Abstract: Brain machine interface (BMI) has been proposed as a novel technique to control prosthetic devices aimed at restoring motor functions in paralyzed patients. In this paper, we propose a neural network based controller that maps rat’s brain signals and transforms them into robot movement. First, the rat is trained to move the robot by pressing the right and left lever in order to get food. Next, we collect brain signals with four implanted electrodes, two in the motor cortex and two in the somatosensory cortex area. The collected data are used to train and evaluate different artificial neural controllers. Trained neural controllers are employed online to map brain signals and transform them into robot motion. Offline and online classification results of rat’s brain signals show that the Radial Basis Function Neural Networks (RBFNN) outperforms other neural networks. In addition, online robot control results show that even with a limited number of electrodes, the robot motion generated by RBFNN matched the motion generated by the left and right lever position.
Abstract: Sound-based positioning systems are a potential alternative low-cost navigation system. Recently, we developed such an audible sound-based positioning system, based on a spread spectrum approach. It was shown to accurately localize a stationary object. Here, we extend this localization to a moving object by compensating for the Doppler shift associated with the object movement. Numerical simulations and experiments indicate that by compensating for the Doppler shift, the system can accurately determine the position of an object moving along a non-linear path. When the object moved in a circular path with an angular velocity of 0 to 1.3 rad/s, it could be localized to within 25 mm of the actual position. Experiments also showed the proposed system has a high noise tolerance of up to −25 dB signal-to-noise ratio (SNR) without compromising accuracy.
Abstract: This paper investigates the trajectory generation problem for an advanced driver assistance system that could sense the driving state of the vehicle, so that a collision free trajectory can be generated safely. Specifically, the problem of trajectory generation is solved for the safety assessment of the driving state and to manipulate the vehicle in order to avoid any possible collisions. The vehicle senses the environment so as to obtain information about other vehicles and static obstacles ahead. Vehicles may share the perception of the environment via an inter-vehicle communication system. The planning algorithm is based on a visibility graph. A lateral repulsive potential is applied to adaptively maintain a trade-off between the trajectory length and vehicle clearance, which is the greatest problem associated with visibility graphs. As opposed to adaptive roadmap approaches, the algorithm exploits the structured nature of the environment for construction of the roadmap. Furthermore, the mostly organized nature of traffic systems is exploited to obtain orientation invariance, which is another limitation of both visibility graphs and adaptive roadmaps. Simulation results show that the algorithm can successfully solve the problem for a variety of commonly found scenarios.
Abstract: Inspired by the simplicity of how nature solves its problems, this paper presents a novel approach that would enable a swarm of ant robotic agents (robots with limited sensing, communication, computational and memory resources) form a visual representation of distributed hazardous substances within an environment dominated by diffusion processes using a decentralized approach. Such a visual representation could be very useful in enabling a quicker evacuation of a city’s population affected by such hazardous substances. This is especially true if the ratio of emergency workers to the population number is very small.
Abstract: Spherical robotics is an emerging research field due to a ball’s characteristic to be holonomic, have a sealed internal environment, and rebound from collisions easily. As the research moves forward, individual groups have begun to develop unique methods of propulsion, each having distinctive engineering trade-offs: weight is sacrificed for power; speed is forfeited for control accuracy, etc. Early spherical robots operated similar to a hamster ball and had a limited torque and a high-energy loss due to internal friction. Researchers have begun to develop various novel concepts to maneuver and control this family of robot. This article is an overview of the current research directions that various groups have taken, the nomenclature used in this subdiscipline, and the various uses of the fundamental principles of physics for propelling a spherical robot.
Abstract: It is my great pleasure to welcome you to a new open access journal, Robotics, which is dedicated to both the foundations of artificial intelligence, bio-mechanics, mechatronics and control theories, and the real-world applications of robotic perception, cognition and actions. This includes the innovative scientific trends, and discovery resulting from solving new challenges in the field of robotics. Its open access and rapid dissemination are the unique features separating this journal from all existing journals dedicated to robotics. [...]
