A methodology for provably stable behaviour-based intelligent control

This paper presents a design methodology for a class of behaviour-based control systems, arguing its potential for application to safety critical systems. We propose a formal basis for subsumption architecture design based on two extensions to Lyapunov stability theory, the Second Order Stability Theorems, and interpretations of system safety and liveness in Lyapunov stability terms. The subsumption of the new theorems by the classical stability theorems serves as a model of dynamical subsumption, forming the basis of the design methodology. Behaviour-based control also offers the potential for using simple computational mechanisms, which will simplify the safety assurance process. © 2005 Elsevier B.V. All rights reserved

Heuristic localization and mapping for active sensing with humanoid robot NAO

The purpose of this thesis is to utilize vision system for autonomous navigation. The platform which has been used was an NAO humanoid robot. More specifically, NAO cameras and its makers have been used to solve the two most fundamental problems of autonomous mobile robots which are localization and mapping the environment. NAO markers have been printed and positioned on virtual walls to construct an experimental environment to investigate proposed localization and mapping methods. In algorithm side, basically NAO uses two known markers to localize itself and averages over all location predicted using each pair of known markers. At the same time NAO calculates the location of any unknown markers and add it to the Map. Moreover, A simple go-to-goal path planning algorithm has been implemented to provide a continuous localization and mapping for longer walks of NAO. The result of this work shows that NAO can navigate in an experimental environment using only its marker and camera and reach a predefined target location successfully. Also, It has been shown that NAO can locate itself with acceptable accuracy and make a feature-based map of markers at each location. This thesis provides a starting point for experimenting with different algorithms in path planning as well as possibility to investigate active sensing methods. Furthermore, the possibility of combining other features with NAO marker can be investigated to provide even more accurate result

The Influence of Collective Working Memory Strategies on Agent Teams

Past self-organizing models of collectively moving "particles" (simulated bird flocks, fish schools, etc.) typically have been based on purely reflexive agents that have no significant memory of past movements or environmental obstacles. These agent collectives usually operate in abstract environments, but as these domains take on a greater realism, the collective requires behaviors use not only presently observed stimuli but also remembered information. It is hypothesized that the addition of a limited working memory of the environment, distributed among the collective's individuals can improve efficiency in performing tasks. This is first approached in a more traditional particle system in an abstract environment. Then it is explored for a single agent, and finally a team of agents, operating in a simulated 3-dimensional environment of greater realism. In the abstract environment, a limited distributed working memory produced a significant improvement in travel between locations, in some cases improving performance over time, while in others surprisingly achieving an immediate benefit from the influence of memory. When strategies for accumulating and manipulating memory were subsequently explored for a more realistic single agent in the 3-dimensional environment, if the agent kept a local or a cumulative working memory, its performance improved on different tasks, both when navigating nearby obstacles and, in the case of cumulative memory, when covering previously traversed terrain. When investigating a team of these agents engaged in a pursuit scenario, it was determined that a communicating and coordinating team still benefited from a working memory of the environment distributed among the agents, even with limited memory capacity. This demonstrates that a limited distributed working memory in a multi-agent system improves performance on tasks in domains of increasing complexity. This is true even though individual agents know only a fraction of the collective's entire memory, using this partial memory and interactions with others in the team to perform tasks. These results may prove useful in improving existing methodologies for control of collective movements for robotic teams, computer graphics, particle swarm optimization, and computer games, and in interpreting future experimental research on group movements in biological populations

Learning of Unknown Environments in Goal-Directed Guidance and Navigation Tasks: Autonomous Systems and Humans

University of Minnesota Ph.D. dissertation. December 2017. Major: Aerospace Engineering. Advisor: Berenice Mettler. 1 computer file (PDF); xvi, 176 pages.Guidance and navigation in unknown environments requires learning of the task environment simultaneous to path planning. Autonomous guidance in unknown environments requires a real-time integration of environment sensing, mapping, planning, trajectory generation, and tracking. For brute force optimal control, the spatial environment should be mapped accurately. The real-world environments are in general cluttered, complex, unknown, and uncertain. An accurate model of such environments requires to store an enormous amount of information and then that information has to be processed in optimal control formulation, which is not computationally cheap and efficient for online operations of autonomous guidance systems. On the contrary, humans and animals are in general able to navigate efficiently in unknown, complex, and cluttered environments. Like autonomous guidance systems, humans and animals also do not have unlimited information processing and sensing capacities due to their biological and physical constraints. Therefore, it is relevant to understand cognitive mechanisms that help humans learn and navigate efficiently in unknown environments. Such understanding can help to design planning algorithms that are computationally efficient as well as better understand how to improve human-machine interfaces in particular between operators and autonomous agents. This dissertation is organized in three parts: 1) computational investigation of environment learning in guidance and navigation (chapters 3 and 4), 2) investigation of human environment learning in guidance tasks (chapters 5 and 6), and 3) autonomous guidance framework based on a graph representation of environment using subgoals that are invariants in agent-environment interactions (chapter 7). In the first part, the dissertation presents a computational framework for learning autonomous guidance behavior in unknown or partially known environments. The learning framework uses a receding horizon trajectory optimization associated with a spatial value function (SVF). The SVF describes optimal (e.g. minimum time) guidance behavior represented as cost and velocity at any point in geographical space to reach a specified goal state. For guidance in unknown environments, a local SVF based on current vehicle state is updated online using environment data from onboard exteroceptive sensors. The proposed learning framework has the advantage in that it learns information directly relevant to the optimal guidance and control behavior enabling optimal trajectory planning in unknown or partially known environments. The learning framework is evaluated by measuring performance over successive runs in a 3-D indoor flight simulation. The test vehicle in the simulations is a Blade-Cx2 coaxial miniature helicopter. The environment is a priori unknown to the learning system. The dissertation investigates changes in performance, dynamic behavior, SVF, and control behavior in body frame, as a result of learning over successive runs. In the second part, the dissertation focuses on modeling and evaluating how a human operator learns an unknown task environment in goal-directed navigation tasks. Previous studies have showed that human pilots organize their guidance and perceptual behavior using the interaction patterns (IPs), i.e., invariants in their sensory-motor processes in interactions with the task space. However, previous studies were performed in known environments. In this dissertation, the concept of IPs is used to build a modeling and analysis framework to investigate human environment learning and decision-making in navigation of unknown environments. This approach emphasizes the agent dynamics (e.g., a vehicle controlled by a human operator), which is not typical in simultaneous navigation and environment learning studies. The framework is applied to analyze human data from simulated first-person guidance experiments in an obstacle field. Subjects were asked to perform multiple trials and find minimum-time routes between prespecified start and goal locations without priori knowledge of the environment. They used a joystick to control flight behavior and navigate in the environment. In the third part, the subgoal graph framework used to model and evaluate humans is extended to an autonomous guidance algorithm for navigation in unknown environments. The autonomous guidance framework based on subgoal graph is an improvement to the SVF based guidance and learning framework presented in the first part. The latter uses a grid representation of the environment, which is computationally costly in comparison to the graph based guidance model

Explorations into the behaviour-oriented nature of intelligence : Fuzzy behavioural maps.

This thesis explores the behaviour-oriented nature of intelligence and presents the definition and use of Fuzzy Behavioural Maps (FBMs) as a flexible development framework for providing complex autonomous agent behaviour. This thesis provides a proof-of-concept for simple FBMs, including some experimental results in Mobile Robotics and Fuzzy Logic Control. This practical work shows the design of a collision avoidance behaviour (of a mobile robot) using a simple FBM and, the implementation of this using a Fuzzy Logic Controller (FLC). The FBM incorporates three causally related sensorimotor activities (moving around, perceiving obstacles and, varying speed). This Collision Avoidance FBM is designed (in more detail) using fuzzy relations (between levels of perception, motion and variation of speed) in the form of fuzzy control rules. The FLC stores and manipulates these fuzzy control (FBM) rules using fuzzy inference mechanisms and other related implementation parameters (fuzzy sets and fuzzy logic operators). The resulting FBM-FLC architecture controls the behaviour patterns of the agent. Its fuzzy inference mechanisms determine the level of activation of each FBM node while driving appropriate control actions over the creature's motors. The thesis validates (demonstrates the general fitness of) this control architecture through various pilot tests (computer simulations). This practical work also serves to emphasise some benefits in the use of FLC techniques to implement FBMs (e.g. flexibility of the fuzzy aggregation methods and fuzzy granularity).More generally, the thesis presents and validates a FBM Framework to develop more complex autonomous agent behaviour. This framework represents a top-down approach to derive the BB models using generic FBMs, levels of abstraction and refinement stages. Its major scope is to capture and model behavioural dynamics at different levels of abstraction (through different levels of refinement). Most obviously, the framework maps some required behaviours into connection structures of behaviour-producing modules that are causally related. But the main idea is following as many refinement stages as required to complete the development process. These refinement stages help to identify lower design parameters (i.e. control actions) rather than linguistic variables, fuzzy sets or, fuzzy inference mechanisms. They facilitate the definition of the behaviours selected from first levels of abstraction. Further, the thesis proposes taking the FBM Framework into the implementation levels that are required to build BB control architecture and provides and application case study. This describes how to develop a complex, non-hierarchical, multi-agent behaviour system using the refinement capabilities of the FBM Framework. Finally, the thesis introduces some more general ideas about the use of this framework to cope with some, current complexity issues around the behaviour-oriented nature of intelligence