Cooperative path planner for UAVs using ACO algorithm with Gaussian distribution functions

Cheng, Chi‐Tsun; Fallahi, Kia; Leung, Henry; Tse, Chi Kong

doi:10.1109/iscas.2009.5117713

Cited by 16 publications

(17 citation statements)

References 12 publications

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“…Thus biologically-inspired approaches consisting of algorithms based on fundamental aspects of natural intelligence have emerged, such as behavioral autonomy and social interaction, evolution and learning [70]. Considering the CPP problem with aerial vehicles, several authors have explored different approaches in the literature, including real-time search methods [36], random walk [71], cellular systems [72][73][74], evolutionary computation [75,76], and swarm intelligence [77][78][79]. Coverage with uncertainty considering information points is also addressed [80][81][82][83][84][85].…”

Section: Partial Informationmentioning

confidence: 99%

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Survey on Coverage Path Planning with Unmanned Aerial Vehicles

Cabreira

Brisolara

Ferreira

2019

Drones

365

194

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Coverage path planning consists of finding the route which covers every point of a certain area of interest. In recent times, Unmanned Aerial Vehicles (UAVs) have been employed in several application domains involving terrain coverage, such as surveillance, smart farming, photogrammetry, disaster management, civil security, and wildfire tracking, among others. This paper aims to explore and analyze the existing studies in the literature related to the different approaches employed in coverage path planning problems, especially those using UAVs. We address simple geometric flight patterns and more complex grid-based solutions considering full and partial information about the area of interest. The surveyed coverage approaches are classified according to a classical taxonomy, such as no decomposition, exact cellular decomposition, and approximate cellular decomposition. This review also contemplates different shapes of the area of interest, such as rectangular, concave and convex polygons. The performance metrics usually applied to evaluate the success of the coverage missions are also presented.

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Section: Partial Informationmentioning

confidence: 99%

“…Cheng et al [77] propose another bio-inspired approach for cooperative coverage. This approach represents the path of each vehicle as the B-spline curve containing control points, as illustrated in Figure 31a.…”

Section: Ant Colony Optimizationmentioning

confidence: 99%

Survey on Coverage Path Planning with Unmanned Aerial Vehicles

Cabreira

Brisolara

Ferreira

2019

Drones

365

194

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“…In ACO, the computational resources are allocated to a set of relatively simple agents that exploit a form of indirect communication mediated by the environment to construct solutions to the finding the shortest trajectory from ant nest to a considered problem. More details about the type of ACO for global optimal trajectory planning of UAV which is used in this work can be found in our previous work [13].…”

Section: ) Aco Path Optimization Using B-spline Curvesmentioning

confidence: 99%

“…Order of B-spline curve is 4. More details about the the path optimization using ACO is given in our previous work [13]. The weights for path objectives are w P L = 6, w ME = 3, and w MT = 4.…”

Section: Simulationsmentioning

confidence: 99%

An integrated ACO-AHP approach for resource management optimization

Fallahi

Leung

Chandana

2009

2009 IEEE International Conference on Systems, Man and Cybernetics

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The most often used operator to aggregate criteria in decision making problems is the classical weighted sum model or weighted sum model. However, in many problems, the criteria considered interact and a substitute to the weighted sum model has to be adopted. Multi-criteria decision making (MCDM) problems involve the ranking of a finite set of alternatives in terms of a finite number of decision criteria. Usually such criteria may be in conflict with each other. A typical problem in MCDA is concerned with the task of ranking a finite number of decision alternatives, each of which is explicitly described in terms of different characteristics often called decision criteria or objectives. This research applies an integrated multi-criteria decision making approach to design an optimal UAV resource management. In this approach, the ant colony optimization (ACO) is used firstly to obtain optimal solutions satisfying some path planning criteria, then, fuzzy analytic hierarchy process (AHP) is formulated to select the best set of UAVs. Due to vagueness and uncertainty, fuzzy set theory based AHP is employed in the decision making judgments, because it can handle uncertainty easily. The proposed method can be extended to any sensor network resource management problem.

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“…The limitations introduced by these techniques regarding online UAV path planning are long execution time in the complicated large environment, the generated path is not optimal and may or may not satisfy the UAV physical and environmental constraints. Heuristic methods were introduced for solving the problems of path optimality based on Bio-inspired algorithms which originated in swarm intelligence field, it mainly consists of simple agents population which interact locally with their environment and with one another as in genetic algorithms (GA) [14], [15], ant colony optimization (ACO) [36], particle swarm optimization (PSO) [16], artificial bee colony algorithms (ABC) [6], [37]. These algorithms deal with a set of feasible solutions that are evaluated each time step according to fitness function, with time the particles are accelerated to converge to an optimal solution or evolving the recent population to next generations according to their fitness, then applying crossover and mutation until reaching pre-set value of iterations number or finding an optimal solution.…”

Section: Introductionmentioning

confidence: 99%

High-Performance Path Planning of UAV under Complex Environment

Abozied¹,

Qin²

2016

IJMO

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Abstract-This paper presents a high-performance path planning algorithm against resource and physical constraints under complex environment based on a modified RRTs algorithm so as to generate a fast and near optimal 3D UAV collision-free path with good smoothness and optimality. The advantages of the proposed algorithm lie in its low computational complexity and small memory to model complicated spaces with a large number of obstacles thus it can meet the requirements of implementation using low cost embedded processors. A series of simulation experiments and comparative analysis are carried out about four benchmark scenarios with VPB-RRT and DRRT algorithms to verify the predominance of our algorithm. The implementation results with hardware in the loop on a low cost embedded board based on Atmel SAM3X8E ARM Cortex-M3 CPU validated its performance effect. Meanwhile, various results demonstrated its robustness and effectiveness to play an important role in the online UAV mission generation.Index Terms-Path planning, rapidly-exploring random tree (RRT), unmanned aerial vehicle (UAV), Obstacle avoidance

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Cooperative path planner for UAVs using ACO algorithm with Gaussian distribution functions

Cited by 16 publications

References 12 publications

Survey on Coverage Path Planning with Unmanned Aerial Vehicles

Survey on Coverage Path Planning with Unmanned Aerial Vehicles

An integrated ACO-AHP approach for resource management optimization

High-Performance Path Planning of UAV under Complex Environment

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