40 accepted papers
This letter presents a continuous probabilistic modeling methodology for spatial point cloud data using finite Gaussian Mixture Models (GMMs) where the number of components are adapted based on the scene complexity. Few hierarchical and adaptive methods have been proposed to address the challenge of
In human-in-the-loop navigation, the operator’s intention is to locally avoid obstacles while planning long-horizon paths in order to complete the navigation task. We propose a hierarchical teleoperation framework that captures these characteristics of intention, and generates trajectories that are…
This paper enhances the feedback linearization controller for multirotors with a learned acceleration error model and a thrust input delay mitigation model. Feedback linearization controllers are theoretically appealing but their performance suffers on real systems, where the true system does not ma…
In multi-robot multi-target tracking, robots coordinate to monitor groups of targets moving about an environment. We approach planning for such scenarios by formulating a receding-horizon, multi-robot sensing problem with a mutual information objective. Such problems are NP-Hard in general. Yet, our…
Volumetric objectives for exploration and perception tasks seek to capture a sense of value (or reward) for hypothetical observations at one or more camera views for robots operating in unknown environments. For example, a volumetric objective may reward robots proportionally to the expected volume…
This work addresses the problem of learning a model of a dynamic environment using many independent Hidden Markov Models (HMMs) with a limited number of observations available per iteration. Many techniques exist to model dynamic environments, but do not consider how to deploy robots to build this m…
We present a method to assist the operator in teleoperation of mobile robots by generating trajectories such that the vehicle completes the desired task with ease in unstructured environments. Traditional assisted teleoperation methods have focused on reactive methods to avoid collisions, but neglec…
This work addresses planning for multi-robot formations online in cluttered environments via a data-driven search approach. The user-specified objective function governing formation shape and rotation is expressed in terms of offline demonstrations of robot motions (performed in an obstacle free env…
State-of-the-art dense mapping approaches cannot be deployed on Size, Weight, and Power (SWaP) constrained platforms because of their large memory and compute requirements. In this paper, we present an accurate, and efficient approach to dense multi-fidelity 3D mapping using Gaussian distributions a…
Safe high-speed autonomous navigation for MAVs in unknown environments requires fast planning to enable the robot to adapt and react quickly to incoming information about obstacles within the world. Furthermore, when operating in environments not known a priori, the robot may make decisions that lea…
Traditional dense volumetric representations for robotic mapping make simplifying assumptions about sensor noise characteristics due to computational constraints. We present a framework that, unlike conventional occupancy grid maps, explicitly models the sensor ray formation for a depth sensor via…
We consider the kinodynamic multi-robot planning problem in cluttered 3-D workspaces. Reachability analysis on position invariant geometric trees is leveraged to find kino- dynamically feasible trajectories for the multi-robot team from potentially non-stationary initial states. The key contribution…
This letter presents a framework for planning and perception for multi-robot exploration in large and unstructured three-dimensional environments. We employ a Gaussian mixture model for global mapping to model complex environment geometries while maintaining a small memory footprint which enables di
In this work, we consider the problem of online centralized kinodynamic multi-robot replanning (from potentially non-stationary initial states) and coordination in known and cluttered workspaces. Offline state lattice reachability analysis is leveraged to decouple the planning problem into two seque…
We present a control strategy that applies inverse dynamics to a learned acceleration error model for accurate multirotor control input generation. This allows us to retain accurate trajectory and control input generation despite the presence of exogenous disturbances and modeling errors. Although a…
This paper proposes a method to deploy teams of robots with constrained energy capacities to persistently maintain a map of an uncertain environment. Typical occupancy map approaches assume a static world; however, we introduce a decay in confidence that degrades the occupancy probability of grid ce…
State-of-the-art forward facing monocular visual-inertial odometry algorithms are often brittle in practice, especially whilst dealing with initialisation and motion in directions that render the state unobservable. In such cases having a reliable complementary odometry algorithm enables robust and…
This paper develops an exploration framework that leverages Gaussian mixture models (GMMs) for high-fidelity perceptual modeling and exploits the compactness of the distributions for information sharing in communications-constrained applications. State-of-the-art, high-resolution perceptual modeling…
Occupancy mapping is fundamental for active perception systems to enable reasoning about known and unknown regions of the environment. The majority of occupancy mapping approaches enforce an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i
3D radiation source localization is a common task across applications such as decommissioning, disaster response, and security, but traditional count-based sensors struggle to efficiently disambiguate between symmetries in sensor, source, and environment configurations. Recent works have demonstrate…
Size, weight, and power constrained platforms impose constraints on computational resources that introduce unique challenges in implementing localization algorithms. We present a framework to perform fast localization on such platforms enabled by the compressive capabilities of Gaussian Mixture Mode…
This letter presents a robust Gaussian mixture model registration technique to enable mapping and navigation in dark, complex, unstructured domains such as caves and mines. Subterranean environments are often unmapped and challenged by low-lighting conditions and communication constraints. Prior wor
In unknown, cluttered environments, robots require online real-time mapping and collision checking in order to navigate robustly. Discrete map representations are inefficient for collision checking as they are expensive in terms of memory and computation. This paper takes a probabilistic approach to…
We propose a task-independent adaptive teleoperation methodology that seeks to improve operator performance and efficiency by concurrently modeling user intent and adapting the set of available actions according to the predicted intent. User input selects a robot motion from a finite set of dynamica…
This work considers estimation of mass parameters for multi-robot coordinated lifting in the context of coordinated aerial manipulation, and develops strategies for active parameter estimation for cooperative manipulation tasks through an information-theoretic framework. The active sensing problem i…
This work addresses the problem of efficient online exploration and mapping using multi-robot teams via a distributed algorithm for planning for multi-robot exploration---distributed sequential greedy assignment (DSGA)---based on the sequential greedy assignment (SGA) algorithm. SGA permits bounds o…
We present an extension to Experience-driven Predictive Control (EPC) that leverages a Gaussian belief propagation strategy to compute an uncertainty set bounding the evolution of the system state in the presence of time-varying state uncertainty. This uncertainty set is used to tighten the constrai…
This work presents Experience-driven Predictive Control (EPC) as a fast technique for solving nonlinear model predictive control (NMPC) problems with uncertain system dynamics. EPC leverages an affine dynamics model that is updated online via Locally Weighted Projection Regression (LWPR) to capture…
This paper presents a fast approximate multi-agent decision theoretic planning method extended from the well-known Markov Decision Process (MDP). Our objective is to plan motions for a team of homogeneous micro air vehicles (MAVs) toward a set of goals, such that each MAV at any state at any moment…
This paper presents a real-time, kinodynamic planning and information-theoretic exploration framework that enables high-resolution mapping of three-dimensional environments featuring complex concavities and disjoint objects. The proposed approach targets planetary exploration applications and seeks…
We consider the problem of generating dynamically feasible and safe plans for teams of aerial robots (quadrotors) while holding a fixed relative formation as well as transitioning between a sequence of formations. We extend the existing assignment and planning approaches for quadrotor teams to find…
In this work, we consider the problem of fast, accurate control of a robot with constrained dynamics. We present a new nonlinear model predictive control (MPC) technique, Nonlinear Partial Enumeration (NPE), that combines online and offline computation in a nonlinear version of the partial enumerati
This paper presents an online optimization-based approach to compute trajectories to enable substitution of robots in formation-based deployments with durations that exceed the energy capacity of individual systems. The proposed algorithm computes trajectories in a multi-robot context to ensure a co…
We demonstrate distributed, online, and real-time cooperative localization and mapping between multiple robots operating throughout an unknown environment using indirect measurements. We present a novel Expectation Maximization (EM) based approach to efficiently identify inlier multi-robot loop clos…
We propose an information-theoretic planning approach that enables mobile robots to autonomously construct dense 3D maps in a computationally efficient manner. Inspired by prior work, we accomplish this task by formulating an information-theoretic objective function based on Cauchy-Schwarz quadratic…
We develop a computationally efficient control policy for active perception that incorporates explicit models of sensing and mobility to build 3D maps with ground and aerial robots. Like previous work, our policy maximizes an information-theoretic objective function between the discrete occupancy be…
We propose information-theoretic strategies for Occupancy Grid (OG) compression to enable high-speed exploration on computationally constrained mobile robots. We first formulate optimal lossy compression for OGs based on the Principle of Relevant Information. The solution to this formulation is a si…
We propose the Stochastic Multi-Robot Persistent Coverage Problem (SMRPCP) and correspondant methodology to compute an optimal schedule that enables a fleet of energy-constrained unmanned aerial vehicles to repeatedly perform a set of tasks while maximizing the frequency of task completion and prese…
In this paper, we present an algorithm to solve the Multi-Robot Persistent Coverage Problem (MRPCP). Here, we seek to compute a schedule that will allow a fleet of agents to visit all targets of a given set while maximizing the frequency of visitation and maintaining a sufficient fuel capacity by re…
There have been increasing interests in the robotics community in building smaller and more agile autonomous micro aerial vehicles (MAVs). In particular, the monocular visual-inertial system (VINS) that consists of only a camera and an inertial measurement unit (IMU) forms a great minimum sensor sui…