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Cooperative Path Planning and Cooperative Perception for UAVs Swarm

Cooperative Path Planning and Cooperative Perception for UAVs Swarm PDF Author: M. A. Shah
Publisher:
ISBN:
Category :
Languages : en
Pages :

Book Description
In this research Pythagorean Hodograph based path planning and camera based cooperative perception are investigated separately and then these two entirely separate areas (Path Planning and Perception) are integrated for the application in online pop-up obstacle locating & avoidance and moving target tracking & surveillance in dynamic environments. The path planning is integrated with the cooperative perception to deal with the challenges posed by the dynamic environment. The aim of this integration is to achieve maximum autonomy required to execute a mission autonomously by multiple fixed wings UAVs in a dynamic environment. During the mission execution, the cooperating UAVs start from some initial location in the operating environment and finish at some final location while trying to achieve the mission's objectives in a cooperative way. Naturally planning a feasible (safe and flyable) path for each participating UAV from initial position to a final location becomes a compulsory task of mission planning. For fixed wing UAVs flyable paths mean, paths which have tangential and curvature continuity and which obey the kinematic and dynamic constraint of the UAVs. In this research an algorithm based on Pythagorean hodograph curves is developed and used for planning feasible (safe and flyable) paths. The Pythagorean hodograph (PH) yields paths of exact length having tangential and curvature continuity. These continuous paths are made flyable for the UAVs by imposing the kinematic constraints of the UAVs. These constraints are imposed by the curvature and torsion manipulation of the planned paths. The safety of these paths is ensured by making it free of inter collisions between the vehicles and collisions with the known obstacles. These feasible paths are known as the initial paths or reference trajectories. In this research the operating environment is assumed to be dynamic in which changes are taking place at all times. Each UAV taking part in the mission is equipped with a vision sensor to perceive these changes continuously in a cooperative way. As the mission is assumed to be executed in day light, therefore light intensity video camera is used as a vision sensor. A perception algorithm for locating an object cooperatively in 3D is developed in this research. This algorithm is based on the optimization of errors in target position acquired by the on board camera. The algorithm is used by the cooperative perception system for optimal position estimation of the object in the scene. The target position information between the participating UAVs is exchanged through wireless communication for data fusion purposes. After developing efficient algorithms for path planning and cooperative perception, the two algorithms are integrated to be used in reactive obstacle avoidance and target tracking. During the mission, when the UAVs start their flight on the reference trajectories generated by the path planning algorithm, the perception algorithm comes into action. During the travel on these paths if the perception system of any of the UAVs detects an interrupting obstacle which was not known a priori in the map, then the exact location of this obstacle is determined with the help of the perception algorithm in a cooperative way. Using the location of the interrupting obstacle determined by the perception algorithm the path planning algorithm plans an evasive manoeuvre for the corresponding UAV to avoid it. After avoiding the obstacle the UAV comes back to its reference trajectory as soon as possible. In the operation of surveillance and tracking during the mission, the onboard perception algorithm locates an object of interest dynamically and the Pythagorean hodograph (PH) path planner uses this location to generate the paths for the cooperating UAVs to keep in close proximity of the target. In this case the close proximity of the target means to follow the moving target in such a way that it remains in the fields of views of the UAVs cameras at any time. By this integration of path planning and cooperative perception the continuous surveillance and tracking of the target was made possible even when the individual UAV experiences failure. During this research the mid flight obstacle locating & avoidance, and target surveillance & tracking have been successfully achieved by the integration of the path planning and cooperative perception. The purpose of this integration is to achieve an enhanced autonomy for the cooperating group of UAVs to increase the probability of their survival in mission being executed in dynamic environments.

Cooperative Path Planning and Cooperative Perception for UAVs Swarm

Cooperative Path Planning and Cooperative Perception for UAVs Swarm PDF Author: M. A. Shah
Publisher:
ISBN:
Category :
Languages : en
Pages :

Book Description
In this research Pythagorean Hodograph based path planning and camera based cooperative perception are investigated separately and then these two entirely separate areas (Path Planning and Perception) are integrated for the application in online pop-up obstacle locating & avoidance and moving target tracking & surveillance in dynamic environments. The path planning is integrated with the cooperative perception to deal with the challenges posed by the dynamic environment. The aim of this integration is to achieve maximum autonomy required to execute a mission autonomously by multiple fixed wings UAVs in a dynamic environment. During the mission execution, the cooperating UAVs start from some initial location in the operating environment and finish at some final location while trying to achieve the mission's objectives in a cooperative way. Naturally planning a feasible (safe and flyable) path for each participating UAV from initial position to a final location becomes a compulsory task of mission planning. For fixed wing UAVs flyable paths mean, paths which have tangential and curvature continuity and which obey the kinematic and dynamic constraint of the UAVs. In this research an algorithm based on Pythagorean hodograph curves is developed and used for planning feasible (safe and flyable) paths. The Pythagorean hodograph (PH) yields paths of exact length having tangential and curvature continuity. These continuous paths are made flyable for the UAVs by imposing the kinematic constraints of the UAVs. These constraints are imposed by the curvature and torsion manipulation of the planned paths. The safety of these paths is ensured by making it free of inter collisions between the vehicles and collisions with the known obstacles. These feasible paths are known as the initial paths or reference trajectories. In this research the operating environment is assumed to be dynamic in which changes are taking place at all times. Each UAV taking part in the mission is equipped with a vision sensor to perceive these changes continuously in a cooperative way. As the mission is assumed to be executed in day light, therefore light intensity video camera is used as a vision sensor. A perception algorithm for locating an object cooperatively in 3D is developed in this research. This algorithm is based on the optimization of errors in target position acquired by the on board camera. The algorithm is used by the cooperative perception system for optimal position estimation of the object in the scene. The target position information between the participating UAVs is exchanged through wireless communication for data fusion purposes. After developing efficient algorithms for path planning and cooperative perception, the two algorithms are integrated to be used in reactive obstacle avoidance and target tracking. During the mission, when the UAVs start their flight on the reference trajectories generated by the path planning algorithm, the perception algorithm comes into action. During the travel on these paths if the perception system of any of the UAVs detects an interrupting obstacle which was not known a priori in the map, then the exact location of this obstacle is determined with the help of the perception algorithm in a cooperative way. Using the location of the interrupting obstacle determined by the perception algorithm the path planning algorithm plans an evasive manoeuvre for the corresponding UAV to avoid it. After avoiding the obstacle the UAV comes back to its reference trajectory as soon as possible. In the operation of surveillance and tracking during the mission, the onboard perception algorithm locates an object of interest dynamically and the Pythagorean hodograph (PH) path planner uses this location to generate the paths for the cooperating UAVs to keep in close proximity of the target. In this case the close proximity of the target means to follow the moving target in such a way that it remains in the fields of views of the UAVs cameras at any time. By this integration of path planning and cooperative perception the continuous surveillance and tracking of the target was made possible even when the individual UAV experiences failure. During this research the mid flight obstacle locating & avoidance, and target surveillance & tracking have been successfully achieved by the integration of the path planning and cooperative perception. The purpose of this integration is to achieve an enhanced autonomy for the cooperating group of UAVs to increase the probability of their survival in mission being executed in dynamic environments.

Cooperative Path Planning of Unmanned Aerial Vehicles

Cooperative Path Planning of Unmanned Aerial Vehicles PDF Author: Antonios Tsourdos
Publisher: Progress in Astronautics and A
ISBN: 9781600867798
Category : Technology & Engineering
Languages : en
Pages : 0

Book Description
An invaluable addition to the literature on UAV guidance and cooperative control, Cooperative Path Planning of Unmanned Aerial Vehicles is a dedicated, practical guide to computational path planning for UAVs. One of the key issues facing future development of UAVs is path planning: it is vital that swarm UAVs/ MAVs can cooperate together in a coordinated manner, obeying a pre-planned course but able to react to their environment by communicating and cooperating. An optimized path is necessary in order to ensure a UAV completes its mission efficiently, safely, and successfully. Focussing on the path planning of multiple UAVs for simultaneous arrival on target, Cooperative Path Planning of Unmanned Aerial Vehicles also offers coverage of path planners that are applicable to land, sea, or space-borne vehicles. Cooperative Path Planning of Unmanned Aerial Vehicles is authored by leading researchers from Cranfield University and provides an authoritative resource for researchers, academics and engineers working in the area of cooperative systems, cooperative control and optimization particularly in the aerospace industry.Include chapters on path planning, 3-D path planning, cooperative path planning, path planning in complex environments as well as guidance for accurate path following and sense and avoid algorithms to deal with collision avoidanceApproaches the solution to UAV path planning via two phases: producing paths to meet curvature constraints - the flyable paths, and then tuning the flyable paths to meet the mission demandsDescribes flyable path approaches using composite curves using Dubins and Clothoid principles, and continuous curves using Pythagorean Hodograph principles; and extends these approaches to cater for the complex problem of obstacle avoidance.?

Multi-UAV Path Planning and Guidance for Cooperative Hunting in a Cluttered Environment

Multi-UAV Path Planning and Guidance for Cooperative Hunting in a Cluttered Environment PDF Author: Joshua Savio Furtado
Publisher:
ISBN:
Category :
Languages : en
Pages : 0

Book Description
This thesis presents a path planning and guidance algorithm for cooperative hunting by a group of multi-rotor Unmanned Aerial Vehicles (UAVs). A group of intelligent UAVs called pursuers work together to capture a single evader. The target is known to be within a certain region called the target zone. Particle Swarm Optimization is used to generate near-optimal safe paths for the pursuers to get from their base location to the target zone in the presence of static obstacles. Once the pursuers reach the target zone, Proportional Navigation guidance law is used in conjunction with the Potential Field algorithm and Rendezvous Law to simultaneously encircle the target. Previous work done in the cooperative hunting problem does not address the important aspect of rendezvous. Simulations as well as experimental results are shown to demonstrate the feasibility and effectiveness of this work after testing in different scenarios.

Proceedings of 2023 7th Chinese Conference on Swarm Intelligence and Cooperative Control

Proceedings of 2023 7th Chinese Conference on Swarm Intelligence and Cooperative Control PDF Author: Xiaoduo Li
Publisher: Springer Nature
ISBN: 9819733367
Category :
Languages : en
Pages : 714

Book Description


Proceedings of 2021 5th Chinese Conference on Swarm Intelligence and Cooperative Control

Proceedings of 2021 5th Chinese Conference on Swarm Intelligence and Cooperative Control PDF Author: Zhang Ren
Publisher: Springer Nature
ISBN: 9811939985
Category : Technology & Engineering
Languages : en
Pages : 1902

Book Description
This book includes original, peer-reviewed research papers from the 2021 5th Chinese Conference on Swarm Intelligence and Cooperative Control (CCSICC2021), held in Shenzhen, China on January 19-22, 2022. The topics covered include but are not limited to: reviews and discussions of swarm intelligence, basic theories on swarm intelligence, swarm communication and networking, swarm perception, awareness and location, swarm decision and planning, cooperative control, cooperative guidance, swarm simulation and assessment. The papers showcased here share the latest findings on theories, algorithms and applications in swarm intelligence and cooperative control, making the book a valuable asset for researchers, engineers, and university students alike.

Cooperative search for moving targets with the ability to perceive and evade using multiple UAVs

Cooperative search for moving targets with the ability to perceive and evade using multiple UAVs PDF Author: Ziyi Wang
Publisher: OAE Publishing Inc.
ISBN:
Category : Computers
Languages : en
Pages : 27

Book Description
This paper focuses on the problem of regional cooperative search using multiple unmanned aerial vehicles (UAVs) for targets that have the ability to perceive and evade. When UAVs search for moving targets in a mission area, the targets can perceive the positions and flight direction of UAVs within certain limits and take corresponding evasive actions, which makes the search more challenging than traditional search problems. To address this problem, we first define a detailed motion model for such targets and design various search information maps and their update methods to describe the environmental information based on the prediction of moving targets and the search results of UAVs. We then establish a multi-UAV search path planning optimization model based on the model predictive control, which includes various newly designed objective functions of search benefits and costs. We propose a priority-encoded improved genetic algorithm with a fine-adjustment mechanism to solve this model. The simulation results show that the proposed method can effectively improve the cooperative search efficiency, and more targets can be found at a much faster rate compared to traditional search methods.

UAV Cooperative Decision and Control

UAV Cooperative Decision and Control PDF Author: Tal Shima
Publisher: SIAM
ISBN: 0898718589
Category : Mathematics
Languages : en
Pages : 180

Book Description
Unmanned aerial vehicles (UAVs) are increasingly used in military missions because they have the advantages of not placing human life at risk and of lowering operation costs via decreased vehicle weight. These benefits can be fully realized only if UAVs work cooperatively in groups with an efficient exchange of information. This book provides an authoritative reference on cooperative decision and control of UAVs and the means available to solve problems involving them.

Proceedings of 2023 7th Chinese Conference on Swarm Intelligence and Cooperative Control

Proceedings of 2023 7th Chinese Conference on Swarm Intelligence and Cooperative Control PDF Author: Jianglong Yu
Publisher: Springer Nature
ISBN: 9819733324
Category :
Languages : en
Pages : 700

Book Description


Proceedings of 2023 7th Chinese Conference on Swarm Intelligence and Cooperative Control

Proceedings of 2023 7th Chinese Conference on Swarm Intelligence and Cooperative Control PDF Author: Guo-Ping Jiang
Publisher: Springer Nature
ISBN: 9819733405
Category :
Languages : en
Pages : 674

Book Description


Intelligent Coordination of UAV Swarm Systems

Intelligent Coordination of UAV Swarm Systems PDF Author: Xiwang Dong
Publisher: Mdpi AG
ISBN: 9783036586595
Category : Technology & Engineering
Languages : en
Pages : 0

Book Description
The reprint delves into the fascinating world of unmanned aerial vehicle (UAV) swarm systems and their intelligent coordination. This comprehensive collection of research papers explores advancements in UAV swarm systems, such as intelligent perception and cognition, swarm navigation and localization, autonomous decision and planning, cooperative guidance and control, and swarm intelligence. UAV swarm systems have gained significant attention in recent years due to their potential for revolutionizing various domains, including surveillance, search and rescue, environmental monitoring, and disaster response. Intelligent perception and cognition play a crucial role in enabling UAV swarm systems to perceive and understand their environment. Swarm navigation and localization techniques ensure precise positioning and effective movement coordination within the swarm. Autonomous decision and planning algorithms empower UAV swarm systems to make intelligent choices in real-time. Cooperative guidance and control strategies facilitate seamless collaboration among individual UAVs within the swarm. Swarm intelligence, inspired by the collective behavior of social insects, offers valuable insights into designing robust and scalable UAV swarm systems.