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Robotics - Current Research

The robots of tomorrow will be the direct result of the robotic research projects of today. The goals of most robotic research projects is the advancement of abilities in one or more of the following technological areas:

These technological advances will lead to improvements and innovations in the application of robotics to industry, medicine, the military, space exploration, underwater exploration, and personal service. The research projects listed below are only a few of many robotic research projects worldwide.

Artificial Intelligence

Human Behavior and Emotion

Cog, a humanoid robot from MIT.

Two of the many research projects of the MIT Artificial Intelligence department include an artificial humanoid called Cog, and his baby brother, Kismet. What the researchers learn while putting the robots together will be shared to speed up development.

Once finished, Cog will have everything except legs, whereas Kismet has only a 36-kilogram head that can display a wide variety of emotions. To do this Kismet has been given movable facial features that can express basic emotional states that resemble those of a human infant. Kismet can thus let its "parents" know whether it needs more or less stimulation--an interactive process that the researchers hope will produce an intelligent robot that has some basic "understanding" of the world.

This approach of creating AI by building on basic behaviors through interactive learning contrasts with older methods, in which a computer is loaded with lots of facts about the world in the hope that intelligence will eventually emerge.

Cog is 2 meters tall, complete with arms, hands and all three senses--including touch-sensitive skin. Its makers will eventually try to use the same sort of social interaction as Kismet to help Cog develop intelligence equivalent to that of a two-year-old child.

Kismet is an autonomous robot designed for social interactions with humans and is part of the larger Cog Project. This project focuses not on robot-robot interactions, but rather on the construction of robots that engage in meaningful social exchanges with humans. By doing so, it is possible to have a socially sophisticated human assist the robot in acquiring more sophisticated communication skills and helping it learn the meaning these acts have for others.

Kismet with its creator, Cybthia Breazeal
of MIT.  Breazeal also helped create Cog.

Kismet has a repertoire of responses driven by emotive and behavioral systems. The hope is that Kismet will be able to build upon these basic responses after it is switched on or "born", learn all about the world and become intelligent.

Crucial to its drives are the behaviors that Kismet uses to keep its emotional balance. For example, when there are no visual cues to stimulate it, such as a face or toy, it will become increasingly sad and lonely and look for people to play with.

Any advances made with Kismet will be passed on to its big brother Cog, the robot brainchild of Rodney Brooks, head of MIT's AI department.

Hardware and Software Brains

In mimicking human intelligence, the goal is to make sure robots get a brain and reasoning. An important pioneer in the field of AI is Marvin Minsky.

Without a brain capable of processing input, a robot cannot react to its environment. A brain can be stimulated in hardware or software. Most robots at present have software brains, meaning a computer with a program running. These robots are connected to or equipped with a computer. A drawback is the limited number of processes that can be run on today's computers and the single purpose programs running on these computers. The programs cannot change themselves.  In other words, learning is not possible.

A brain made out of hardware, or a number of processors will be closer to reality. The brain will consists of several chips that act both independently and as a group.  The general belief is that the real brain works as a neural network of lots of independent processing units. Every chip in itself has a small program. It will process information but also pass it on to other chips. The program changes on a continuous basis. The network of chips is quick and will adapt, so in contrast with the software brain, it will learn.

An example of a hardware brain is Robokoneko the robocat from Genobyte. It has a  brain from a machine, the CAM-machine.

Effectors and Mobility

Autonomous Flying Vehicle Project

Robot helicopter research began at the University of Southern California in 1991 with the formation of the Autonomous Flying Vehicle Project and continues to the present day. The first robot built was the AFV (Autonomous Flying Vehicle). The AVATAR (Autonomous Vehicle Aerial Tracking And Retrieval), was created in 1994. The current robot, the second generation AVATAR (Autonomous Vehicle Aerial Tracking And Reconnaissance), was developed in 1997. The ``R'' in AVATAR changed to reflect a change in robot capabilities.

Fish Robot

Without question, the fish is the best swimmer in the world. That is why the Ship Research Institute of Japan decided to build the Fish Robot. This project hopes to apply what is learned while building and researching with the Fish Robot to the design and construction of ships.


Robots use electro-engines for movement. Engine parts are relatively cheap and last long. Engines are applied to move arm, turn wheels or move other parts, for instance camera's. Engines are less usefull with walking robots. In that particular case engines prove to be a weak part, a jumping robot is a mayor challenge to engine parts. Human being use muscle, which contract and expand, to move around. A muscle receive a signal form the brain and contracts. Causing a joint, like the knee to move.

Material to mimic a muscle is still a dream. Nitinol, an alloy that consist of the metals nickel and titanium will shrink if an electric current travels through the alloy, it will only contract 8% maximum.

The downside, nitinol is very expensive en the contraction is too little to allow it to be used to make walking robots. For the time being walking robots will not use muscles or engines but pneumatic of hydrolic technologies.


To demonstrate advances in research and to stimulate scientist to share progress the Robocup competition is organized a few times a year. Robocup is a competition of Robot soccer teams. Movement, pattern recognition, where's the ball, where's the goal, who is in my team, all this and more is needed to score a goal. A simple games becomes a challenge for a robot team. Besides moving and finding the ball and team members the robots needs to define a strategy and take lots of decisions in a short time frame. Robocup has produced many advancements in both robotic effectors and sensors. Who could have imagined that soccer would contribute to robot research where robots eventually will be smart and capable of cooperation with other to reach a goal?

Sensor Detection

Robotic Vision

Machine vision involves devices which sense images and processes which interpret the images.  Thomas Braunl of The University of Western Australia provides an excellent example of robotic vision research in both hardware and software.

EyeBot is a controller for mobile robots with wheels, walking robots or flying robots. It consists of a powerful 32-Bit microcontroller board with a graphics display and a digital grayscale or color camera. The camera is directly connected to the robot board (no frame grabber). This allows programmers to write powerful robot control programs without a big and heavy computer system and without having to sacrifice vision.

  • Ideal basis for programming of real time image processing

  • Integrated digital camera (grayscale or color)

  • Large graphics display (LCD)

  • Can be extended with own mechanics and sensors to full mobile robot

  • Programmed from IBM-PC or Unix workstation,
    programs are downloaded via serial line (RS-232) into RAM or Flash-ROM

  • Programming in C or assembly language

Improv is a tool for basic real time image processing with low resolution, e.g. suitable for mobile robots. It has been developed for PCs with Linux operating system. Improv works with a number of inexpensive low-resolution digital cameras (no framegrabber required), and is available from Joker Robotics.   Improv displays the live camera image in the first window, while subsequent image operations can be applied to this image in five more windows. For each sub-window, a sequence of image processing routines may be specified.

Sensor Based Motion Planning

Sensor Based Planning incorporates sensor information, reflecting the current state of the environment, into a robot's planning process, as opposed to classical planning , where full knowledge of the world's geometry is assumed to be known prior to the planning event. Sensor based planning is important because: (1) the robot often has no a priori knowledge of the world; (2) the robot may have only a coarse knowledge of the world because of limited memory; (3) the world model is bound to contain inaccuracies which can be overcome with sensor based planning strategies; and (4) the world is subject to unexpected occurrences or rapidly changing situations.

There already exists a large number of classical path planning methods. However, many of these techniques are not amenable to sensor based interpretation. It is not possible to simply add a step to acquire sensory information, and then construct a plan from the acquired model using a classical technique, since the robot needs a path planning strategy in the first place to acquire the world model.

The first principal problem in sensor based motion planning is the find-goal problem. In this problem, the robot seeks to use its on-board sensors to find a collision free path from its current configuration to a goal configuration. In the first variation of the find goal problem, which we term the absolute find-goal problem, the absolute coordinates of the goal configuration are assumed to be known. A second variation on this problem is described below.

The second principal problem in sensor based motion planning is sensor-based exploration, in which a robot is not directed to seek a particular goal in an unknown environment, but is instead directed to explore the apriori unknown environment in such a way as to see all potentially important features. The exploration problem can be motivated by the following application. Imagine that a robot is to explore the interior of a collapsed building, which has crumbled due to an earthquake, in order to search for human survivors. It is clearly impossible to have knowledge of the building's interior geometry prior to the exploration. Thus, the robot must be able to see, with its on-board sensors, all points in the building's interior while following its exploration path. In this way, no potential survivors will be missed by the exploring robot. Algorithms that solve the find-goal problem are not useful for exploration because the location of the ``goal'' (a human survivor in our example) is not known. A second variation on the find-goal problem that is motivated by this scenario and which is an intermediary between the find-goal and exploration problems is the recognizable find-goal problem. In this case, the absolute coordinates of the goal are not known, but it is assumed that the robot can recognize the goal if it becomes with in line of sight. The aim of the recognizable find-goal problem is to explore an unknown environment so as to find a recognizable goal. If the goal is reached before the entire environment is searched, then the search procedure is terminated.

Control Systems

Hierarchical Behavior Control

Development of a hierarchical behavior control scheme for rovers and mobile robots is currently underway. It attempts to model and control mobile systems using distinct rule-based controllers and decision-making subsystems that collectively represent a hierarchical decomposition of autonomous vehicle behavior. This research approach employs fuzzy logic, behavior control, and genetic programming as tools for developing autonomous robots. Complex, multi-variable fuzzy rule-based systems are developed in the framework of behavior-based control for autonomous navigation. Genetic programming methods are used to computationally evolve fuzzy coordination rules for low-level motion behaviors. In addition, embedded control applications are being developed for microrover navigation using conventional microprocessors and specialized fuzzy VLSI chips.

Nano Technology and Medical Applications

The movie Innerspace shows a miniature spaceship travelling through the artery system of a human. It is a nice illustration of the promise of Nano technology. Nano Technology is a technique where miniature robots go to places humans will never be able to travel. Nano technology is a new science where robotics play a mayor part. Questions that needs to be solved because of the very tiny mechinical parts: can a robot repair itself, how do you control a nano robot, how does a nano robot move. Will it be able to work autonomously. Will it be able so shift in shape. Is a nanan robot a mechanical device or is it more like a microprocessor. Once these questions are answered Nano technology will change medical science for ever. Surgery will be performed in lots of cases by one or more Nano robots that will travel inside the human body.

Intelligent Systems for Communication Networks

Third generation wireless networks like the Universal Mobile telecommunication System (UMTS) are being developed to support wide band services. A major scenario is to support such services of a user roaming between a cellular terrestrial network and a satellite Personal Communication Network (PCN) while maintaining the quality of service during the hand-over process. And requiring some degree of continuity of quality of service guarantees.  This project will focus on developing new protocols which uses artificial intelligent systems to support such hand-over process. Further Seamless roaming and user tracking using intelligent systems will be investigated.

Active Vibration Control

In recent years, the reduction of undesirable vibrations in the dynamic systems such as airplanes, vehicles, tall buildings and off-shore structures has become a crucial issue due to the increased social awareness of comfort as well as the ever increasing heights of new inner city buildings. With the advent of new construction materials and new construction methods, the buildings and structures are becoming taller, and more flexible. With a good design and under normal loading conditions, the response of these structures to vibrations will remain in the safe and comfortable domain. However, there is no guarantee that in-service loads experienced by tall buildings and structures will always be in the allowed range. The undesirable vibration levels could be reached under large environmental loads such as winds and earthquakes, and could adversely affect human comfort and even structural safety. It is becoming critically important to suppress dynamic responses of tall buildings and structures due to the strong winds and earthquakes not only for their safety but also their serviceability. When tall buildings and structures are flexible, design performances may become impossible to achieve by conventional design practice. Hence, additional devices are installed in tall buildings and structures to compensate the dynamic responses caused by environmental loads. As a result, new concepts and methods of structural protection have been proposed.

Due to recent development of sensors and digital control techniques, active control methods of dynamic responses of tall buildings and structures have been developed, and some of them have been implemented to actual buildings. The precondition is however that the implementation is simple enough to be realtime. In engineering applications with rule based systems providing efficient results, the implementation is often easier than its complex conventional counterpart.

The active vibration control in the structural engineering has become known as an area of research in which the vibrations and motions of the tall buildings and structures can be controlled or modified by means of the actions of a control system through some external energy supply. Compared with the passive vibration control the active vibration control can more effectively keep the tall buildings and structures safe and comfortable under the various environmental loads such as strong winds or earthquake hazards. This implies that the active vibration control can be effective and adaptive over a much wider frequency range and also for transient vibration, which is the reason to attract interest of the researchers not only in structural engineering but also in control engineering. Among many methods, that have been proposed, are active mass drivers (AMDs), active tendon systems (ATS), and active variable stiffness systems (AVSs).

Hyper-Redundant Robotics Systems

Robot manipulators which have more than the minimum number of degrees-of-freedom are termed ``kinematically redundant,'' or simply ``redundant.'' Redundancy in manipulator design has been recognized as a means to improve manipulator performance in complex and unstructured environments. ``Hyper-redundant'' robots have a very large degree of kinematic redundancy, and are analogous in morphology and operation to snakes, elephant trunks, and tentacles. There are a number of very important applications where such robots would be advantageous.

While ``snake-like'' robots have been investigated for nearly 25 years, they have remained a laboratory curiousity. There are a number of reasons for this: (1) previous kinematic modeling techniques have not been particularly efficient or well suited to the needs of hyper-redundant robot task modeling; (2) the mechanical design and implementation of hyper-redundant robots has been perceived as unnecessarily complex; and (3) hyper-redundant robots are not anthropomorphic, and therefore pose interesting programming problems. Our research group has undertaken a broadly based program to overcome the obstacles to practical deployment of hyper-redundant robots.


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