Cesar A. Munoz

A TCAS-II Resolution Advisory Detection Algorithm

The Traffic Alert and Collision Avoidance System (TCAS) is a family of airborne systems designed to reduce the risk of mid-air collisions between aircraft. TCAS II, the current generation of TCAS devices, provides resolution advisories that direct pilots to maintain or increase vertical separation when aircraft distance and time parameters are beyond designed system thresholds. This paper presents a mathematical model of the TCAS II Resolution Advisory (RA) logic that assumes accurate aircraft state information. Based on this model, an algorithm for RA detection is also presented. This algorithm is analogous to a conflict detection algorithm, but instead of predicting loss of separation, it predicts resolution advisories. It has been formally verified that for a kinematic model of aircraft trajectories, this algorithm completely and correctly characterizes all encounter geometries between two aircraft that lead to a resolution advisory within a given lookahead time interval. The RA detection algorithm proposed in this paper is a fundamental component of a National Aeronautics and Space Administration (NASA) sense and avoid concept for the integration of Unmanned Aircraft Systems in civil airspace.

Fuzzy motivations for evolutionary behavior learning by a mobile robot

In this paper we describe a fuzzy logic based approach for providing biologically based motivations to be used in evolutionary mobile robot learning. Takagi-Sugeno-Kang (TSK) fuzzy logic is used to motivate a small mobile robot to acquire complex behaviors and to perform environment recognition. This method is implemented and tested in behavior based navigation and action sequence based environment recognition tasks in a Khepera mobile robot simulator. Our fuzzy logic based motivation technique is shown as a simple and powerful method for a robot to acquire a diverse set of fit behaviors as well as providing an intuitive user interface framework.

Unmanned aircraft systems in the national airspace system: a formal methods perspective

As the technological and operational capabilities of unmanned aircraft systems (UAS) have grown, so too have international efforts to integrate UAS into civil airspace. However, one of the major concerns that must be addressed in realizing this integration is that of safety. For example, UAS lack an on-board pilot to comply with the legal requirement that pilots see and avoid other aircraft. This requirement has motivated the development of a detect and avoid (DAA) capability for UAS that provides situational awareness and maneuver guidance to UAS operators to aid them in avoiding and remaining well clear of other aircraft in the airspace. The NASA Langley Research Center Formal Methods group has played a fundamental role in the development of this capability. This article gives a selected survey of the formal methods work conducted in support of the development of a DAA concept for UAS. This work includes specification of low-level and high-level functional requirements, formal verification of algorithms, and rigorous validation of software implementations.

A Formally Verified Conflict Detection Algorithm for Polynomial Trajectories

In air traffic management, conflict detection algorithms are used to determine whether or not aircraft are predicted to lose horizontal and vertical separation minima within a time interval assuming a trajectory model. In the case of linear trajectories, conflict detection algorithms have been proposed that are both complete, i.e., they detect all conflicts, and sound, i.e., they do not present false alarms. In general, for arbitrary nonlinear trajectory models, it is possible to define detection algorithms that are either sound or complete, but not both. This paper considers the case of nonlinear aircraft trajectory models based on polynomial functions. In particular, it proposes a conflict detection algorithm that precisely determines whether, given a lookahead time, two aircraft flying polynomial trajectories are in conflict. That is, it has been formally verified that, assuming that the aircraft trajectories are modeled as polynomial functions, the proposed algorithm is both sound and complete.

A Mathematical Analysis of Conflict Prevention Information

In air traffic management, conflict prevention information refers to the guidance maneuvers, which if taken, ensure that an aircrafts path is conflict-free. These guidance maneuvers take the form of changes to track angle or ground speed. Conflict prevention information may be assembled into prevention bands that advise the crew on maneuvers that should not be taken. Unlike conflict resolution systems, which presume that the aircraft already has a conflict, conflict prevention systems show conflicts for any maneuver, giving the pilot confidence that if a maneuver is made, then no near-term conflicts will result. Because near-term conflicts can lead to safety concerns, strong verification of information correctness is required. This paper presents a mathematical framework to analyze the correctness of algorithms that produce conflict prevention information incorporating an arbitrary number of traffic aircraft and with both a near-term and intermediate-term lookahead times. The framework is illustrated with a formally verified algorithm for 2-dimensional track angle prevention bands.

Acquiring Adaptive Behaviors of Mobile Robots Using Genetic Algorithms and Artificial Neural Networks

This paper describes the use of soft computing techniques for acquiring adaptive behaviors to be used in mobile robot exploration. Action-based environment modeling (AEM) based navigation is used within unknown environments and unsupervised adaptive learning is used for obtaining of the dynamic behaviors. In this investigation it is shown that this unsupervised adaptive method is capable of training a simple low cost robot towards developing highly fit behaviors within a diverse set of complex environments. The experiments that endorse these affirmations were made in Khepera robot simulator. The robot makes use of a neural network to interpret the measurements from the robot sensors in order to determine its next behavior. The training of this network was made using a genetic algorithm (GA), where each individual robot is constituted by a neural network. Fitness evaluation provides the quality of robot behavior with respect to his exploration capability within his environment

DANTi: Detect and Avoid iN The Cockpit

Mid-air collision risk continues to be a concern for manned aircraft operations, especially near busy non-towered airports. The use of Detect and Avoid (DAA) technologies and draft standards developed for unmanned aircraft systems (UAS), either alone or in combination with other collision avoidance technologies, may be useful in mitigating this collision risk for manned aircraft. This paper describes a NASA research effort known as DANTi (DAA iN The Cockpit), including the initial development of the concept of use, a software prototype, and results from initial flight tests conducted with this prototype. The prototype used a single Automatic Dependent Surveillance – Broadcast (ADS-B) traffic sensor and the own aircraft’s position, track, heading and air data information, along with NASA-developed DAA software to display traffic alerts and maneuver guidance to manned aircraft pilots on a portable tablet device. Initial flight tests with the prototype showed a successful DANTi proof-of-concept, but also demonstrated that the traffic separation parameter set specified in the RTCA SC-228 Phase I DAA MOPS may generate excessive false alerts during traffic pattern operations. Several parameter sets with smaller separation values were also tested in flight, one of which yielded more timely alerts for the maneuvers tested. Results from this study may further inform future DANTi efforts as well as Phase II DAA MOPS development.

An Independent and Coordinated Criterion for Kinematic Aircraft Maneuvers

This paper proposes a mathematical definition of an aircraft-separation criterion for kinematic-based horizontal maneuvers. It has been formally proved that kinematic maneuvers that satisfy the new criterion are independent and coordinated for repulsiveness, i.e., the distance at closest point of approach increases whether one or both aircraft maneuver according to the criterion. The proposed criterion is currently used in NASA’s Airborne Coordinated Resolution and Detection (ACCoRD) set of tools for the design and analysis of separation assurance systems.

Formal Verification and Automated Testing for Diagnostic and Monitoring Systems

are combined. Formal methods achieve this by using symbolic methods of analysis, and it is the computational complexity of these symbolic methods that limits and complicates their application. Recently, new methods and tools for symbolic analysis have been developed that are far more ecient and eective in practice than earlier methods. These include solvers for “satisfiability modulo theories” (SMT solvers) and methods for using these to analyze discrete and hybrid (i.e., mixed discrete and continuous) systems. In addition, new ways to apply these capabilities have been developed, such as their use for test generation, and controller synthesis, in addition to analysis. In this paper, we outline these new methods, and describe a project to extend and apply them in combination to issues in verification and testing of diagnostic and monitoring systems.

Learning performance in evolutionary behavior based mobile robot navigation

In this paper we utilize information theory to study the impact in learning performance of various motivation and environmental configurations. This study is done within the context of an evolutionary fuzzy motivation based approach used for acquiring behaviors in mobile robot exploration of complex environments. Our robot makes use of a neural network to evaluate measurements from its sensors in order to establish its next behavior. Adaptive learning, fuzzy based fitness and Action-based Environment Modeling (AEM) are integrated and applied toward training the robot. Using information theory we determine the conditions that lead the robot toward highly fit behaviors. The research performed also shows that information theory is a useful tool in analyzing robotic training methods.