Making robots that are able to localize, track and separate multiple sound sources, even in noisy places, is essential for their deployment in our everyday environments. This could for example allow them to process human speech, even in crowded places, or identify noises of interest and where they came from. Unlike vision however, there are few software and hardware tools that can easily be integrated to robotic platforms.

The ManyEars open source framework allows users to easily experiment with robot audition. The software, which can be downloaded here, is compatible with ROS (Robot Operating System). Its modular design makes it possible to interface with different microphone configurations and hardware, thereby allowing the same software package to be used for different robots. A Graphical User Interface is provided for tuning parameters and visualizing information about the sound sources in real-time. The figure below illustrates the architecture of the ManyEars software library. It is composed of five modules: Preprocessing, Localization, Tracking, Separation and Postprocessing.

To make use of the ManyEars software, a computer, a sound card and microphones are required. ManyEars can be used with commercially available sound cards and microphones. However, commercial sound cards present limitations when used for embedded robotic applications: they can be expensive and have functionalities which are not required for robot audition. They also require significant amount of power and size. For these reasons, the authors introduce a customized microphone board and sound card available as an open hardware solution that can be used on your robot and interfaced with the software package. The board uses an array of microphones, instead of only one or two, thereby allowing a robot to localize, track, and separate multiple sound sources.

The framework is demonstrated using the the microphones array on the IRL-1 robot shown below. The placement of the microphones is marked by red circles. Results show that the robot is able to track two human speakers producing uninterrupted speech sequences, even when they are moving, and crossing paths. For videos of the IRL-1, check out the lab’s YouTube Channel.

Most research in robotics focuses on a specific problem: building better hardware, implementing new algorithms, or demonstrating a new task. Combining all these state-of-the-art ingredients into a single system is the key to making autonomous robots capable of performing useful work in realistic environments. With this in mind, Stéphane Magnenat walks us through all the steps needed to perform autonomous construction using the marXbot in the video below. To make the task challenging, the building blocks from which robots build towers are distributed throughout the environment, which is riddled with ditches that can only be overcome by using these same building blocks as bridges. Because there are few building blocks, the robot has to figure out how to move the blocks in an near-to-optimal way so that it can navigate the environment while still building the tower. Furthermore, the robot does not have any information about its environment beforehand and can only use limited computational resources, as is often the case in realistic robot scenarios.

Solving this challenge requires an integrated system architecture (see figure below) that leverages modern algorithms and representations. The architecture is implemented using ASEBA, which is an open-source control architecture for microcontrollers. The low-level implements reactive behaviors such as avoiding obstacles and ditches or grasping objects. The high-level instead takes care of mapping the environment (using a version of FastSLAM), path-planning and reasoning.

The authors hope that such an integrated approach could help shed light on the capabilities required for intelligent physical interaction with the real world.

Have you ever seen those videos of quadrocopters performing acrobatic maneuvers?

The latest paper on the Autonomous Robots website presents a simple method to make your robot achieve adaptive fast open-loop maneuvers, whether it’s performing multiple flips or fast translation motions. The method is thought to be straightforward to implement and understand, and general enough that it could be applied to problems outside of aerial acrobatics.

Before the experiment, an engineer with knowledge of the problem defines a maneuver as an initial state, a desired final state, and a parameterized control function responsible for producing the maneuver. A model of the robot motion is used to initialize the parameters of this control function. Because models are never perfect, the parameters then need to be refined during experiments. The error between the robot’s desired state and its achieved state after each maneuver is used to iteratively correct parameter values. More details can be found in the figure below or in the paper.

Method to achieve adaptive fast open-loop maneuver. p represents the parameters to be adapted, C is a first-order correction matrix, γ is a correction step size, and e is a vector of error measurements. (1) The user defines a motion in terms of initial and desired final states and a parameterized input function. (2) A first-principles continuous-time model is used to find nominal parameters p0 and C. (3) The motion is performed on the physical vehicle, (4) the error is measured and (5) a correction is applied to the parameters. The process is then repeated.

Experiments were performed in the ETH Flying Machine Arena which is equipped with an 8-camera motion capture system providing robot position and rotation measurements used for parametric learning.