Experimental Implementation of Spectral Multiscale Coverage and Search Algorithms for Autonomous UAVs

Mathew, George; Kannan, Suresh K.; Surana, Amit; Bajekal, Sanjay; Chevva, Konda Reddy

doi:10.2514/6.2013-5182

Cited by 8 publications

(12 citation statements)

References 13 publications

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“…In this section, we first compare our algorithm with the implementation of Mathew et. al [24] in an unconstrained environment. In Section IV-B, we demonstrate that our formulation can capture coordination of multiple robots equipped with different sensing capabilities.…”

Section: Resultsmentioning

confidence: 99%

Ergodic coverage in constrained environments using stochastic trajectory optimization

Ayvali

Salman

Choset

2017

2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Abstract-In search and surveillance applications in robotics, it is intuitive to spatially distribute robot trajectories with respect to the probability of locating targets in the domain. Ergodic coverage is one such approach to trajectory planning in which a robot is directed such that the percentage of time spent in a region is in proportion to the probability of locating targets in that region. In this work, we extend the ergodic coverage algorithm to robots operating in constrained environments and present a formulation that can capture sensor footprint and avoid obstacles and restricted areas in the domain. We demonstrate that our formulation easily extends to coordination of multiple robots equipped with different sensing capabilities to perform ergodic coverage of a domain.

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Section: Resultsmentioning

confidence: 99%

Ergodic coverage in constrained environments using stochastic trajectory optimization

Ayvali

Salman

Choset

2017

2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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“…This paper keeps the original methodology proposed by [3], which is the foundation of most literatures on ergodic control, and which has the advantage of providing closed form solutions for many of the commonly used models of dynamical systems (kinematics-based) [4], while providing multi-scale coverage behaviour. To do so, we propose a solution based on a low-rank tensor approximation to overcome the curse of dimensionality.…”

Section: A Ergodic Controlmentioning

confidence: 99%

“…Ergodic control, on the other hand, aims to design a control policy for a given autonomous system so that the trajectory evolution of the resulting dynamical system is ergodic for the reference probability distribution. Systems engineered in such a way have already found applications in robotics [2], [4]. The original approach to ergodic control, as proposed in [3], involves spectral analysis of the dynamical system evolution.…”

Section: Introductionmentioning

confidence: 99%

“…This elegant method was proposed for point-mass systems having receding horizon control with infinitesimal control horizon. This original work paved the way for various extensions, with other types of dynamical systems and finite-horizon controllers [4], [2], [5]. The idea behind [3] is to minimize a metric, called the ergodic metric, that quantifies the match between the Fourier coefficients of a reference distribution and those of the time-averaged statistics of the system trajectory.…”

Section: Introductionmentioning

confidence: 99%

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Ergodic Exploration using Tensor Train: Applications in Insertion Tasks

Shetty¹,

Silvério²,

Calinon³

2021

Preprint

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By generating control policies that create natural search behaviors in autonomous systems, ergodic control provides a principled solution to address tasks that require exploration. A large class of ergodic control algorithms relies on spectral analysis, which suffers from the curse of dimensionality, both in storage and computation. This drawback has prohibited the application of ergodic control in robot manipulation since it often requires exploration in state space with more than 2 dimensions. Indeed, the original ergodic control formulation will typically not allow exploratory behaviors to be generated for a complete 6D end-effector pose. In this paper, we propose a solution for ergodic exploration based on spectral analysis in multidimensional spaces using low-rank tensor approximation techniques. We rely on tensor train decomposition, a recent approach from multilinear algebra for low-rank approximation and efficient computation of multidimensional arrays. The proposed solution is efficient both computationally and storage-wise, hence making it suitable for its online implementation in robotic systems. The approach is applied to a peg-in-hole insertion task using a 7-axis Franka Emika Panda robot, where ergodic exploration allows the task to be achieved without requiring the use of force/torque sensors.

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“…The algorithm that we use here is a new variant of the Spectral Multi-scale Coverage (SMC) algorithm which was proposed in 36 . This algorithm was extended to dynamic environments in 37 (called DSMC) and its practical feasibility was shown in 38 . We give a short introduction to DSMC algorithm and emphasize the alterations and that leads to the algorithm introduced in this paper called modified DSMC (mDSMC).…”

Section: Search In Dynamic Environmentsmentioning

confidence: 99%

Search strategy in a complex and dynamic environment: the MH370 case

Ivić

Crnković

Arbabi

et al. 2020

Sci Rep

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Search and detection of objects on the ocean surface is a challenging task due to the complexity of the drift dynamics and lack of known optimal solutions for the path of the search agents. This challenge was highlighted by the unsuccessful search for Malaysian Flight 370 (MH370) which disappeared on March 8, 2014. In this paper, we propose an improvement of a search algorithm rooted in the ergodic theory of dynamical systems which can accommodate complex geometries and uncertainties of the drifting search areas on the ocean surface. We illustrate the effectiveness of this algorithm in a computational replication of the conducted search for MH370. We compare the algorithms using many realizations with random initial positions, and analyze the influence of the stochastic drift on the search success. In comparison to conventional search methods, the proposed algorithm leads to an order of magnitude improvement in success rate over the time period of the actual search operation. Simulations of the proposed search control also indicate that the initial success rate of finding debris increases in the event of delayed search commencement. This is due to the existence of convergence zones in the search area which leads to local aggregation of debris in those zones and hence reduction of the effective size of the area to be searched.

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Experimental Implementation of Spectral Multiscale Coverage and Search Algorithms for Autonomous UAVs

Cited by 8 publications

References 13 publications

Ergodic coverage in constrained environments using stochastic trajectory optimization

Ergodic coverage in constrained environments using stochastic trajectory optimization

Ergodic Exploration using Tensor Train: Applications in Insertion Tasks

Search strategy in a complex and dynamic environment: the MH370 case

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