Path planning for a tethered mobile robot

Kim, Soonkyum; Bhattacharya, Subhrajit; Kumar, Vijay

doi:10.1109/icra.2014.6906996

Cited by 46 publications

(55 citation statements)

References 14 publications

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“…Now, given a discrete graph representation of the configuration space, G = (V, E) (i.e., the vertex set, V , consists of points in X, and the edge set, E, contains edges that connect neighboring vertices) such that x s ∈ V , we construct an h-augmented graph, G h = (V h , E h ), which is essentially a lift of G into the universal covering space of X (Hatcher 2001). The construction of such augmented graphs has been described in our prior work (Bhattacharya et al 2015;Kim et al 2014), and the explicit construction of G h can be described as follows:…”

Section: Application To Graph Search-based Path Planningmentioning

confidence: 99%

“…Motivation: Homotopy Invariant in (R 2 − O) We are interested in constructing computable homotopy invariants for trajectories in a configuration space that are amenable to graph search-based path planning. To that end there is a very simple construction for configuration spaces of the form R 2 − O (Euclidean plane punctured by obstacles) (Grigoriev and Slissenko 1998;Hershberger and Snoeyink 1991;Tovar et al 2008;Bhattacharya et al 2015;Kim et al 2014): We start by placing representative points, ζ i , inside the i th connected component of the obstacles, O i ⊂ O. We then construct non-intersecting rays, r 1 , r 2 , · · · , r m , emanating from the representative points (this is always possible, for example, by choosing the rays to be parallel to each other).…”

Section: Configuration Spaces With Free Fundamental Groupsmentioning

confidence: 99%

“…Figure 4(a) shows 5 such optimal trajectories in the graph, connecting a given start and goal vertex, where G was constructed by an uniform hexagonal discretization of the planar configuration space. One can then employ a simple curve shortening algorithm (Kim et al 2014) to obtain ones more optimal than the ones restricted to G (Figure 4(b)). Similarly, shortest trajectories connecting x s and x e can be obtained in 3-dimensional configuration spaces (Figure 4(d)) with free fundamental group.…”

Section: Application To Graph Search-based Path Planningmentioning

confidence: 99%

“…For example, consideration of homotopy classes is vital in planning trajectories for robot teams separating/caging and transporting objects using a flexible cable (Bhattacharya et al 2015), or in planning optimal trajectories for robots that are tethered to a base using a fixed-length flexible cable (Kim et al 2014). This paper addresses the problem of optimal path planning with homotopy class as the optimization constraint.…”

Section: Introductionmentioning

confidence: 99%

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Path Homotopy Invariants and their Application to Optimal Trajectory Planning

Bhattacharya

2015

Proceedings of the IMA Conference on Mathematics of Robotics

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We consider the problem of optimal path planning in different homotopy classes in a given environment. Though important in robotics applications, path-planning with reasoning about homotopy classes of trajectories has typically focused on subsets of the Euclidean plane in the robotics literature. The problem of finding optimal trajectories in different homotopy classes in more general configuration spaces (or even characterizing the homotopy classes of such trajectories) can be difficult. In this paper we propose automated solutions to this problem in several general classes of configuration spaces by constructing presentations of fundamental groups and giving algorithms for solving the word problem in such groups. We present explicit results that apply to knot and link complements in 3-space, and also discuss how to extend to cylindricallydeleted coordination spaces of arbitrary dimension.

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Section: Application To Graph Search-based Path Planningmentioning

confidence: 99%

Section: Configuration Spaces With Free Fundamental Groupsmentioning

confidence: 99%

Section: Application To Graph Search-based Path Planningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Path Homotopy Invariants and their Application to Optimal Trajectory Planning

Bhattacharya

2015

Proceedings of the IMA Conference on Mathematics of Robotics

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“…Since we plan trajectories in a graph, our method lends itself quite naturally to homotopy-aware path planning, where we can compute shortest trajectories restricted to different homotopy classes. The basic algorithm for doing this is outlined in [3,13]. The fundamental idea is to use certain homotopy invariants (called h-signature) [2] to construct a homotopy-augmented graph, G h = (V h , E h ), in which every vertex is a pair of the form (m, w) ∈ V h , with m ∈ V and w is a "word" made up of letters associated with non-intersecting rays emanating from connected components of obstacles (see Figure 4(b)).…”

Section: B Planning Optimal Trajectories In Different Topological CLmentioning

confidence: 99%

Time and Energy Optimal Path Planning in General Flows

Kularatne

Bhattacharya

Hsieh

Robotics: Science and Systems XII

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Abstract-Autonomous surface and underwater vehicles (ASVs and AUVs) are increasingly being used for persistent monitoring of ocean phenomena. Typically, these vehicles are deployed for long periods of time and must operate with limited energy budgets. As a result, there is increased interest in recent years on developing energy efficient motion plans for these vehicles that leverage the dynamics of the surrounding flow field. In this paper, we present a graph search based method to plan time and energy optimal paths in a flow field where the kinematic actuation constraints on the vehicles are captured in our cost functions. We also use tools from topological path planning to generate optimal paths in different homotopy classes, which facilitates simultaneous exploration of the environment. The proposed strategy is validated using analytical flow models for large scale ocean circulation and in experiments using an indoor laboratory testbed capable of creating flows with ocean-like features. We also present a Riemannian metric based approximation for these cost functions which provides an alternative method for computing time and energy optimal paths. The Riemannian approximation results in smoother trajectories in contrast to the graph based approach while requiring less computational time. I. INTRODUCTIONTo better understand oceanic processes, researchers are employing autonomous underwater and surface vehicles (AUVs and ASVs) for long-term surveillance of the dynamics of plankton assemblages [5], temperature and salinity profiles [19,29,27], and the onset of harmful algae blooms [6,9]. In these and similar environmental monitoring applications in the ocean, AUVs and ASVs are often deployed over long periods while operating with limited energy budgets. As such, there is increased interest in recent years on developing more energy efficient motion and trajectory plans for AUVs and ASVs.In recent years, researchers have demonstrated how AUV/ASV motion planning and adaptive sampling strategies can be improved by incorporating either historical or current ocean flow data [24,26,25]. In [24,26], Smith et al. rely on regional ocean model systems (ROMS) to predict the dynamics of an evolving ocean front and uses the resulting predictions to generate waypoints for the AUV to enable it to track the feature of interest. The approach was then integrated with an unscented Kalman filter to better estimate the vehicle's deadreckoning error along a given path in [25]. Path planning for an AUV tasked with an adaptive sampling task was achieved in [8] by employing Lagrangian drifters to drift along a "patch" of water of interest. Rather than rely on ROMS data, Das et al. effectively achieves real-time sampling of the ocean currents in the region of interest using the drifters. Wu et al. solves the inverse problem in [30] where differences in actual and

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Shared autonomy for low‐cost underwater vehicles

Lawrance

DeBortoli

Jones

et al. 2018

Journal of Field Robotics

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This field report presents an overview of the development and testing of a semiautonomous underwater vehicle (sAUV). The work presented here is aimed at bridging the gap between current remotely operated vehicles and autonomous research platforms by developing shared autonomy capabilities for low-cost underwater vehicles. We use commercially available components and open-source software interfaces to provide a wider range of capabilities for underwater autonomy research at a lower cost than previously available systems. We describe the overall structure of the system, discuss its capabilities, and provide results demonstrating system performance. We place particular emphasis on shared autonomy, where a human operator is assisted in controlling an underwater tethered vehicle. We present three capabilities developed for the sAUV: (a) an assisted control mode that provides a variable level of assistance using an on-line estimate of user skill level, (b) a planner to generate paths that avoid tether entanglement, and (c) a sonar processing algorithm that identifies informative sonar images for selecting features for 3D scene reconstruction. The vehicle has been deployed on five off-shore and near-shore marine field deployments since 2015, and this report includes selected results from four of those trials to demonstrate the capabilities and limitations of the sAUV system. K E Y W O R D S human robot interaction, perception, planning, underwater robotics 1 | INTRODUCTION Unmanned underwater vehicles (UUVs) are used for a wide variety of tasks, from short-duration (hour-scale) inspection tasks to longduration (month-scale) data gathering missions. Within this group of vehicles there exists a wide range of autonomous capability, from limited or no autonomy on most industry-standard remotely operated vehicles (ROVs) to full autonomy on research-grade autonomous underwater vehicles (AUVs). In this paper, we examine partial-autonomy approaches that leverage the capabilities of autonomous systems to complement the skills of human operators.We use the term semi-autonomous to refer to a system that has some autonomous capabilities but must rely on a human operator for certain mission-critical capabilities. In this study, we develop and demonstrate a semi-autonomous underwater vehicle (sAUV) system.One of the primary limitations to deployment of AUVs is the perception that autonomy capabilities are not sufficiently robust for wide adoption. In many cases, the expense and specialized design of AUVs for particular tasks limits their applicability to a wider audience.In time-sensitive deployments, the robustness and task flexibility of ROVs means that they can be more readily deployed for a wider variety of tasks than autonomous systems (Murphy et al., 2012). J Field Robotics. 2019;36:495-516.wileyonlinelibrary.com/journal/rob

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Path planning for a tethered mobile robot

Cited by 46 publications

References 14 publications

Path Homotopy Invariants and their Application to Optimal Trajectory Planning

Path Homotopy Invariants and their Application to Optimal Trajectory Planning

Time and Energy Optimal Path Planning in General Flows

Shared autonomy for low‐cost underwater vehicles

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