vertebrate brain—the medial reticular formation (RF) of the brainstem—and, in doing so, we have made three key contributions. First, this work constitutes the first model (and quantitative review) of this important brain structure for over three decades. Second, we have developed the first graph-theoretic analysis of vertebrate brain connectivity at the neural network level. Third, we propose simple metrics to quantitatively assess the extent to which the networks studied are small-world or scale-free. We conclude that the medial RF is configured to create small-world (implying coherent rapid-processing capabilities), but not scale-free, type networks under assumptions which are amenable to quantitative measurement

Distributed formation tracking using local coordinate systems

This paper studies the formation tracking problem for multi-agent systems, for which a distributed estimator–controller scheme is designed relying only on the agents’ local coordinate systems such that the centroid of the controlled formation tracks a given trajectory. By introducing a gradient descent term into the estimator, the explicit knowledge of the bound of the agents’ speed is not necessary in contrast to existing works, and each agent is able to compute the centroid of the whole formation in finite time. Then, based on the centroid estimation, a distributed control algorithm is proposed to render the formation tracking and stabilization errors to converge to zero, respectively. Finally, numerical simulations are carried to validate our proposed framework for solving the formation tracking problem