Gaurav S. Sukhatme

article

Data‐driven learning and planning for environmental sampling

Kai‐Chieh Ma, Lantao Liu, Hordur K. Heidarsson, Gaurav S. Sukhatme

Journal of Field Robotics > 35 > 5 > 643 - 661

Robots such as autonomous underwater vehicles (AUVs) and autonomous surface vehicles (ASVs) have been used for sensing and monitoring aquatic environments such as oceans and lakes. Environmental sampling is a challenging task because the environmental attributes to be observed can vary both spatially and temporally, and the target environment is usually a large and continuous domain whereas the sampling...

article

Interactive Perception: Leveraging Action in Perception and Perception in Action

Jeannette Bohg, Karol Hausman, Bharath Sankaran, Oliver Brock, more

IEEE Transactions on Robotics > 2017 > 33 > 6 > 1273 - 1291

Recent approaches in robot perception follow the insight that perception is facilitated by interaction with the environment. These approaches are subsumed under the term Interactive Perception (IP). This view of perception provides the following benefits. First, interaction with the environment creates a rich sensory signal that would otherwise not be present. Second, knowledge of the regularity in...

chapter

Planning high-speed safe trajectories in confidence-rich maps

Eric Heiden, Karol Hausman, Gaurav S. Sukhatme, Ali-akbar Agha-mohammadi

2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) > 2880 - 2886

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

Planning safe, high-speed trajectories in unknown environments remains a major roadblock on the way toward achieving fast autonomous flight. Current state-of-the-art planning approaches use sampling-based methods or trajectory optimization to obtain fast trajectories, whose safety is evaluated by taking into account the current state estimate of the environment. In unknown environments, however, this...

chapter

A spatio-temporal representation for the orienteering problem with time-varying profits

Zhibei Ma, Kai Yin, Lantao Liu, Gaurav S. Sukhatme

2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) > 6785 - 6792

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

We consider an orienteering problem (OP) where an agent needs to visit a series (possibly a subset) of depots, from which the maximal accumulated profits are desired within given limited time budget. Different from most existing works where the profits are assumed to be static, in this work we investigate a variant that has arbitrary time-dependent profits. Specifically, the profits to be collected...

chapter

Downwash-aware trajectory planning for large quadrotor teams

James A. Preiss, Wolfgang Honig, Nora Ayanian, Gaurav S. Sukhatme

2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) > 250 - 257

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

We describe a method for formation-change trajectory planning for large quadrotor teams in obstacle-rich environments. Our method decomposes the planning problem into two stages: a discrete planner operating on a graph representation of the workspace, and a continuous refinement that converts the non-smooth graph plan into a set of C^k-continuous trajectories, locally optimizing an integral-squared-derivative...

article

Generalized Topology Control for Nonholonomic Teams With Discontinuous Interactions

Ryan K. Williams, Andrea Gasparri, Giovanni Ulivi, Gaurav S. Sukhatme

IEEE Transactions on Robotics > 2017 > 33 > 4 > 994 - 1001

In this paper, we consider the problem of general topology control in multirobot systems with nonholonomic kinematics. Our contribution is twofold: We first demonstrate the correctness of topology control under the assumption that the network topology can switch arbitrarily and that potential-based mobility is discontinuous with respect to topology changes; we then demonstrate that a multirobot team...

article

Observability-Aware Trajectory Optimization for Self-Calibration With Application to UAVs

Karol Hausman, James Preiss, Gaurav S. Sukhatme, Stephan Weiss

IEEE Robotics and Automation Letters > 2017 > 2 > 3 > 1770 - 1777

We study the nonlinear observability of a system's states in view of how well they are observable and what control inputs would improve the convergence of their estimates. We use these insights to develop an observability-aware trajectory-optimization framework for nonlinear systems that produces trajectories well suited for self-calibration. Our method reasons about the quality of observability while...

chapter

Informative planning and online learning with sparse Gaussian processes

Kai-Chieh Ma, Lantao Liu, Gaurav S. Sukhatme

2017 IEEE International Conference on Robotics and Automation (ICRA) > 4292 - 4298

2017 IEEE International Conference on Robotics and Automation (ICRA)

A big challenge in environmental monitoring is the spatiotemporal variation of the phenomena to be observed. To enable persistent sensing and estimation in such a setting, it is beneficial to have a time-varying underlying environmental model. Here we present a planning and learning method that enables an autonomous marine vehicle to perform persistent ocean monitoring tasks by learning and refining...

chapter

Crazyswarm: A large nano-quadcopter swarm

James A. Preiss, Wolfgang Honig, Gaurav S. Sukhatme, Nora Ayanian

2017 IEEE International Conference on Robotics and Automation (ICRA) > 3299 - 3304

2017 IEEE International Conference on Robotics and Automation (ICRA)

We define a system architecture for a large swarm of miniature quadcopters flying in dense formation indoors. The large number of small vehicles motivates novel design choices for state estimation and communication. For state estimation, we develop a method to reliably track many small rigid bodies with identical motion-capture marker arrangements. Our communication infrastructure uses compressed...

chapter

Multi-robot coordination through dynamic Voronoi partitioning for informative adaptive sampling in communication-constrained environments

Stephanie Kemna, John G. Rogers, Carlos Nieto-Granda, Stuart Young, more

2017 IEEE International Conference on Robotics and Automation (ICRA) > 2124 - 2130

2017 IEEE International Conference on Robotics and Automation (ICRA)

Autonomous underwater vehicles (AUVs) are cost- and time-efficient systems for environmental sampling. Informative adaptive sampling has been shown to be an effective method of sampling a lake or ocean for environmental modeling. In this paper, we focus on multi-robot coordination for informative adaptive sampling. We use a dynamic Voronoi partitioning approach whereby the vehicles, in a decentralized...

chapter

Feature selection for learning versatile manipulation skills based on observed and desired trajectories

Oliver Kroemer, Gaurav S. Sukhatme

2017 IEEE International Conference on Robotics and Automation (ICRA) > 4713 - 4720

2017 IEEE International Conference on Robotics and Automation (ICRA)

For a manipulation skill to be applicable to a wide range of scenarios, it must generalize between different objects and object configurations. Robots should therefore learn skills that adapt to features describing the objects being manipulated. Most of these object features will however be irrelevant for generalizing the skill and, hence, the robot should select a small set of relevant features for...

article

Response of phytoplankton and bacterial biomass during a wastewater effluent diversion into nearshore coastal waters

David A. Caron, Alyssa G. Gellene, Jayme Smith, Erica L. Seubert, more

Estuarine, Coastal and Shelf Science > 2017 > 186 > PB > 223-236

A 3-week diversion of the Orange County Sanitation District effluent discharge into nearshore waters off Newport Beach, CA constituted a considerable injection of secondarily-treated effluent into the coastal ecosystem. The location ≈1.6 km from shore, shallow water depth (≈16 m), volume and nutrient content of the discharge (≈5.3 × 10⁸ L day⁻¹ of effluent with inorganic nitrogen concentration >2 mM)...

chapter

An adaptive k-opt method for solving traveling salesman problem

Zhibei Ma, Lantao Liu, Gaurav S. Sukhatme

2016 IEEE 55th Conference on Decision and Control (CDC) > 6537 - 6543

2016 IEEE 55th Conference on Decision and Control (CDC)

This paper presents a new heuristic solution to the traveling salesman problem (TSP). Inspired by an existing technique that employs the task swap mechanism to solve the multi-agent task allocation, we exploit the adaptive k-swap based searching process and take into account the newly introduced subtour constraint, and propose a new variant of k-opt method for incrementally improving suboptimal but...

article

Circling the Seas: Design of Lagrangian Drifters for Ocean Monitoring

Supreeth Subbaraya, Andreas Breitenmoser, Artem Molchanov, Jorg Muller, more

IEEE Robotics & Automation Magazine > 2016 > 23 > 4 > 42 - 53

Lagrangian drifters are oceanographic devices used to study circulation patterns in the ocean. The devices are typically passive, which means that they have no actuators to initiate motion. They are mainly driven by the ocean currents. A drifter, as depicted in Figure 1, in principle, consists of a surface float and a drogue connected via a tether. The surface float provides the necessary buoyancy...

chapter

Learning spatial preconditions of manipulation skills using random forests

Oliver Kroemer, Gaurav S. Sukhatme

2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids) > 676 - 683

2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids)

Robots working in everyday and unstructured environments will need to perform manipulation skills using different sets of objects. To determine if a manipulation skill can be executed in a given situation, a robot will need to learn the preconditions of the skill. The robot will need to check both that the required objects are present in the scene and that they are arranged in a suitable manner for...

chapter

Activity-Based Semantic Mapping of an Urban Environment

Denis F. Wolf, Gaurav S. Sukhatme

Springer Tracts in Advanced Robotics > Experimental Robotics > Navigation > 321-329

We address the problem of semantic mapping using mobile robots. We focus on the problem of mapping activity as a precursor to automatically classifying, modeling and ultimately understanding the usage of space in a typical urban outdoor environment. We propose and compare two methods for activity mapping - one based on hidden Markov models and the other based on support vector machines. Both approaches...

chapter

An Experimental Study of Localization Using Wireless Ethernet

Andrew Howard, Sajid Siddiqi, And Gaurav S. Sukhatme

Springer Tracts in Advanced Robotics > Field and Service Robotics > 145-153

This paper studies the use of wireless Ethernet (Wi-Fi) as a localization sensor for mobile robots. Wi-Fi-based localization relies on the existence of one or more Wi-Fi devices in the environment to act as beacons, and uses signal strength information from those beacons to localize the robot. Through the experiments described in this paper, we explore the general properties of Wi-Fi in indoor...

chapter

Localization for Mobile Robot Teams: A Distributed MLE Approach

Andrew Howard, Maja J. Matarić, Gaurav S. Sukhatme

Springer Tracts in Advanced Robotics > Experimental Robotics VIII > Path Planning and Localization > 146-155

This paper describes a method for localizing the members of a mobile robot team, using only the robots themselves as landmarks. We assume that robots are equipped with sensors that allow them to measure the relative pose and identity of nearby robots, as well as sensors that allow them to measure changes in their own pose. Using a combination of maximum likelihood estimation and distributed numerical...

chapter

Occlusion-aware multi-robot 3D tracking

Karol Hausman, Gregory Kahn, Sachin Patil, Jorg Muller, more

2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) > 1863 - 1870

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

We introduce an optimization-based control approach that enables a team of robots to cooperatively track a target using onboard sensing. In this setting, the robots are required to estimate their own positions as well as concurrently track the target. Our probabilistic method generates controls that minimize the expected uncertainty of the target. Additionally, our method efficiently reasons about...

chapter

Online trajectory optimization to improve object recognition

Christian Potthast, Gaurav S. Sukhatme

2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) > 4765 - 4772

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

We present an online trajectory optimization approach that optimizes a trajectory such that object recognition performance is improved. Inspired by prior work, we formulate the optimization as a derivative-free stochastic optimization, allowing us to express the cost function in an arbitrary way. The cost function is defined such that information acquisition of target objects is improved, while simultaneously...

INFONA - science communication portal

Search results for: Gaurav S. Sukhatme

Data‐driven learning and planning for environmental sampling

Interactive Perception: Leveraging Action in Perception and Perception in Action

Planning high-speed safe trajectories in confidence-rich maps

A spatio-temporal representation for the orienteering problem with time-varying profits

Downwash-aware trajectory planning for large quadrotor teams

Generalized Topology Control for Nonholonomic Teams With Discontinuous Interactions

Observability-Aware Trajectory Optimization for Self-Calibration With Application to UAVs

Informative planning and online learning with sparse Gaussian processes

Crazyswarm: A large nano-quadcopter swarm

Multi-robot coordination through dynamic Voronoi partitioning for informative adaptive sampling in communication-constrained environments

Feature selection for learning versatile manipulation skills based on observed and desired trajectories

Response of phytoplankton and bacterial biomass during a wastewater effluent diversion into nearshore coastal waters

An adaptive k-opt method for solving traveling salesman problem

Circling the Seas: Design of Lagrangian Drifters for Ocean Monitoring

Learning spatial preconditions of manipulation skills using random forests

Activity-Based Semantic Mapping of an Urban Environment

An Experimental Study of Localization Using Wireless Ethernet

Localization for Mobile Robot Teams: A Distributed MLE Approach

Occlusion-aware multi-robot 3D tracking

Online trajectory optimization to improve object recognition

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