Multiple Source Seeking using Glowworm Swarm Optimization and Distributed Gradient Estimation

Turgeman, Avi; Werner, Herbert

doi:10.23919/acc.2018.8430843

Cited by 11 publications

(6 citation statements)

References 22 publications

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“…Previous research with simulations, assuming negligible agent dynamics and external disturbances, successfully demonstrated this task. Some used gradient-based control strategies for agents in leader-follower roles [13] [14], fixed formations [15] [71] or bioinspired swarms [17] [18] [19]. Another team [16] simulated this task by proposing a loosely controlled formation using a probabilistic gradientestimation control strategy, while [11] dithered vehicle motion with amplitudes proportional to the square of the source location estimation error.…”

Section: Ia Adaptive Navigation In 2d Scalar Fieldsmentioning

confidence: 99%

Multiple UAV Adaptive Navigation for Three-Dimensional Scalar Fields

Lee¹,

Kitts

Neumann

et al. 2021

IEEE Access

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Adaptive Navigation (AN) control strategies allow an agent to autonomously alter its trajectory based on realtime measurements of the environment. Compared to conventional navigation methods, these techniques can reduce required time and energy to explore scalar characteristics of unknown and dynamic regions of interest (e.g., temperature, concentration level). Multiple Uncrewed Aerial Vehicle (UAV) approaches to AN can improve performance by exploiting synchronized spatially-dispersed measurements to generate realtime information regarding the structure of the local scalar field, which is then used to inform navigation decisions. This article presents initial results of a comprehensive program to develop, verify, and experimentally implement mission-level AN capabilities in three-dimensional (3D) space using our unique multilayer control architecture for groups of vehicles. Using our flexible formation control system, we build upon our prior 2D AN work and provide new contributions to 3D scalar field AN by a) demonstrating a wide range of 3D AN capabilities using a unified, multilayer control architecture, b) extending multivehicle 2D AN control primitives to navigation in 3D scalar fields, and c) introducing statebased sequencing of these primitive AN functions to execute 3D mission-level capabilities such as isosurface mapping and plume following. We verify functionality using high-fidelity simulations of multicopter drone clusters, accounting for vehicle dynamics, outdoor wind gust disturbances, position sensor inaccuracy, and scalar field sensor noise. This paper presents the multilayer architecture for multivehicle formation control, the 3D AN control primitives, the sequencing approaches for specific mission-level capabilities, and simulation results that demonstrate these functions.

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Section: Ia Adaptive Navigation In 2d Scalar Fieldsmentioning

confidence: 99%

Multiple UAV Adaptive Navigation for Three-Dimensional Scalar Fields

Lee¹,

Kitts

Neumann

et al. 2021

IEEE Access

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“…In this part, experiments with different population sizes and different initial position distribution of robots are implemented in the environment, shown in Figure 1a. Eight tests are carried out with 12,15,18,20,25,30,40, 50 robots, in turn, and the working time of robots is 200 s. Each test is implemented 400 times, and the initial position of robots is updated every time. The performance of SRPB is compared with PSO, RPSO, A-RPSO, GSO, FA, and LFS.…”

Section: Different Population Sizesmentioning

confidence: 99%

“…The method in this paper could be implemented in this situation. Each robot updates its velocity and position by Equations (29), (30), and stores a set of virtual match points. There are some assumptions.…”

Section: Practical Application Analysismentioning

confidence: 99%

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A Distributed Strategy for Cooperative Autonomous Robots Using Pedestrian Behavior for Multi-Target Search in the Unknown Environment

Shi

2020

Sensors

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Searching multiple targets with swarm robots is a realistic and significant problem. The goal is to search the targets in the minimum time while avoiding collisions with other robots. In this paper, inspired by pedestrian behavior, swarm robotic pedestrian behavior (SRPB) was proposed. It considered many realistic constraints in the multi-target search problem, including limited communication range, limited working time, unknown sources, unknown extrema, the arbitrary initial location of robots, non-oriented search, and no central coordination. The performance of different cooperative strategies was evaluated in terms of average time to find the first, the half, and the last source, the number of located sources and the collision rate. Several experiments with different target signals, fixed initial location, arbitrary initial location, different population sizes, and the different number of targets were implemented. It was demonstrated by numerous experiments that SRPB had excellent stability, quick source seeking, a high number of located sources, and a low collision rate in various search strategies.

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“…7). For this purpose, a multiple extrema seeking optimization approach ( [15], [16]) could be in favor. In a second optimization step, the energy-optimal solution is selected among the solution candidates in this reduced set.…”

Section: Conclusion and Outlook On Future Researchmentioning

confidence: 99%

Energy Optimal Trajectory Planning for Electrically Driven Railway Vehicles with Particle Swarm optimization

Prohl

Aschemann

2019

2019 IEEE 15th International Conference on Control and Automation (ICCA)

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This paper deals with the optimization of velocity trajectories for electrically driven railway vehicles w.r.t. the total energy consumption between two successive stops. First, a quasi-static model of the complete power train of a standard railway vehicle is described. Based on four principle operating regimes-acceleration, cruising, coasting, and braking-energyoptimal trajectories are computed by determining the optimal regime sequence. For the optimization of the switching points between these operating states, particle swarm optimization (PSO) is used. The benefits of this approach are pointed out by simulation results using validated train models.

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Multiple Source Seeking using Glowworm Swarm Optimization and Distributed Gradient Estimation

Cited by 11 publications

References 22 publications

Multiple UAV Adaptive Navigation for Three-Dimensional Scalar Fields

Multiple UAV Adaptive Navigation for Three-Dimensional Scalar Fields

A Distributed Strategy for Cooperative Autonomous Robots Using Pedestrian Behavior for Multi-Target Search in the Unknown Environment

Energy Optimal Trajectory Planning for Electrically Driven Railway Vehicles with Particle Swarm optimization

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