Research Projects

One of the promising applications of autonomous systems is in human rehabilitation and assistance, especially for severely disabled patients. Such systems require a reliable yet intuitive human interface that does not require neuromuscular control.

Brain Computer Interfaces (BCIs) have been extensively developed in recent years as a potential interface for a range of applications for human rehabilitation and assistance. Non-invasive BCIs captures brain signals through an electroencephalogram (EEG) recording, with an array of electrodes mounted in an EEG-cap . While initial applications of BCI focused mainly on spelling applications more recent applications extend to the control of semi-autonomous rehabilitation robots, the control of neuroprosthetic devices, and navigation of autonomous wheelchairs, and for gaming entertainment. BCIs are especially promising for severely handicapped patients since relevant EEG responses can be elicited even by motor imagery. This is the basis of the Berlin Brain-Computer Interface (BBCI).

The success of BCIs in interpreting human intention depends on proper preprocessing and machine learning techniques for both feature detection and classification. Typical success rates are around 70%. Remaining errors are detected and corrected by the user, thereby hampering the efficiency and transparency of the interface. To facilitate automatic error correction by the BCI itself it needs to detect the EEG activity associated with error detection by the user, and augment the control accordingly. While the importance and potential benefits of error detection for improving BCI systems has been noted, investigations focused mainly on detecting cognitive errors. Here we propose to investigate the existence and detection of movement related errors, which are expected to arise from estimation errors. The proposal is motivated by our recent results from invasive BCIs (known as Brain-Machine Interfaces, BMIs) which indicate that the modulations in the neural activity increases when Monkeys first started to use the interface, and especially after they stopped moving the hand. It is hypothesized that this enhancement is due to increasing variance of estimation errors and control signals. Most importantly, we hypothesize that this enhanced activity would result in detectable changes in EEG activity. Our proposal is targeted at detecting and characterizing EEG potentials related to motor-errors. Once detected these potentials can be used for on-line autonomous motor correction and enhance the application of BCIs handicapped patients.

Tripped roll-over is a major cause of truck accidents: when the wheel of a truck hits the pavement stone lining, or conversely, when a truck tries to get back on a road and hits the elevated asphalt rim, roll-over may occur. Untripped roll-over accidents with SUVs driven off-road with inexperienced drivers is also a serious problem. Warning and prevention of untripped roll-over accidents can save lives. The proposed research will investigate and develop more reliable roll-over indicators. An extended active safety Medlinger roll-over avoidance algorithms will be tested for the first time.

The extensions will include new kinds and combinations of sensors and actuators improving roll-over avoidance control performance and stability. A novel approach, using combined quantitative feedback theory (QFT) and model predictive control (MPC) will be investigated to cope with a wider spectrum of roll-over scenarios. The algorithms will be tested first in simulation and then on an actual autonomous unmanned ground vehicle (UGV) driven in the cooperative autonomous systems (CASY) lab.

Dynamically stable legged robots are promising to provide significant advantages in navigating unstructured terrain. This capability makes them suitable for a range of important applications in surveillance, terrain exploration, rescue missions and service.

Dynamically stable legged robots are commonly controlled by networks of coupled oscillators, which model the biological central pattern generators. These systems are capable of autonomously generating and, most importantly, preserving the desired gait despite disturbances. However, disturbances do affect the direction and speed of motion, so while movement continues, the goal-directed behavior is sacrificed. Thus, additional control strategies are needed to modulate the gait and correct the speed and direction of movement.

Here we propose to integrate event-based sensory feedback to track and modulate the gait. Gait modulation is expected to reduce the effect of disturbances on the planned trajectory, and gait tracking to support self-tracking, These capabilities are critical for autonomous goal directed behavior.
Specifically, the timing of leg touch-downs will be detected using simple force sensors incorporated in the legs. These events will be used as direct or indirect inputs to the network of coupled oscillators, for gait tracking and adaptation. This proposal is motivated and extends our successful application of a similar approach to the control of a robotic yoyo.

More generally, legged locomotion is an example of hybrid dynamical systems in which the continuous-time dynamics change at discrete events (here leg touch-downs and lift-ups). In this framework, the long term goal of this proposal is to investigate the use of event-timing for controlling hybrid systems.

In this study we look for optimal algorithms for patrolling, searching and surveillance using multiple cooperative autonomous unmanned aerial vehicles (UAVs).

The proposed algorithms will employ realistic dynamical modeling as well as flocking rules via inter-vehicle ad-hoc communication.

Combining some recent progress in solving for the optimal search pattern of UAV flocks (MARS Lab) and recent results in inter-vehicle communication, simulation and control (DSSL) will lead to beyond the state-of-the-art algorithms for cooperative self-organization and search using UAV flocks.