Somaiyeh MahmoudZadeh

S. MahmoudZadeh, A. Yazdani, A. Elmi et al.

This study proposes an efficient data collection strategy exploiting a team of Unmanned Aerial Vehicles (UAVs) to monitor and collect the data of a large distributed sensor network usually used for environmental monitoring, meteorology, agriculture, and renewable energy applications. The study develops a collaborative mission planning system that enables a team of UAVs to conduct and complete the mission of sensors' data collection collaboratively while considering existing constraints of the UAV payload and battery capacity. The proposed mission planner system employs the Differential Evolution (DE) optimization algorithm enabling UAVs to maximize the number of visited sensor nodes given the priority of the sensors and avoiding the redundant collection of sensors' data. The proposed mission planner is evaluated through extensive simulation and comparative analysis. The simulation results confirm the effectiveness and fidelity of the proposed mission planner to be used for the distributed sensor network monitoring and data collection.

Amin Abbasi, Somaiyeh MahmoudZadeh, Amirmehdi Yazdani et al.

A two-wheeled mobile robot, namely Kian-I, is designed and prototyped in this research. The Kian-I is comparable with Khepera-IV in terms of dimensional specifications, mounted sensors, and performance capabilities and can be used for educational purposes and cost-effective experimental tests. A motion control architecture is designed for Kian-I in this study to facilitate accurate navigation for the robot in an immersive environment. The implemented control structure consists of two main components of the path recommender system and trajectory tracking controller. Given partial knowledge about the operation field, the path recommender system adopts B-spline curves and Particle Swarm Optimization (PSO) algorithm to determine a collision-free path curve with translational velocity constraint. The provided optimal reference path feeds into the trajectory tracking controller enabling Kian-I to navigate autonomously in the operating field. The trajectory tracking module eliminate the error between the desired path and the followed trajectory through controlling the wheels' velocity. To assess the feasibility of the proposed control architecture, the performance of Kian-I robot in autonomous navigation from any arbitrary initial pose to a target of interest is evaluated through numerous simulation and experimental studies. The experimental results demonstrate the functional capacities and performance of the prototyped robot to be used as a benchmark for investigation and verification of various mobile robot algorithms in the laboratory environment.

Somaiyeh MahmoudZadeh, David MW Powers, Reza Bairam Zadeh

Advances in hardware technology have facilitated more integration of sophisticated software toward augmenting the development of Unmanned Vehicles (UVs) and mitigating constraints for onboard intelligence. As a result, UVs can operate in complex missions where continuous trans-formation in environmental condition calls for a higher level of situational responsiveness and autonomous decision making. This book is a research monograph that aims to provide a comprehensive survey of UVs autonomy and its related properties in internal and external situation awareness to-ward robust mission planning in severe conditions. An advance level of intelligence is essential to minimize the reliance on the human supervisor, which is a main concept of autonomy. A self-controlled system needs a robust mission management strategy to push the boundaries towards autonomous structures, and the UV should be aware of its internal state and capabilities to assess whether current mission goal is achievable or find an alternative solution. In this book, the AUVs will become the major case study thread but other cases/types of vehicle will also be considered. In-deed the research monograph, the review chapters and the new approaches we have developed would be appropriate for use as a reference in upper years or postgraduate degrees for its coverage of literature and algorithms relating to Robot/Vehicle planning, tasking, routing, and trust.

S. MahmoudZadeh, D. M. W Powers, A. Atyabi

This paper describes a reflexive multilayered mission planner with a mounted energy efficient local path planner for Unmanned Underwater Vehicle's (UUV) navigation throughout the complex subsea volume in a time-variant semi-dynamic operation network. The UUV routing protocol in Underwater Wireless Sensor Network (UNSW) is generalized with a homogeneous Dynamic Knapsack-Traveler Salesman Problem emerging with an adaptive path planning mechanism to address UUV's long-duration missions on dynamically changing subsea volume. The framework includes a base layer of global path planning, an inner layer of local path planning and an environmental sub-layer. Such a multilayer integrated structure facilitates the framework to adopt any algorithm with real-time performance. The evolutionary technique known as Differential Evolution algorithm is employed by both base and inner layers to examine the performance of the framework in efficient mission timing and its resilience against the environmental disturbances. Relying on reactive nature of the framework and fast computational performance of the DE algorithm, the simulations show promising results and this new framework guarantees a safe and efficient deployment in a turbulent uncertain marine environment passing through a proper sequence of stations considering various constraint in a complex environment.

Weixiang Li, Wei Jie, Somaiyeh MahmoudZadeh

In this paper a hybrid image defogging approach based on region segmentation is proposed to address the dark channel priori algorithm's shortcomings in de-fogging the sky regions. The preliminary stage of the proposed approach focuses on the segmentation of sky and non-sky regions in a foggy image taking the advantageous of Meanshift and edge detection with embedded confidence. In the second stage, an improved dark channel priori algorithm is employed to defog the non-sky region. Ultimately, the sky area is processed by DehazeNet algorithm, which relies on deep learning Convolutional Neural Networks. The simulation results show that the proposed hybrid approach in this research addresses the problem of color distortion associated with sky regions in foggy images. The approach greatly improves the image quality indices including entropy information, visibility ratio of the edges, average gradient, and the saturation percentage with a very fast computation time, which is a good indication of the excellent performance of this model.

Somaiyeh MahmoudZadeh

This study introduces a connective model of routing -- local path planning for Autonomous Underwater Vehicle (AUV) time efficient maneuver in long-range operations. Assuming the vehicle operating in a turbulent underwater environment, the local path planner produces the water-current resilient shortest paths along the existent nodes in the global route. A re-routing procedure is defined to re-organize the order of nodes in a route and compensate any lost time during the mission. The Firefly Optimization Algorithm (FOA) is conducted by both of the planners to validate the model's performance in mission timing and its robustness against water current variations. Considering the limitation over the battery life time, the model offers an accurate mission timing and real-time performance. The routing system and the local path planner operate cooperatively, and this is another reason for model's real-time performance. The simulation results confirms the model's capability in fulfillment of the expected criterion and proves its significant robustness against underwater uncertainties and variations of the mission conditions.

Somaiyeh MahmoudZadeh

Providing a higher level of decision autonomy and accompanying prompt changes of an uncertain environment is a true challenge of AUVs autonomous operations. The proceeding approach introduces a robust reactive structure that accommodates an AUV's mission planning, task-time management in a top level and incorporates environmental changes by a synchronic motion planning in a lower level. The proposed architecture is developed in a hierarchal modular format and a bunch of evolutionary algorithms are employed by each module to investigate the efficiency and robustness of the structure in different mission scenarios while water current data, uncertain static-mobile/motile obstacles, and vehicles Kino-dynamic constraints are taken into account. The motion planner is facilitated with online re-planning capability to refine the vehicle's trajectory based on local variations of the environment. A small computational load is devoted for re-planning procedure since the upper layer mission planner renders an efficient overview of the operation area that AUV should fly thru. Numerical simulations are carried out to investigate robustness and performance of the architecture in different situations of a real-world underwater environment. Analysis of the simulation results claims the remarkable capability of the proposed model in accurate mission task-time-threat management while guarantying a secure deployment during the mission.