Piezoelectric Grippers for Mobile Micromanipulation

The ability to efficiently and precisely manipulate objects in inaccessible environments is becoming an essential requirement for many applications of mobile robots, particularly at small sizes. Here, we propose and implement a mobile micromanipulation solution using a piezoelectric microgripper integrated into a dexterous robot, HAMR (the Harvard Ambulatory MicroRobot), that has a size of approximately 4.5 cm by 4 cm by 2.3 cm and a maximum payload of approximately 3 g. Our 100 mg miniature gripper is composed of recurve piezoelectric actuators that produce parallel jaw motions (stroke of 236 +/- 32 mu m at 200 V) while providing high gripping forces (blocked force of 0.575 N at 200 V), making it effective for micromanipulation applications with tiny objects. Using this gripper, we successfully demonstrated a grasping and lifting task with an object of 1.3 g and thickness of 250 mu m at an operating voltage of 100 V. Finally, by taking advantage of the locomotion capabilities ofHAMR, wedemonstrate mobile manipulation by changing the position and orientation of small objects weighing up to 2.8 g controlled by the movement of the robot. We expect that the addition of this novel manipulation capability will increase the effectiveness of such miniature robots for accomplishing real-world tasks.

Autonomous Construction by a Mobile Robot in Unknown Environments with Scarce Resources

Thierry Barras, Michael Bonani, Daniel Burnier, Valentin Longchamp, Stéphane Magnenat, Francesco Mondada, Philippe Rétornaz, Frédéric Rochat, Patrick Schoeneich, Florian Christopher Vaussard, Martin Voelkle

Autonomous construction by mobile robots would be useful in various situations, such as in outer space, in hazardous environments, but also for the building industry. Current works tackle simplified scenarios where environment is flat and resources readily available; moreover robots build simple structures. However, target applications would feature complex 3D environments, remote resources, and would require the construction of multi-layer structures of various types. In this poster, we show our current work in pursuing a step toward such applications. We propose a simple experimental setup where a miniature mobile robot senses its environment and finds the right course of action to build structures. Resources are remote, requiring the robot to first locate them, then to create a way to fetch them and finally to build the structure. The robot performs SLAM to build a map and uses an HTN planner to choose its actions. It manipulates its environment through a magnetic gripper.

2010

Software integration in mobile robotics, a science to scale up machine intelligence

Stéphane Magnenat

The present work tackles integration in mobile robotics. Integration is often considered to be a mere technique, unworthy of scientific investigation. On the contrary, we show that integrating capabilities in a mobile robot entails new questions that the parts alone do not feature. These questions reflect the structure of the application and the physics of the world. We also show that a successful integration process transforms the parts themselves and allows to scale up mobile-robot intelligence in real-world applications. In Chapter 2 we present the hardware. In Chapter 3, we show that building a low-level control architecture considering the mechanic and electronic reality of the robot improves the performances and allows to integrate a large number of sensors and actuators. In Chapter 4, we show that globally optimising mechatronic parameters considering the robot as a whole allows to implement slam using an inexpensive sensor with a low processor load. In Chapter 5, we show that based on the output from the slam algorithm, we can combine infrared proximity sensors and vision to detect objects and to build a semantic map of the environment. We show how to find free paths for the robot and how to create a dual geometric-symbolic representation of the world. In Chapter 6, we show that the nature of scenarios influences the implementation of a task-planning algorithm and changes its execution properties. All these chapters contribute results that together prove that integration is a science. In Chapter 7, we show that combining these results improves the state of the art in a difficult application : autonomous construction in unknown environments with scarce resources. This application is interesting because it is challenging at multiple levels : For low-level control, manipulating objects in the real world to build structures is difficult. At the level of perceptions, the fusion of multiple heterogeneous inexpensive sensors is not trivial, because these sensors are noisy and the noise is non-Gaussian. At the level of cognition, reasoning about elements from an unknown world in real time on a miniature robot is demanding. Building this application upon our other results proves that integration allows to scale up machine intelligence, because this application shows intelligence that is beyond the state of the art, still only combining basic components that are individually slightly behind the state of the art.

EPFL2010

Adaptive Lévy Taxis for Odor Source Localization in Realistic Environmental Conditions

Romain Jean-Paul Emery, Ali Marjovi, Alcherio Martinoli, Faezeh Rahbar

Odor source localization with mobile robots has recently been subject to many research works, but remains a challenging task mainly due to the large number of environmental parameters that make it hard to describe gas concentration fields. We designed a new algorithm called Adaptive Lévy Taxis (ALT) to achieve odor plume tracking through a correlated random walk. In order to compare its performances with well-established solutions, we have implemented three moth-inspired algorithms on the same robotic platform. To improve the performance of the latter algorithms, we developed a rigorous way to determine one of their key parameters, the odor concentration threshold at which the robot considers to be inside or outside the plume. The methods have been systematically evaluated in a large wind tunnel under various environmental conditions. Experiments revealed that the performance of ALT is consistently good in all environmental conditions (in particular when compared to the three reference algorithms) in terms of both distance traveled to find the source and success rate.

2017

Software integration in mobile robotics, a science to scale up machine intelligence

Stéphane Magnenat

EPFL2010