Damit mobile Roboter vielfältige komplexe Aufgaben autonom erfüllen können, benötigen sie Planung, um so entsprechend der Gegebenheiten ihrer Umgebung zu handeln. Durch die stetig zunehmenden Fähigkeiten der Roboterhardware gewinnt die Handlungsplanung und deren Integration in das Gesamtsystem zunehmend an Bedeutung. Die vorliegende Arbeit versucht, einen weiteren Schritt Richtung planbasierter Robotersteuerung zu gehen. Dabei wird zunächst die Verwendung des HTN-Planers SHOP2 in einem Robotersystem, das sich das Lernen aus Erfahrungen zum Ziel gesetzt hat, beschrieben und Wege aufgezeigt, wie die Robustheit des Systems durch die Integration mit anderen Komponenten erhöht werden kann. Mobilen Robotern stehen unterschiedliche Formen von Wissen, wie temporales oder räumliches Wissen oder Informationen über Ressourcen zur Verfügung. Diese können von SHOP2 jedoch nicht genutzt werden. Um diese Anforderung zu erfüllen, wird in dieser Arbeit der hybride hierarchische Planer CHIMP präsentiert, der die Vorteile hierarchischer Planung und der hybriden Planung als Meta-CSP, das die Integration verschiedener Wissensformen erlaubt, kombiniert. Des Weiteren können seine Pläne parallel ausführbare Aktionen enthalten, und zusätzliche Aufgaben können während der Ausführung in den bestehenden Plan integriert werden.

Action planning has been used in the field of robotics for solving long-running tasks. In the robot architectures field, it is also known as the deliberative layer. However, there is still a gap between the symbolic representation on the one hand and the low-level control and sensor representation on the other. In addition, the definition of a planning problem for a complex, real-world robot is not trivial. The planning process could become intractable as its search spaces become large. As the defined planning problem determines the complexity and the computationability for solving the problem, it should contain only relevant states. In this work, a novel approach which amalgamates Description Logic (DL) reasoning with Hierarchical Task Network (HTN) planning is introduced. The planning domain description as well as fundamental HTN planning concepts are represented in DL and can therefore be subject to DL reasoning; from these representations, concise planning problems are generated for HTN planning. The method is presented through an example in the robot navigation domain. In addition, a case study of the RoboCup@Home domain is given. As proof of concept, a well-known planning problem that often serves as a benchmark, namely that of the blocks-world, is modeled and solved using this approach. An analysis of the performance of the approach has been conducted and the results show that this approach yields significantly smaller planning problem descriptions than those generated by current representations in HTN planning.

An autonomous robot that acts in a goal-directed fashion requires a world model of the elements that are relevant to the robot's task. In real-world, dynamic environments, the world model has to be created and continually updated from uncertain sensor data. The symbols used in plan-based robot control have to be anchored to detected objects. Furthermore, robot perception is not only a bottom-up and passive process: Knowledge about the composition of compound objects can be used to recognize larger-scale structures from their parts. Knowledge about the spatial context of an object and about common relations to other objects can be exploited to improve the quality of the world model and can inform an active search for objects that are missing from the world model. This thesis makes several contributions to address these challenges: First, a model-based semantic mapping system is presented that recognizes larger-scale structures like furniture based on semantic descriptions in an ontology. Second, a context-aware anchoring process is presented that creates and maintains the links between object symbols and the sensor data corresponding to those objects while exploiting the geometric context of objects. Third, an active perception system is presented that actively searches for a required object while being guided by the robot's knowledge about the environment.

In the context of evolutionary robotics and neurorobotics, artificial neural networks, used as controllers for animats, are examined to identify principles of neuro-control, network organization, the interaction between body and control, and other likewise properties. Before such an examination can take place, suitable neuro-controllers have to be identified. A promising and widely used technique to search for such networks are evolutionary algorithms specifically adapted for neural networks. These allow the search for neuro-controllers with various network topologies directly on physically grounded (simulated) animats. This neuro-evolution approach works well for small neuro-controllers and has lead to interesting results. However, due to the exponentially increasing search space with respect to the number of involved neurons, this approach does not scale well with larger networks. This scaling problem makes it difficult to find non-trivial, larger networks, that show interesting properties. In the context of this thesis, networks of this class are called mid-scale networks, having between 50 and 500 neurons. Searching for networks of this class involves very large search spaces, including all possible synaptic connections between the neurons, the bias terms of the neurons and (optionally) parameters of the neuron model, such as the transfer function, activation function or parameters of learning rules. In this domain, most evolutionary algorithms are not able to find suitable, non-trivial neuro-controllers in feasible time. To cope with this problem and to shift the frontier for evolvable network topologies a bit further, a novel evolutionary method has been developed in this thesis: the Interactively Constrained Neuro-Evolution method (ICONE). A way to approach the problem of increasing search spaces is the introduction of measures that reduce and restrict the search space back to a feasible domain. With ICONE, this restriction is realized with a unified, extensible and highly adaptable concept: Instead of evolving networks freely, networks are evolved within specifically designed constraint masks, that define mandatory properties of the evolving networks. These constraint masks are defined primarily using so called functional constraints, that actively modify a neural network to enforce the adherence of all required limitations and assumptions. Consequently, independently of the mutations taking place during evolution, the constraint masks repair and readjust the networks so that constraint violations are not able to evolve. Such functional constraints can be very specific and can enforce various network properties, such as symmetries, structure reuse, connectivity patterns, connectivity density heuristics, synaptic pathways, local processing assemblies, and much more. Constraint masks therefore describe a narrow, user defined subset of the parameter space -- based on domain knowledge and user experience -- that focuses the search on a smaller search space leading to a higher success rate for the evolution. Due to the involved domain knowledge, such evolutions are strongly biased towards specific classes of networks, because only networks within the defined search space can evolve. This, surely, can also be actively used to lead the evolution towards specific solution approaches, allowing the experimenter not only to search for any upcoming solution, but also to confirm assumptions about possible solutions. This makes it easier to investigate specific neuro-control principles, because the experimenter can systematically search for networks implementing the desired principles, simply by using suitable constraints to enforce them. Constraint masks in ICONE are built up by functional constraints working on so called neuro-modules. These modules are used to structure the networks, to define the scope for constraints and to simplify the reuse of (evolved) neural structures. The concept of functional, constrained neuro-modules allows a simple and flexible way to construct constraint masks and to inherit constraints when neuro-modules are reused or shared. A final cornerstone of the ICONE method is the interactive control of the evolution process, that allows the adaptation of the evolution parameters and the constraint masks to guide evolution towards promising domains and to counteract undesired developments. Due to the constraint masks, this interactive guidance is more effective than the adaptation of the evolution parameters alone, so that the identification of promising search space regions becomes easier. This thesis describes the ICONE method in detail and shows several applications of the method and the involved features. The examples demonstrate that the method can be used effectively for problems in the domain of mid-scale networks. Hereby, as effects of the constraint masks and the herewith reduced complexity of the networks, the results are -- despite their size -- often easy to comprehend, well analyzable and easy to reuse. Another benefit of constraint masks is the ability to deliberately search for very specific network configurations, which allows the effective and systematic exploration of distinct variations for an evolution experiment, simply by changing the constraint masks over the course of multiple evolution runs. The ICONE method therefore is a promising novel evolution method to tackle the problem of evolving mid-scale networks, pushing the frontier of evolvable networks a bit further. This allows for novel evolution experiments in the domain of neurorobotics and evolutionary robotics and may possibly lead to new insights into neuro-dynamical principles of animat control.

Mobile robots need to move in the real world for the majority of tasks. Their control is often intertwined with the tasks they have to solve. Unforeseen events must have an adequate and prompt reaction, in order to solve the corresponding task satisfactorily. A robust system must be able to respond to a variety of events with specific solutions and strategies to keep the system running. Robot navigation control systems are essential for this. In this thesis we present a robot navigation control system that fulfills these requirements: Move Base Flex. Furthermore, the map representation used to model the environment is essential for path planning. Depending on the representation of the map, path planners can solve problems like simple 2D indoor navigation, but also complex rough terrain outdoor navigation with multiple levels and varying slopes, if the corresponding representation can model them accurately. With Move Base Flex, we present a middle layer navigation framework for navigation control, that is map independent at its core. Based on this, we present the Mesh Navigation Stack to master path planning in complex outdoor environments using a developed mesh map to model surfaces in 3D. Finally, to solve path planning in complex outdoor environments, we have developed and integrated the Continuous Vector Field Planner with the aforementioned solutions and evaluated it on five challenging and complex outdoor datasets in simulation and in the real-world. Beyond that, the corresponding developed software packages are open source available and have been released to easily reproduce the provided scientific results.