A Field Programmable Gate Array (FPGA) is an integrated circuit designed to be configured by a customer or a designer after manufacturing – hence “field-programmable”. For the progress of VLSI technology, the Field programmable gate arrays (FPGAs) have been widely investigated due to their programmable hard-wired feature, fast time-to-market, shorter design cycle, embedding processor, low power consumption and higher density for implementing digital system. FPGA provides a tradeoff between the special-purpose Application Specified Integrated Circuit (ASIC) hardware and general-purpose processors. The novel FPGA technology is able to combine an embedded processor IP (Intellectual Property) and an application IP to be a SoPC (System on-a-Programmable-Chip) developing environment, allowing a user to design a SoPC module. The circuits required with fast processing but simple computation are suitable to be implemented by hardware in FPGA, and the highly complicated control algorithm, reducing development time and cost.
Probabilistic robotics, also called statistical robotics, is a field of robotics that involves the control and behavior of robots in environments subject to unforeseeable events. Because reality always involves uncertainty, probabilistic robotics may help robots to more effectively contend with real-world scenarios. Originally, probabilistic robotics involved the ability of a robot to locate itself using maps of known work environments. A blueprint of the surroundings, along with tools such as proximity sensing and machine vision, allowed a robot to navigate and perform tasks with a minimum of errors or mishaps. More recently, probabilistic robotics has become concerned with the development of robots that can work effectively in environments that they have not previously encountered. Therefore, a robot must develop a sense of the most likely result of a given movement or actions, based on defined statistical functions, and then strive for the optimum outcome.
As shown by the attached diagram, robots create models of environment using probabilities and solve tasks with the help of probabilistic reasoning. The parameters of probabilistic models can be learned using the environmental data.
Probably someday we will have a statistical Black Friday feast.
drjohnxwang 