Unmanned aerial vehicles (UAVs) have recently attracted the attention of researchers due to their numerous potential civilian applications. However, current robot navigation technologies need further development for efficient application to various scenarios. One key issue is the “Sense and Avoid” capability, currently of immense interest to researchers. Such a capability is required for safe operation of UAVs in civilian domain. For autonomous decision making and control of UAVs, several path-planning and navigation algorithms have been proposed. This is a challenging task to be carried out in a 3D environment, especially while accounting for sensor noise, uncertainties in operating conditions, and real-time applicability. Heuristic and non-heuristic or exact techniques are the two solution methodologies that categorize path-planning algorithms. The aim of this paper is to carry out a comprehensive and comparative study of existing UAV path-planning algorithms for both methods. Three different obstacle scenarios test the performance of each algorithm. We have compared the computational time and solution optimality, and tested each algorithm with variations in the availability of global and local obstacle information.

This paper proposes the implementation of fastdynamic Mixed Integer Linear Programming (MILP) and Path Smoother for efficient path planning of Unmanned Aerial Vehicles (UAVs) in various flight formations. The UAVs taking part in a cooperative flight are assumed to be equipped with Automatic Dependent Surveillance Broadcast (ADS-B) which enables sharing the flight information with neighboring aircraft. The design and implementation of flights for various formations have been carried out in a generic manner such that multiple UAVs with arbitrarily geographically located base stations can take part in collision-free formation flight. The paper formulates the problem of path of planning in the framework of a novel fast-dynamic MILP and proposes a cost function that minimizes time and energy consumption. The paper presents elaborate construction of constraint equations to enforce the formation to visit pre-defined way-points and avoid the collisions with any intruder aircraft. The performance of the proposed algorithm has been verified and compared with respect to the standard MILP method via a number of simulations carried out using different scenarios featuring multiple UAVs flying in various formations.

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Objectives: Psychogenic non-epileptic seizures (PNES) have been hypothesized to emerge in the context of neural networks instability. To explore this hypothesis in children, we applied a graph theory approach to examine connectivity in neural networks in the resting-state EEG in 35 children with PNES, 31 children with other functional neurological symptoms (but no PNES), and 75 healthy controls. Methods: The networks were extracted from Laplacian-transformed time series by a coherence connectivity estimation method. Results: Children with PNES (vs. controls) showed widespread changes in network metrics: increased global efficiency (gamma and beta bands), increased local efficiency (gamma band), and increased modularity (gamma and alpha bands). Compared to controls, they also had higher levels of autonomic arousal (e.g., lower heart variability); more anxiety, depression, and stress on the Depression Anxiety and Stress Scales; and more adverse childhood experiences on the Early Life Stress Questionnaire. Increases in network metrics correlated with arousal. Children with other functional neurological symptoms (but no PNES) showed scattered and less pronounced changes in network metrics. Conclusion: The results indicate that children with PNES present with increased activation of neural networks coupled with increased physiological arousal. While this shift in functional organization may confer a short-term adaptive advantage-one that facilitates neural communication and the child's capacity to respond self-protectively in the face of stressful life events-it may also have a significant biological cost. It may predispose the child's neural networks to periods of instability-presenting clinically as PNES-when the neural networks are faced with perturbations in energy flow or with additional demands.

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