Robots and autonomous systems have been equipped with enhanced features over the past decade, thanks to the advances in signal processing, which provides efficient and appropriate analysis of sensor data and enable autonomy.
Signal processing output is highly significant in robots and autonomous systems mainly for managing uncertainty.
Signal processing ensures that there is sufficient sensor data, future autonomous actions are controlled and communication lines are optimal.
Mathai NJ et al (2008) conducted research to design lightweight signal processing and control architectures for multi-robot system agents.
The design of an analog-amenable signal processing scheme was presented. Dynamic and control systems theory was used both as a synthesis toolset and as a description language to thoroughly develop the computational machinery.
These mechanisms are combined with structural insights from behaviour-based robotics to design overall algorithmic architectures.
According to these researchers, robotic behaviour includes actions by an agent to ensure that the way it perceives its environment evolves in a specific desired pattern.
The researchers provided an intuitive aid to design this behavioural pattern by presenting a new visual tool, an inspired vector field design that enables the designer to exploit the environment dynamics.
Ludwig L et al (2006) studied dispersing robotic swarms to cover a potentially hostile, unknown area to set up a sensor surveillance network.
In previous research studies, each robot knows the relative location of the neighbouring robot through sensors. However, it is difficult for extremely small robots to carry sensors.
Wireless signal intensity is used as a rough distance approximation to support a large group of dispersed robots.
Simulation experiments show that a swarm is capable of dispersing effectively by using wireless signal intensities without knowing the relative location of neighbouring robots.
Li Tzuu-Hseng HS et al (2004) used infrared sensors to develop a real-time fuzzy target tracking control scheme for autonomous mobile robots.
Initially two mobile robots are set up, wherein one is a tracker mobile robot having infrared receivers and reflective sensors and the other is a target mobile robot with infrared transmitters.
The transmitter robot is designed to drive in a particular trajectory and the receiver robot is designed to monitor the target mobile robot.
The research includes design of the fuzzy target tracking control unit, which includes a gate network and a behavior network.
The behaviour network included the fuzzy target tracking control (FTTC) mode, the fuzzy wall following control (FWFC) mode and two fixed control modes to deal with a number of situations in real applications. The fuzzy sliding-mode control enables switching between the FWFC and FTTC modes.
The combined measurement of two infrared sensors is done using a gate network and is developed to identify which action must be executed and which situation is belonged to.
Target tracking control with obstacle avoidance is also studied in this research. Real-time implementation experiments and computer simulations show the feasibility and efficiency of the proposed control schemes.
Signal processing opens up more avenues for robotic applications and as signal processing becomes more and more sophisticated, we can look forward to more intelligent and co-ordinated robots.
The applications of signal processing in robotic sectors include:
Autonomous navigation, robot teams or swarms of robots and target tracking.
Remote health monitoring, neurobiological surveillance systems and fall detection for aged patients.
Decoding electroencephalography (EEG) for brain computer interfaces and prosthetics.
Infrared sensor network for intruder detection, interactive LED dancefloor and biologically inspired chemical sensing.
Home security with ultrasound and infrared sensors, infrared sensing for mapping the environment.
Mobile robots with sensor arrays and design assistive technologies for individuals with cognitive disabilities.
Over the last 15 years, signal processing has witnessed a lot of change and will change more in the coming years.
Signal processing today can be defined as the method of manipulating, analyzing and presenting natural information.
New imaging and communication technologies have improved individual capabilities to interact with the environment in a multi-modal manner and latest advancements in biology such as microarrays have resulted in a better understanding of biological processes.
The key to success will be to combine new innovations with strong theoretical foundations and understand the requirements of practical implementations.