LameAlice, a Nanorover for Planetary Exploration

The recent advances in micro-technology made it possible to realize very small autonomous robots. Such mobile micro-robots have many potential applications. Especially in space exploration, where weight and size is a very dominant issue, mobile micro-robots enable new mission concepts with multiple robots working in parallel. This makes the mission highly redundant and allows for simultaneous measurements at various places. These ideas motivated us to design and realize the mobile micro-robot LameAlice that might change future concepts for planetary exploration. LameAlice is a four-wheel robot which is composed of two separate blocks with two wheels each. The two blocks are linked with a joint that has two rotational degrees of freedom, allowing optimal adaptability to rough terrain. The robots length is 11 cm, its width 6 cm and its height 4 cm. The weight of the robot is around 40 g including a CCD camera, an on-board controller, two IR emitters for distance measurement, a radio module and the batteries giving an autonomy of a couple of hours.LameAlice moves at a maximum speed of 1 cm/s and is able to overcomes steps of its own height. The wheels are made of flexible blades radially fixed on the axis and powered by watch motors. The on-board camera with a resolution of 256x256 pixels is used for obstacle avoidance enabling fully autonomous operation. The image can also be transmitted to a host computer for remote inspection.

Conception de structures neuronales pour le contrôle de robots mobiles autonomes

Francesco Mondada

There is a large number of possible applications in the field of mobile robotics: Mail delivery robots, domestic or industrial vacuum cleaners, surveillance robots, demining robots and many others could be very interesting products. Despite this potential market and the actual technology, only few simple systems are commercially available. This proves that there are several important and problematic issues in this field, mainly at the intelligence level. As a reaction to the failure of the classical artificial intelligence applied to the field of mobile robotics, several new approaches have been proposed. Artificial neural networks are one of these, and genetic algorithms, supported by the Artificial Life trend, are also getting more and more consideration. These two techniques have already been applied to mobile robotics, but mainly in simulation, and without a final test on a real mobile robot. The use of physical robots for this research seems to be still problematic due to the lack of efficient tools. Several neural structures for the control of mobile robots have been analysed in this work. All experiences have been carried out on physical robots. To reach this goal, an important effort has been made in order to design new efficient robotic tools. Together with Edo Franzi, André Guignard and Yves Cheneval, we have developed and built hardware and software tools that make an efficient research work possible. Along with several analysis software tools, the mobile robot Khepera has been a result of this development. Using this equipment, six experiences have been carried out, covering a large spectrum of the possible ways neural networks can be used for the control of mobile robots. These experiments have nevertheless been restricted to simple behaviours and small neural networks. The first two experiments show, with a very simple and manually adjusted behaviour, the important role of the interaction of the robot with its environment. The first experiment is based on a collective behaviour, the second on a collaborative one. The adaptation of the robot to the environment is introduced in the third experiment, in which a learning technique is applied. The result is a robot able to learn how to use visual stimuli to avoid particular obstacles. Despite its interesting results, this approach has turned out to be very limited, due to the rigid structure needed. The last three experiments demonstrate the possibilities of the use of genetic algorithms, which proved to be a very flexible adaptation mechanism. The first of these three experiments tests the feasibility of this approach. The second one takes advantage of the characteristics of genetic algorithms to achieve more complex behaviours. Finally, genetic algorithms and learning techniques are associated in the last experiment, showing a high adaptive structure. An important effort has been made to show both advantages and disadvantages of each technique, in order to provide the necessary elements for the continuation of this research activity.

EPFL1997

MICRON: Small Autonomous Robot for Cell Manipulation Applications

Jean-Marc Breguet, Walter Driesen

Manipulating in the micro- or even nano world still poses a great challenge to robotics. Conventional (stationary) systems suffer from drawbacks regarding integration into process supervision and multi-robot approaches, which become highly relevant to fight scaling effects. This paper describes work currently being carried out which aims to make automated manipulation of micrometer-scaled objects possible by robots with nanometer precision. The goal is to establish a small cluster of (up to five) micro robots equipped with on-board electronics, sensors and wireless power supply. Power autonomy has been reached using inductive energy transmission from an external wireless power supply system or a battery based system. Electronics requirements are fulfilled in the electronic module with the full custom integrated circuit design for the robot locomotion control and the closed loop force control for AFM tool in cell manipulation applications. The maximum velocity obtained is about 0.4 mm/s with a saw tooth voltage signals of 20Vpp and 2500 Hz. In order to keep a AFM tool on micro-robot a specific tip with integrated piezoresistance, instead of the classical laser beam methodology, is validated for force measurement.

2005

SWARM-BOT: A Swarm of Autonomous Mobile Robots with Self-Assembling Capabilities

Mirco Dorigo, Dario Floreano, Luca Maria Gambardella, André Guignard, Francesco Mondada

We present a new robotic concept, called SWARM-BOT, based on a swarm of small and simple autonomous mobile robots called S-BOTs. S-BOTs have a particular assembling capability that allows them to connect physically to other S-BOTs and form a bigger robot entity, the SWARM-BOT. A SWARM-BOT is typically composed by 10 to 30 S-BOTs physically interconnected. S-BOTs can autonomously assemble into a SWARM-BOT but also disassemble again. This feature of the S-BOTs provides SWARM-BOT with self-assembling and self-reconfiguring capabilities. Such a concept, by taking advantage from the collective and distributed approaches, ensures robustness to failures even in hard environment conditions. The approach presented finds its theoretical roots in recent studies on swarm intelligence.

2002

Conception de structures neuronales pour le contrôle de robots mobiles autonomes

Francesco Mondada

EPFL1997