Ravi Vaidyanathan

Estimation of IMU and MARG orientation using a gradient descent algorithm

This paper presents a novel orientation algorithm designed to support a computationally efficient, wearable inertial human motion tracking system for rehabilitation applications. It is applicable to inertial measurement units (IMUs) consisting of tri-axis gyroscopes and accelerometers, and magnetic angular rate and gravity (MARG) sensor arrays that also include tri-axis magnetometers. The MARG implementation incorporates magnetic distortion compensation. The algorithm uses a quaternion representation, allowing accelerometer and magnetometer data to be used in an analytically derived and optimised gradient descent algorithm to compute the direction of the gyroscope measurement error as a quaternion derivative. Performance has been evaluated empirically using a commercially available orientation sensor and reference measurements of orientation obtained using an optical measurement system. Performance was also benchmarked against the propriety Kalman-based algorithm of orientation sensor. Results indicate the algorithm achieves levels of accuracy matching that of the Kalman based algorithm; < 0.8° static RMS error, < 1.7° dynamic RMS error. The implications of the low computational load and ability to operate at small sampling rates significantly reduces the hardware and power necessary for wearable inertial movement tracking, enabling the creation of lightweight, inexpensive systems capable of functioning for extended periods of time.

Multi-modal locomotion: from animal to application

The majority of robotic vehicles that can be found today are bound to operations within a single media (i.e. land, air or water). This is very rarely the case when considering locomotive capabilities in natural systems. Utility for small robots often reflects the exact same problem domain as small animals, hence providing numerous avenues for biological inspiration. This paper begins to investigate the various modes of locomotion adopted by different genus groups in multiple media as an initial attempt to determine the compromise in ability adopted by the animals when achieving multi-modal locomotion. A review of current biologically inspired multi-modal robots is also presented. The primary aim of this research is to lay the foundation for a generation of vehicles capable of multi-modal locomotion, allowing ambulatory abilities in more than one media, surpassing current capabilities. By identifying and understanding when natural systems use specific locomotion mechanisms, when they opt for disparate mechanisms for each mode of locomotion rather than using a synergized singular mechanism, and how this affects their capability in each medium, similar combinations can be used as inspiration for future multi-modal biologically inspired robotic platforms.

Design of an autonomous amphibious robot for surf zone operation: part i mechanical design for multi-mode mobility

The capability of autonomous and semi-autonomous platforms to function in the shallow water surf zone is critical for a wide range of military and civilian operations. Of particular importance is the ability to transition between locomotion modes in aquatic and terrestrial settings. The study of animal locomotion mechanisms can provide specific inspiration to address these demands. In this work, we summarize on-going efforts to create an autonomous, highly mobile amphibious robot. A water-resistant amphibious prototype design, based on the biologically-inspired Whegstrade platform, has been completed. Through extensive field-testing, mechanisms have been isolated to improve the implementation of the Whegstrade concept and make it more suited for amphibious operation. Specific design improvements include wheel-leg propellers enabling swimming locomotion, an active, compliant, water resistant, non-backdrivable body joint, and improved feet for advanced mobility. These design innovations allow Whegstrade to navigate on rough terrain and underwater, and accomplish tasks with little or no low-level control, thus greatly simplifying autonomous control system implementation. Complementary work is underway for autonomous control. We believe these results can lay the foundation for the development of a generation of amphibious robots with an unprecedented versatility and mobility

Multi-agent control algorithms for chemical cloud detection and mapping using unmanned air vehicles

Traditional control approaches fall well short of the necessary flexibility and efficiency needed to meet the commercial and military demands placed upon UAV swarms. Effective coordination of these swarms requires development of control strategies based on emergent behavior. We have developed a rule-based, decentralized control algorithm that relies on constrained randomized behavior and respects UAV restrictions on sensors, computation, and flight envelope. To demonstrate and evaluate the effectiveness of our approach, we have created a simulation of an air vehicle swarm searching for and mapping a chemical cloud within a patrolled region. We then consider several different detection and mapping strategies based on emergent behavior. We then establish an inverse linear relation between the size of the swarm and the time to detect the cloud, regardless of the size of the cloud. Further, we also show the size of the swarm has a linear relation with the successful detection of the cloud.

Decentralized cooperative auction for multiple agent task allocation using synchronized random number generators

A collection of agents, faced with multiple tasks to perform, must effectively map agents to tasks in order to perform the tasks quickly with limited wasted resources. We propose a decentralized control algorithm based on synchronized random number generators to enact a cooperative task auction among the agents. The algorithm finds probabilistically reasonable solutions in few rounds of bidding. Additionally, as the length of the auction increases, the expectation of a better solution increases. This algorithm is not intended to find the optimal solution; it finds a good solution with less computation and communication.

Tongue-Movement Communication and Control Concept for Hands-Free Human–Machine Interfaces

A new communication and control concept using tongue movements is introduced to generate, detect, and classify signals that can be used in novel hands-free human-machine interface applications such as communicating with a computer and controlling devices. The signals that are caused by tongue movements are the changes in the airflow pressure that occur in the ear canal. The goal is to demonstrate that the ear pressure signals that are acquired using a microphone that is inserted into the ear canal, due to specific tongue movements, are distinct and that the signals can be detected and classified very accurately. The strategy that is developed for demonstrating the concept includes energy-based signal detection and segmentation to extract ear pressure signals due to tongue movements, signal normalization to decrease the trial-to-trial variations in the signals, and pairwise cross-correlation signal averaging to obtain accurate estimates from ensembles of pressure signals. A new decision fusion classification algorithm is formulated to assign the pressure signals to their respective tongue-movement classes. The complete strategy of signal detection and segmentation, estimation, and classification is tested on four tongue movements of eight subjects. Through extensive experiments, it is demonstrated that the ear pressure signals due to the tongue movements are distinct and that the four pressure signals can be classified with an accuracy of more than 97% averaged across the eight subjects using the decision fusion classification algorithm. Thus, it is concluded that, through the unique concept that is introduced in this paper, human-computer interfaces that use tongue movements can be designed for hands-free communication and control applications.

A sensor platform capable of aerial and terrestrial locomotion

A sensor platform has been developed that is capable of both aerial and terrestrial locomotion, as well as transitioning between the two. The morphing micro air-land vehicle (MMALV) implements biological inspiration in both flying and walking. MMALV integrates the University of Floridas micro air vehicle (MAV) technology with the terrain mobility of Mini-Whegs/spl trade/. Fabricated of lightweight carbon fiber, the UF-MAV employs a flexible wing design to achieve improved stability over other MAVs of similar size. Mini-Whegs/spl trade/ employs the patented (pending) wheel leg running gear that makes the Whegs/spl trade/ and Mini-Whegs/spl trade/ line of robots fast, agile, and efficient. MMALV has a 30.5cm wingspan, and is 25.4cm long. Terrestrial locomotion is achieved using two independently controlled wheel legs, which are differentially actuated to perform turning. The vehicle successfully performs the transition from flight to walking. Furthermore, MMALV is capable of transitioning from terrestrial to aerial locomotion by walking off a structure of only 20 feet. A wing retraction mechanism improves the portability of the vehicle, as well as its terrestrial stealth and ability to enter small openings.

Design of an autonomous amphibious robot for surf zone operations: part II - hardware, control implementation and simulation

This paper describes a work at The Naval Postgraduate School (NPS) and Case Western Reserve University (CWRU) to create an autonomous highly mobile amphibious robot. A first generation land-based prototype has been constructed and field tested. This robot design, based on a tracked element, is capable of autonomous waypoint navigation, self-orientation, obstacle avoidance, and has the capacity to transmit sensor (visual) feedback. A water-resistant second generation amphibious prototype design, based around the biologically inspired Whegstrade platform, has been completed. This design marries the unprecedented mobility of Whegstrade with the autonomous hardware and control architectures implemented in the first generation prototype. Furthermore, we have also implemented a dynamic simulation capturing salient features of Whegstrade for testing of robotic locomotion capabilities. The integration of these elements can lay the foundation for the development of a new generation of highly mobile autonomous amphibious robots

Evolutionary path planning for autonomous air vehicles using multi-resolution path representation

We introduce an evolutionary flight path planning algorithm capable of mapping paths for free-flying vehicles functioning under several aerodynamic constraints. An air-to-ground targeting scenario was selected to demonstrate the algorithm. The task of the path planner was to generate inputs flying a munition to a point where it could fire a projectile to eliminate a ground target. Vehicle flight constraints, path destination, and final orientation were optimized through fitness evaluation and iterative improvement of generations of candidate flight paths. Evolutionary operators comprised of one crossover operation and six mutation operators. Several cases for air-to-ground vehicle targeting have been successfully executed by the evolutionary flight path planning algorithm under challenging initial conditions. The results demonstrate that evolutionary optimization can achieve flight objectives for air vehicles without violating limits of the aircraft.

Design of a semi-autonomous hybrid mobility surf-zone robot

Surf-zone environments pose extreme challenges to robot operation. A robot that could autonomously navigate through the rocky terrain, constantly changing underwater currents, hard-packed moist sand, and loose dry sand characterizing this environment, would have very significant utility for a range of defence and civilian missions. The study of animal locomotion mechanisms can elucidate specific movement principles that can be applied to address these demands. In this work, we report on the design and optimization of a biologically inspired autonomous robot for deployment and operation in an ocean beach environment. Based on recent success with beach environment autonomy and a new rugged waterproof robotic platform, we propose a new design that will fuse a range of insect-inspired passive mechanisms with active autonomous control architectures to seamlessly adapt to and traverse through a range of challenging substrates both in and out of the water.