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The objective of this research is to discover principles of ambulatory locomotion on homogeneous and heterogeneous granular substrates and create models of animal and robot interaction within such environments. Since interaction with natural substrates is too complicated to model, we take a robophysics approach – we create a terrain generation system where properties of heterogeneous multi-component substrates can be systematically varied to emulate a wide range of natural terrain properties such as compaction, orientation, obstacle shape/size/distribution, and obstacle mobility within the substrate. A schematic of the proposed system is discussed in detail in the body of this dissertation. Control of such substrates will allow for the systematic exploration of parameters of substrate properties, particularly substrate stiffness and heterogeneities. With this terrain creation system, we systematically explore locomotor strategies of simplified laboratory robots when traversing over different terrain properties. A key feature of this proposed work is the ability to generate general interaction models of locomotor appendages with such complex substrates. These models will aid in the design and control of future robots with morphologies and control strategies that allow for effective navigation on a large diversity of terrains, expanding the scope of terramechanics from large tracked and treaded vehicles on homogeneous ground to arbitrarily shaped and actuated locomotors moving on complex heterogeneous terrestrial substrates.

<p>Multi-robot systems of autonomous mobile robots offer many benefits but also many challenges. This work addresses collision avoidance of robots solving continuous problems in known environments. The approach to handling collision avoidance is here to enhance a motion planning method for single-robot systems to account for auxiliary robots. A few assumptions are made to put the focus of the work on path planning, rather than on localization.</p><p>A method, based on exact cell decomposition and extended with a few rules, was developed and its consistency was proven. The method is divided into two steps: path planning, which is off-line, and path monitoring, which is on-line. This work also introduces the notion of<em>path obstacle</em>, an essential tool for this kind of path planning with many robots.</p><p>Furthermore, an implementation was performed on a system of omni-directional robots and tested in simulations and experiments. The implementation practices centralized control, by letting an additional computer handle the motion planning, to relieve the robots of strenuous computations.</p><p>A few drawbacks with the method are stressed, and the characteristics of problems that the method is suitable for are presented.</p> / QC 20100705

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This thesis puts forward a computational framework that can be used by embodied artificial agents (and in particular autonomous robots) for ontogenetic development. The research investigates methods, endowed with which, an embodied agent can develop control structures for increasingly complex and better adapted behaviour, explicitly and incrementally from its history of interaction with its environment. The temporal horizon of an agent is extended so that past experience can be self-organized into a developing structure that can be used to anticipate the future and act appropriately in environments where state information is incomplete, such as a social environment. A formal definition of sensorimotor experience is given, and Crutchfield’s information metric is used as the basis for comparison of experiences. Information metrics are demonstrated to be able to characterize and identify time-extended behaviour. A definition of a metric space of experiences is followed by the introduction of an architecture that combines this with environmental reinforcement as the basis for a system for robot ontogeny. The architecture is demonstrated and tested in various robotic and simulation experiments. This thesis also introduces the early communication game “Peekaboo” as a tool for the study of human-robot interaction and development. The interaction history architecture is then used by two different robots to develop the capability to engage in the peekaboo game.

Artificial neural networks learned by evolutionary algorithms are commonly used to control the robots. Neural networks can be encoded either directly as a list of weights or indirectly as a weight generator. Unlike direct coding indirect encoding allows to encode a large network using a short genetic code. HyperNEAT is a neuroevolutionary algorithm, which encodes the neural network indirectly, through another (producing) network, which computes synaptic weights. A different algorithm called HyperGP is an alternative to HyperNEAT. In HyperGP, the producing network is replaced by an arithmetic expression, which is being evolved using a genetic programming (GP). We have designed enhancements for HyperGP, using techniques that are either known in a different context of GP or completely new. Algorithm and enhancements have been implemented and experimentally tested on a task of controlling virtual walking robot. The results were compared with HyperNEAT and with the original HyperGP. We have shown that most of the proposed enhancements are effective and, on the given task, HyperGP is better than HyperNEAT. GP thus can successfully replace NEAT in hyper-encoding scheme and improve its efficiency. Powered by TCPDF (www.tcpdf.org)