Simulated Robotic Autonomous Agents with Motion Evolution

This research implemented autonomous control of robotic agents. The movement controls are simulated within a virtual environment. The control system algorithms were subjected to evolution. Genetic algorithms were implemented to enable the robotic agents to adapt in response to objects within the virtual environment. Additionally, each robot’s physical characteristics were subjected to evolution through a survival of the fittest system based on crossover with random mutations. Survival of the fittest was simulated by a shortage of food causing competition. When the food quantity was increased the evolution rate decreased. With increased food, there was reduced competition and average fitness stopped increasing over time. Removing the food bottleneck stopped the survival of the fittest mechanism

Simultaneous incremental neuroevolution of motor control, navigation and object manipulation in 3D virtual creatures

There have been numerous attempts to develop 3D virtual agents by applying evolutionary processes to populations that exist in a realistic physical simulation. Whilst often contributing useful knowledge, no previous work has demonstrated the capacity to evolve a sequence of increasingly complex behaviours in a single, unified system. This thesis has this demonstration as its primary aim. A rigorous exploration of one aspect of incremental artificial evolution was carried out to understand how subtask presentations affect the whole-task generalisation performance of evolved, fixed-morphology 3D agents. Results from this work led to the design of an environment–body–control architecture that can be used as a base for evolving multiple behaviours incrementally. A simulation based on this architecture with a more complex environment was then developed and explored. This system was then adapted to include elements of physical manipulation as a first step toward a fully physical virtual creature environment demonstrating advanced evolved behaviours. The thesis demonstrates that incremental evolutionary systems can be subject to problems of forgetting and loss of gradient, and that different complexification strategies have a strong bearing on the management of these issues. Presenting successive generations of the population to a full range of objective functions (covering and revisiting the range of complexity) outperforms straightforward linear or direct presentations, establishing a more robust approach to the evolution of naturalistic embodied agents. When combining this approach with a bespoke control architecture in a problem requiring reactive and deliberative behaviours, we see results that not only demonstrate success at the tasks, but also show a variety of intricate behaviours being used. This is the first ever example of the simultaneous incremental evolution in 3D of composite behaviours more complex than simple locomotion. Finally, the architecture demonstrably supports extension to manipulation in a feedback control task. Given the problem-agnostic controller architecture, these results indicate a system with potential for discovering yet more advanced behaviours in yet more complex environments

Artificial flying creature by flapping

This paper proposes an artificial flying creature by flapping in a virtual air environment obeying physics law. For this purpose, a concise air drag computation method is introduced. The air drag plays the role of the environment force against the creature. The motion of the creature is automatically computed by use of the physics modeling software system and visualized as an animation movie. Results show that the creature with several pair of wings can fly by flapping as "a life as it could be"