Static and bootstrapped neuro-simulation for complex robots in evolutionary robotics

Evolutionary Robotics (ER) is a field of study focused on the automatic development of controllers and robot morphologies. Evolving controllers on real-world hardware is time-consuming and can damage hardware through wear. Robotic simulators can be used as an alternative to a real-world robot in order to speed up the ER process. Most simulation techniques in practice use physics-based models that rely on an understanding of the robotic system in question. Developing effective physics-based simulators is time consuming and requires a significant level of specialised knowledge. A lengthy simulator development and tuning process is typically required before the ER process can begin. Artificial Neural Networks simulators (SNNs) can be used as an alternative to a physics based simulation approach. SNNs are simple to construct, do not require significant levels of prior knowledge of the robotic system, are computationally efficient and can be highly accurate. Two types of ER approaches utilising SNNs exist. The Static Neuro-Simulation (SNS) approach involves developing SNNs before the ER process where these SNNs are used instead of a physics-based simulator. Alternatively, SNNs can be developed during the ER process, called the Bootstrapped Neuro-Simulation (BNS) approach. Prior work investigating SNNs has largely been limited to simple robots. A complex robot has many degrees of freedom and ifa low-level controller design is used, the solution search space is high-dimensional and difficult to traverse. Prior work investigating the SNS and BNS approaches have mostly relied on simplified controller designs which rely on built-in prior knowledge of intended robot behaviours. This research uses low-level controller designs which in turn rely on low level simulators. Most ER studies are conducted on a single type of robot morphology. This research investigates the SNS and BNS approaches on two significantly different classes of robots. A Hexapod and Snake robot are used to study the SNS and BNS approaches. The Hexapod robot exhibits limbed, walking behaviours. The Snake robot is limbless and generates crawling behaviours. Demonstrating the viability of the SNS and BNS approaches for two different classes of robots provides strong evidence that the tested approaches are likely viable on other classes of robots. Various proposed improvements to the SNS and BNS approaches are investigated. The Results demonstrate that the SNS and BNS approaches are viable when applied to Hexapod and Snake robots without restricting controller designs to those with significant levels of built-in prior knowledge of robot behaviours. SNNs configured in ensembles improve the likely performance outcomes of solutions. The expected benefit of adding simulator noise during the evolutionary process were not as pronounced for problems investigated in this work

Damage recovery for robot controllers and simulators evolved using bootstrapped neuro-simulation

Robots are becoming increasingly complex. This has made manually designing the software responsible for controlling these robots (controllers) challenging, leading to the creation of the field of evolutionary robotics (ER). The ER approach aims to automatically evolve robot controllers and morphologies by utilising concepts from biological evolution. ER techniques use evolutionary algorithms (EA) to evolve populations of controllers - a process that requires the evaluation of a large number of controllers. Performing these evaluations on a real-world robot is both infeasibly time-consuming and poses the risk of damage to the robot. Simulators present a solution to the issue by allowing the evaluation of controllers to take place on a virtual robot. Traditional methods of controller evolution in simulation encounter two major issues. Firstly, physics simulators are complex to create and are often very computationally expensive. Secondly, the reality gap is encountered when controllers are evolved in simulators that are unable to simulate the real world well enough due to implications or small inaccuracies in the simulation, which together cause controllers in the simulation to be unable to transfer effectively to reality. Bootstrapped Neuro-Simulation (BNS) is an ER algorithm that aims to address the issues inherent with the use of simulators. The algorithm concurrently creates a simulator and evolves a population of controllers. The process starts with an initially random population of controllers and an untrained simulator neural network (SNN), a type of robot simulator which utilises artificial neural networks (ANNs) to simulate a robot's behaviour. Controllers are then continually selected for evaluation in the real world, and the data from these real-world evaluations is used to train the controller-evaluation SNN. BNS is a relatively new algorithm that has not yet been explored in depth. An investigation was, therefore, conducted into BNS's ability to evolve closed-loop controllers. BNS was successful in evolving such controllers, and various adaptations to the algorithm were investigated for their ability to improve the evolution of closed-loop controllers. In addition, the factors which had the greatest impact on BNS's effectiveness were reported upon. Damage recovery is an area that has been the focus of a great deal of research. This is because the progression of the field of robotics means that robots no longer operate only in the safe environments that they once did. Robots are now put to use in areas as inaccessible as the surface of Mars, where repairs by a human are impossible. Various methods of damage recovery have previously been proposed and evaluated, but none focused on BNS as a method of damage recovery. In this research, it was hypothesised that BNS's constantly learning nature would allow it to recover from damage, as it would continue to use new information about the state of the real robot to evolve new controllers capable of functioning in the damaged robot. BNS was found to possess the hypothesised damage recovery ability. The algorithm's evaluation was carried out through the evolution of controllers for simple navigation and light-following tasks for a wheeled robot, as well as a locomotion task for a complex legged robot. Various adaptations to the algorithm were then evaluated through extensive parameter investigations in simulation, showing varying levels of effectiveness. These results were further confirmed through evaluation of the adaptations and effective parameter values in real-world evaluations on a real robot. Both a simple and more complex robot morphology were investigated

A comparative study of artificial neural networks and physics models as simulators in evolutionary robotics

The Evolutionary Robotics (ER) process is a technique that applies evolutionary optimization algorithms to the task of automatically developing, or evolving, robotic control programs. These control programs, or simply controllers, are evolved in order to allow a robot to perform a required task. During the ER process, use is often made of robotic simulators to evaluate the performance of candidate controllers that are produced in the course of the controller evolution process. Such simulators accelerate and otherwise simplify the controller evolution process, as opposed to the more arduous process of evaluating controllers in the real world without use of simulation. To date, the vast majority of simulators that have been applied in ER are physics- based models which are constructed by taking into account the underlying physics governing the operation of the robotic system in question. An alternative approach to simulator implementation in ER is the usage of Artificial Neural Networks (ANNs) as simulators in the ER process. Such simulators are referred to as Simulator Neural Networks (SNNs). Previous studies have indicated that SNNs can successfully be used as an alter- native to physics-based simulators in the ER process on various robotic platforms. At the commencement of the current study it was not, however, known how this relatively new method of simulation would compare to traditional physics-based simulation approaches in ER. The study presented in this thesis thus endeavoured to quantitatively compare SNNs and physics-based models as simulators in the ER process. In order to con- duct this comparative study, both SNNs and physics simulators were constructed for the modelling of three different robotic platforms: a differentially-steered robot, a wheeled inverted pendulum robot and a hexapod robot. Each of these two types of simulation was then used in simulation-based evolution processes to evolve con- trollers for each robotic platform. During these controller evolution processes, the SNNs and physics models were compared in terms of their accuracy in making pre- dictions of robotic behaviour, their computational efficiency in arriving at these predictions, the human effort required to construct each simulator and, most im- portantly, the real-world performance of controllers evolved by making use of each simulator. The results obtained in this study illustrated experimentally that SNNs were, in the majority of cases, able to make more accurate predictions than the physics- based models and these SNNs were arguably simpler to construct than the physics simulators. Additionally, SNNs were also shown to be a computationally efficient alternative to physics-based simulators in ER and, again in the majority of cases, these SNNs were able to produce controllers which outperformed those evolved in the physics-based simulators, when these controllers were uploaded to the real-world robots. The results of this thesis thus suggest that SNNs are a viable alternative to more commonly-used physics simulators in ER and further investigation of the potential of this simulation technique appears warranted

Learning locomotion gait through hormone-based controller in modular robots

Modular robots are robots composed of multiple units, called 'modules'. Each module is an independent robot, with its own control electronics, actuators, sensors, communications and power. These modules can change their position and configuration in order to adapt to the requirements of the situation, making modular robot suitable for tasks that involve unknown or unstructured terrains, in which a robot cannot be designed speci cally for them. Some examples of those applications are space exploration, battlefield reconnaissance, finding victims among the debris in natural catastrophes and other similar tasks involving complicated terrains, which require a high versability. But this versability comes with several drawbacks. As modular robots are composed of several independent robots, the nature of their controller is distributed, which difficults their design and programming, requiring additionally a robust communication protocol to share information among modules. The high number of modules also results in a robot with a with number of degrees of freedom, for which achieving the coordination required for locomotion becomes increasingly difficult. Finally, as the modules are fully independent robots, the cost of researching modular robotics is usually very high, since the price of building a single robot has to be multiplied by the high number of modules. This thesis addresses those three mentioned problems: obtaining optimal locomotion gaits from a biologically inspired approach, using sinusoidal oscillators whose parameters are found through evolutionary optimization algorithms; developing a homogenous, distributed controller based on digital hormones that can recognize the current robot configuration and select the proper gait; and the development of a low-cost modular robotic platform to reseach locomotion gaits for different configurations.Ingeniería Electrónica Industrial y Automátic

An Analysis Review: Optimal Trajectory for 6-DOF-based Intelligent Controller in Biomedical Application

With technological advancements and the development of robots have begun to be utilized in numerous sectors, including industrial, agricultural, and medical. Optimizing the path planning of robot manipulators is a fundamental aspect of robot research with promising future prospects. The precise robot manipulator tracks can enhance the efficacy of a variety of robot duties, such as workshop operations, crop harvesting, and medical procedures, among others. Trajectory planning for robot manipulators is one of the fundamental robot technologies, and manipulator trajectory accuracy can be enhanced by the design of their controllers. However, the majority of controllers devised up to this point were incapable of effectively resolving the nonlinearity and uncertainty issues of high-degree freedom manipulators in order to overcome these issues and enhance the track performance of high-degree freedom manipulators. Developing practical path-planning algorithms to efficiently complete robot functions in autonomous robotics is critical. In addition, designing a collision-free path in conjunction with the physical limitations of the robot is a very challenging challenge due to the complex environment surrounding the dynamics and kinetics of robots with different degrees of freedom (DoF) and/or multiple arms. The advantages and disadvantages of current robot motion planning methods, incompleteness, scalability, safety, stability, smoothness, accuracy, optimization, and efficiency are examined in this paper

The Development of Bio-Inspired Cortical Feature Maps for Robot Sensorimotor Controllers

Full version unavailable due to 3rd party copyright restrictions.This project applies principles from the field of Computational Neuroscience to Robotics research, in particular to develop systems inspired by how nature manages to solve sensorimotor coordination tasks. The overall aim has been to build a self-organising sensorimotor system using biologically inspired techniques based upon human cortical development which can in the future be implemented in neuromorphic hardware. This can then deliver the benefits of low power consumption and real time operation but with flexible learning onboard autonomous robots. A core principle is the Self-Organising Feature Map which is based upon the theory of how 2D maps develop in real cortex to represent complex information from the environment. A framework for developing feature maps for both motor and visual directional selectivity representing eight different directions of motion is described as well as how they can be coupled together to make a basic visuomotor system. In contrast to many previous works which use artificially generated visual inputs (for example, image sequences of oriented moving bars or mathematically generated Gaussian bars) a novel feature of the current work is that the visual input is generated by a DVS 128 silicon retina camera which is a neuromorphic device and produces spike events in a frame-free way. One of the main contributions of this work has been to develop a method of autonomous regulation of the map development process which adapts the learning dependent upon input activity. The main results show that distinct directionally selective maps for both the motor and visual modalities are produced under a range of experimental scenarios. The adaptive learning process successfully controls the rate of learning in both motor and visual map development and is used to indicate when sufficient patterns have been presented, thus avoiding the need to define in advance the quantity and range of training data. The coupling training experiments show that the visual input learns to modulate the original motor map response, creating a new visual-motor topological map.EPSRC, University of Plymouth Graduate Schoo