FPGA for Robotic Applications: from Android/Humanoid Robots to Artificial Men

Researches on home robots have been increasing enormously. There has always existed a continuous research effort on problems of anthropomorphic robots which is now called humanoid robots. Currently, robotics has evolved to the point that different branches have reached a remarkable level of maturity, that neural network and fuzzy logic are the main artificial intelligence as intelligent control on the robotics. Despite all this progress, while aiming at accomplishing work-tasks originally charged only to humans, robotic science has perhaps quite naturally turned into the attempt to create artificial men. It is true that artificial men or android humanoid robots open certainly very broad prospects. This “robot” may be viewed as a personal helper, and it will be called a home-robot, or personal robot

"Going back to our roots": second generation biocomputing

Researchers in the field of biocomputing have, for many years, successfully "harvested and exploited" the natural world for inspiration in developing systems that are robust, adaptable and capable of generating novel and even "creative" solutions to human-defined problems. However, in this position paper we argue that the time has now come for a reassessment of how we exploit biology to generate new computational systems. Previous solutions (the "first generation" of biocomputing techniques), whilst reasonably effective, are crude analogues of actual biological systems. We believe that a new, inherently inter-disciplinary approach is needed for the development of the emerging "second generation" of bio-inspired methods. This new modus operandi will require much closer interaction between the engineering and life sciences communities, as well as a bidirectional flow of concepts, applications and expertise. We support our argument by examining, in this new light, three existing areas of biocomputing (genetic programming, artificial immune systems and evolvable hardware), as well as an emerging area (natural genetic engineering) which may provide useful pointers as to the way forward.Comment: Submitted to the International Journal of Unconventional Computin

A contribution to vision-based autonomous helicopter flight in urban environments

A navigation strategy that exploits the optic flow and inertial information to continuously avoid collisions with both lateral and frontal obstacles has been used to control a simulated helicopter flying autonomously in a textured urban environment. Experimental results demonstrate that the corresponding controller generates cautious behavior, whereby the helicopter tends to stay in the middle of narrow corridors, while its forward velocity is automatically reduced when the obstacle density increases. When confronted with a frontal obstacle, the controller is also able to generate a tight U-turn that ensures the UAV’s survival. The paper provides comparisons with related work, and discusses the applicability of the approach to real platforms

On microelectronic self-learning cognitive chip systems

After a brief review of machine learning techniques and applications, this Ph.D. thesis examines several approaches for implementing machine learning architectures and algorithms into hardware within our laboratory. From this interdisciplinary background support, we have motivations for novel approaches that we intend to follow as an objective of innovative hardware implementations of dynamically self-reconfigurable logic for enhanced self-adaptive, self-(re)organizing and eventually self-assembling machine learning systems, while developing this new particular area of research. And after reviewing some relevant background of robotic control methods followed by most recent advanced cognitive controllers, this Ph.D. thesis suggests that amongst many well-known ways of designing operational technologies, the design methodologies of those leading-edge high-tech devices such as cognitive chips that may well lead to intelligent machines exhibiting conscious phenomena should crucially be restricted to extremely well defined constraints. Roboticists also need those as specifications to help decide upfront on otherwise infinitely free hardware/software design details. In addition and most importantly, we propose these specifications as methodological guidelines tightly related to ethics and the nowadays well-identified workings of the human body and of its psyche

Design and Development of Intelligent Navigation Control Systems for Autonomous Robots that Uses Neural Networks and Fuzzy Logic Techniques and Fpga For Its Implementation

This research compares the behavior of three robot navigation controllers namely: PID, Artificial Neural Networks (ANN), and Fuzzy Logic (FL), that are used to control the same autonomous mobile robot platform navigating a real unknown indoor environment that contains simple geometric-shaped static objects to reach a goal in an unspecified location. In particular, the study presents and compares the design, simulation, hardware implementation, and testing of these controllers. The first controller is a traditional linear PID controller, and the other two are intelligent non-linear controllers, one using Artificial Neural Networks and the other using Fuzzy Logic Techniques. Each controller is simulated first in MATLAB® using the Simulink Toolbox. Later the controllers are implemented using Quartus ll® software and finally the hardware design of each controller is implemented and downloaded to a Field-Programmable Gate Array (FPGA) card which is mounted onto the mobile robot platform. The response of each controller was tested in the same physical testing environment using a maze that the robot should navigate avoiding obstacles and reaching the desired goal. To evaluate the controllers\u27 behavior each trial run is graded with a standardized rubric based on the controllers\u27 ability to react to situations presented within the trial run. The results of both the MATLAB® simulation and FPGA implementation show the two intelligent controllers, ANN and FL, outperformed the PID controller. The ANN controller was marginally superior to the FL controller in overall navigation and intelligence

A High Performance Fuzzy Logic Architecture for UAV Decision Making

The majority of Unmanned Aerial Vehicles (UAVs) in operation today are not truly autonomous, but are instead reliant on a remote human pilot. A high degree of autonomy can provide many advantages in terms of cost, operational resources and safety. However, one of the challenges involved in achieving autonomy is that of replicating the reasoning and decision making capabilities of a human pilot. One candidate method for providing this decision making capability is fuzzy logic. In this role, the fuzzy system must satisfy real-time constraints, process large quantities of data and relate to large knowledge bases. Consequently, there is a need for a generic, high performance fuzzy computation platform for UAV applications. Based on Lees’ [1] original work, a high performance fuzzy processing architecture, implemented in Field Programmable Gate Arrays (FPGAs), has been developed and is shown to outclass the performance of existing fuzzy processors

A survey on fractional order control techniques for unmanned aerial and ground vehicles

In recent years, numerous applications of science and engineering for modeling and control of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) systems based on fractional calculus have been realized. The extra fractional order derivative terms allow to optimizing the performance of the systems. The review presented in this paper focuses on the control problems of the UAVs and UGVs that have been addressed by the fractional order techniques over the last decade

Implementation of Block-based Neural Networks on Reconfigurable Computing Platforms

Block-based Neural Networks (BbNNs) provide a flexible and modular architecture to support adaptive applications in dynamic environments. Reconfigurable computing (RC) platforms provide computational efficiency combined with flexibility. Hence, RC provides an ideal match to evolvable BbNN applications. BbNNs are very convenient to build once a library of neural network blocks is built. This library-based approach for the design of BbNNs is extremely useful to automate implementations of BbNNs and evaluate their performance on RC platforms. This is important because, for a given application there may be hundreds to thousands of candidate BbNN implementations possible and evaluating each of them for accuracy and performance, using software simulations will take a very long time, which would not be acceptable for adaptive environments. This thesis focuses on the development and characterization of a library of parameterized VHDL models of neural network blocks, which may be used to build any BbNN. The use of these models is demonstrated in the XOR pattern classification problem and mobile robot navigation problem. For a given application, one may be interested in fabricating an ASIC, once the weights and architecture of the BbNN is decided. Pointers to ASIC implementation of BbNNs with initial results are also included in this thesis