Active Magnetic Anomaly Detection Using Multiple Micro Aerial Vehicles

Dames, Philip; Schwager, Mac; Rus, Daniela; Kumar, Vijay

doi:10.1109/lra.2015.2511444

Cited by 36 publications

(23 citation statements)

References 22 publications

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“…The standard method used to solve this task is to utilize a quadtree representation to adaptively refine the environment in areas that are likely to contain targets [167]- [169]. The main distinction between these three works is that [167], [168] assume that each robot sees one and only one cell, which implicitly connects the elevation of the robots to the quadtree resolution (and the sensing quality) while [169] allows the robots to see multiple cells and utilize the theory of random finite sets [170] to estimate the set of targets.…”

Section: A Target Search and Trackingmentioning

confidence: 99%

A Survey on Aerial Swarm Robotics

et al. 2018

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Abstract-The use of aerial swarms to solve real-world problems has been increasing steadily, accompanied by falling prices and improving performance of communication, sensing, and processing hardware. The commoditization of hardware has reduced unit costs, thereby lowering the barriers to entry to the field of aerial swarm robotics. A key enabling technology for swarms is the family of algorithms that allow the individual members of the swarm to communicate and allocate tasks amongst themselves, plan their trajectories, and coordinate their flight in such a way that the overall objectives of the swarm are achieved efficiently. These algorithms, often organized in a hierarchical fashion, endow the swarm with autonomy at every level, and the role of a human operator can be reduced, in principle, to interactions at a higher level without direct intervention. This technology depends on the clever and innovative application of theoretical tools from control and estimation. This paper reviews the state of the art of these theoretical tools, specifically focusing on how they have been developed for, and applied to, aerial swarms. Aerial swarms differ from swarms of ground-based vehicles in two respects: they operate in a three-dimensional (3-D) space, and the dynamics of individual vehicles adds an extra layer of complexity. We review dynamic modeling and conditions for stability and controllability that are essential in order to achieve cooperative flight and distributed sensing. The main sections of the paper focus on major results covering trajectory generation, task allocation, adversarial control, distributed sensing, monitoring, and mapping. Wherever possible, we indicate how the physics and subsystem technologies of aerial robots are brought to bear on these individual areas.

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Section: A Target Search and Trackingmentioning

confidence: 99%

A Survey on Aerial Swarm Robotics

et al. 2018

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“…Step 3: Extract all the dynamic paths generated from source node to destination node using improved KNN based A * path planning algorithm [2].…”

Section: Donementioning

confidence: 99%

“…It is worth to note that the Ukrainian military realized from their mistakes and begun effecting communication security measures gained from military systems like specific operating procedures and encryption into their Do-It-Yourself (DIY) products. This proves the fact that small teams dedicated to hateful intentions can mess up with UAV [2]. It is vital to change the symmetric encryption keys regularly.…”

Section: Introductionmentioning

confidence: 95%

Secured dynamic path planning and multi-obstacle avoidance model for UAV networks

Kanakam¹,

Sastry²

2017

IJET

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As the number of the obstacles are increasing along with the navigation paths, the security of the communication data also increasing exponentially in the dynamic UAV networks. A large number of path planning and obstacle avoidance models have been proposed on the static and dynamic UAV networks for data communication. Most of the traditional obstacle avoidance models are independent of security for communication data or path planning procedures. Since the UAV sensitive data are stored in each sensor along with the path planning information; there exists an unauthorized access or malicious access to the sensitive data from third party applications or users. Also, traditional standard cryptographic algorithms (both symmetric and asymmetric) are not efficient to provide complete security to vast amount of UAV data dynamically due to its high computational memory and time. In this paper, a novel trust based Ant Colony optimization model was proposed to secure the sensitive UAV data against unauthorized access. In this model, a novel cipher text policy based encryption and decryption procedure is used as an extension to KP-ABE for UAV path security. Experimental results proved that the proposed trust based optimization model is better than the traditional UAV security models in terms of computational time and memory.

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“…Coverage tasks are most similar to our problem, which require a team of robots to collectively observe every location in an environment [25]. Target tracking and search problems require using the sensing capabilities of multiple robots to locate and maintain contact with targets [8], [10], [11]. Mapping tasks require adaptively exploring unobserved regions [10].…”

Section: Related Workmentioning

confidence: 99%

“…The informativeness of observations, and therefore the performance of perception algorithms, can be improved by judiciously selecting observation locations [4]. Performance can be significantly improved by using longer planning horizons [5], [6], [7], jointly planning for multiple robots [8], [9], [10], [11] and considering larger sets of candidate sensing locations. However, current planning algorithms with these properties are often too computationally expensive for practical use in large scale and more complex active perception tasks; we propose a self-organising map algorithm as a solution to bridge this gap.…”

Section: Introductionmentioning

confidence: 99%

Multi-robot path planning for budgeted active perception with self-organising maps

Best

Faigl

2016

2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Abstract-We propose a self-organising map (SOM) algorithm as a solution to a new multi-goal path planning problem for active perception and data collection tasks. We optimise paths for a multi-robot team that aims to maximally observe a set of nodes in the environment. The selected nodes are observed by visiting associated viewpoint regions defined by a sensor model. The key problem characteristics are that the viewpoint regions are overlapping polygonal continuous regions, each node has an observation reward, and the robots are constrained by travel budgets. The SOM algorithm jointly selects and allocates nodes to the robots and finds favourable sequences of sensing locations. The algorithm has polynomial-bounded runtime independent of the number of robots. We demonstrate feasibility for the active perception task of observing a set of 3D objects. The viewpoint regions consider sensing ranges and selfocclusions, and the rewards are measured as discriminability in the ensemble of shape functions feature space. Simulations were performed using a 3D point cloud dataset from a real robot in a large outdoor environment. Our results show the proposed methods enable multi-robot planning for budgeted active perception tasks with continuous sets of candidate viewpoints and long planning horizons.

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Active Magnetic Anomaly Detection Using Multiple Micro Aerial Vehicles

Cited by 36 publications

References 22 publications

A Survey on Aerial Swarm Robotics

A Survey on Aerial Swarm Robotics

Secured dynamic path planning and multi-obstacle avoidance model for UAV networks

Multi-robot path planning for budgeted active perception with self-organising maps

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