Toni Machado

Object transportation by multiple mobile robots controlled by attractor dynamics: theory and implementation

Dynamical systems theory is used as a theoretical language and tool to design a distributed control architecture for teams of mobile robots, that must transport a large object and simultaneously avoid collisions with (either static or dynamic) obstacles. Here we demonstrate in simulations and implementations in real robots that it is possible to simplify the architectures presented in previous work and to extend the approach to teams of n robots. The robots have no prior knowledge of the environment. The motion of each robot is controlled by a time series of asymptotical stable states. The attractor dynamics permits the integration of information from various sources in a graded manner. As a result, the robots show a strikingly smooth an stable team behaviour.

The power of prediction: Robots that read intentions

Humans are experts in cooperating in a smooth and proactive manner. Action and intention understanding are critical components of efficient joint action. In the context of the EU Integrated Project JAST [16] we have developed an anthropomorphic robot endowed with these cognitive capacities. This project and respective robot (ARoS) is the focus of the video. More specifically, the results illustrate crucial cognitive capacities for efficient and successful human-robot collaboration such as goal inference, error detection and anticipatory action selection. Results were considered one of the ICT “success stories”[22].

Transportation of long objects in unknown cluttered environments by a team of robots: A dynamical systems approach

We present a distributed architecture for teams of two autonomous mobile robots that act in coordination in a joint transportation task of long objects. The team is able to perform its transportation task in unknown environments while avoiding static or moving obstacles. The working environment can be cluttered and with narrow passages such as corridors, corners and doors. These characteristics make our approach suitable to be deployed in warehouses or office-like environments. The control architecture of each robot is formalized as a non-linear dynamical system, where by design attractor states dominate. The overt behavior is smooth and stable, because it is generated as a time sequence of attractor states, for the control variables, which contributes to the overall asymptotically stability of the system that makes it robust against perturbations. We present results with real robots in a real indoor cluttered environment.

Experiential Learning of Robotics Fundamentals Based on a Case Study of Robot-Assisted Stereotactic Neurosurgery

Robotics has been playing an important role in modern surgery, especially in procedures that require extreme precision, such as neurosurgery. This paper addresses the challenge of teaching robotics to undergraduate engineering students, through an experiential learning project of robotics fundamentals based on a case study of robot-assisted stereotactic neurosurgery. The project was integrated into the curriculum of a Biomedical Engineering and Electrical and Computer Engineering program, but can also be integrated in related courses. First, students are given a presentation on the planning and execution of a stereotactic neurosurgery procedure, with special attention being paid to the concepts involved, namely spatial transformations, kinematics, and trajectory planning. Students are then taught to use a robotics simulation tool for robot-assisted stereotactic neurosurgery. They are shown how this can be used as a specialized control application, providing direct feedback on the robots motion in a neurosurgery scenario. They are then required to select a robotic manipulator, and to develop and implement its control code to make it perform as a robot assistant in this surgical procedure. Project efficacy was evaluated through student self-report data (with dedicated anonymous surveys) and through the impact on academic and pedagogical results (by means of statistical inference). The results of the student surveys show that the robotics simulator for stereotactic neurosurgery is well suited to its role as an experiential learning tool since it enhances the understanding and application of several robotics concepts in an appealing manner. The positive impact of the project learning experience is supported by a comparison to earlier years of student grades, pass rates, and feedback from an institutional survey.

Multi-constrained joint transportation tasks by teams of autonomous mobile robots using a dynamical systems approach

We present a distributed leader-helper architecture for teams of two autonomous mobile robots that jointly transport large payloads while avoiding collisions with obstacles (either static or dynamic). The leader navigates to the goal destination and the helper is responsible for maintaining an appropriate distance (which is a function of the objects length) to the leader. Both robots share the responsibility of ensuring that the transported object does not collide with obstructions. No path needs to be given a priori to the robots nor to the payload. The team is able to perform its transportation task in unknown environments that can have corridors, corners and may change the layout online. The payload can be of different dimensions. The team is able to cope with abrupt/strong perturbations that challenge the team behavior during the execution of the task. These characteristics make this approach suitable to be deployed in warehouses or office-like environments. The motion of each robot is controlled by a time series asymptotically stable states, which is formalized using the attractor dynamics approach to behavior based robotics. The advantages are: (i) the overt behavior is smooth and stable; (ii) because the behavior is generated as a time sequence of attractor states, for the control variables, it contributes to the overall asymptotically stability of the system that makes it robust against perturbations. We present results of experiments in simulated environments and with real robots in real environments.

Multi-robot cognitive formations

In this paper, we show how a team of autonomous mobile robots, which drive in formation, can be endowed with basic cognitive capabilities. The formation control relies on the leader-follower strategy, with three main pair-wise configurations: column, line and oblique. Furthermore, non-linear attractor dynamics are used to generate basic robotic behaviors (i.e. follow-the-leader and avoid obstacles). The control architecture of each follower integrates a representation of the leader (target) direction, which supports leader detection, selection between multiple leaders (decision) and temporary estimation of leader direction (short-term memory during occlusion and prediction). Formalized as a dynamic neural field, this additional layer is smoothly integrated with the motor movement control system. Experiments conducted in our 3D simulation software, as well as results from the implementation in middle size robotic platforms, show the ability for the team to navigate, whilst keeping formation, through unknown and unstructured environments and is robust against ambiguous and temporarily absent sensory information.