Maurice Fallon

Cooperative localization for autonomous underwater vehicles

This paper describes an algorithm for distributed acoustic navigation for Autonomous Underwater Vehicles (AUVs). Whereas typical AUV navigation systems utilize pre-calibrated arrays of static transponders, our work seeks to create a fully mobile network of AUVs that perform acoustic ranging and data exchange with one another to achieve cooperative positioning for extended duration missions over large areas. The algorithm enumerates possible solutions for the AUV trajectory based on dead-reckoning and range-only measurements provided by acoustic modems that are mounted on each vehicle, and chooses the trajectory via minimization of a cost function based on these constraints. The resulting algorithm is computationally efficient, meets the strict bandwidth requirements of available AUV modems, and has potential to scale well to networks of large numbers of vehicles. The method has undergone extensive experimentation, and results from three different scenarios are reported in this paper, each of which utilizes MIT SCOUT Autonomous Surface Craft (ASC) as convenient platforms for testing. In the first experiment, we utilize three ASCs, each equipped with a Woods Hole acoustic modem, as surrogates for AUVs. In this scenario, two ASCs serve as Communication/Navigation Aids (CNAs) for a third ASC that computes its position based exclusively on GPS positions of the CNAs and acoustic range measurements between platforms. In the second scenario, an undersea glider is used in conjunction with two ASCs serving as CNAs. Finally, in the third experiment, a Bluefin12 AUV serves as the target vehicle. All three experiments demonstrate the successful operation of the technique with real ocean data.

Optimization-based locomotion planning, estimation, and control design for the atlas humanoid robot

This paper describes a collection of optimization algorithms for achieving dynamic planning, control, and state estimation for a bipedal robot designed to operate reliably in complex environments. To make challenging locomotion tasks tractable, we describe several novel applications of convex, mixed-integer, and sparse nonlinear optimization to problems ranging from footstep placement to whole-body planning and control. We also present a state estimator formulation that, when combined with our walking controller, permits highly precise execution of extended walking plans over non-flat terrain. We describe our complete system integration and experiments carried out on Atlas, a full-size hydraulic humanoid robot built by Boston Dynamics, Inc.

Cooperative AUV Navigation using a Single Maneuvering Surface Craft

In this paper we describe the experimental implementation of an online algorithm for cooperative localization of submerged autonomous underwater vehicles (AUVs) supported by an autonomous surface craft. Maintaining accurate localization of an AUV is difficult because electronic signals, such as GPS, are highly attenuated by water. The usual solution to the problem is to utilize expensive navigation sensors to slow the rate of dead-reckoning divergence. We investigate an alternative approach that utilizes the position information of a surface vehicle to bound the error and uncertainty of the on-board position estimates of a low-cost AUV. This approach uses the Woods Hole Oceanographic Institution (WHOI) acoustic modem to exchange vehicle location estimates while simultaneously estimating inter-vehicle range. A study of the system observability is presented so as to motivate both the choice of filtering approach and surface vehicle path planning. The first contribution of this paper is to the presentation of an experiment in which an extended Kalman filter (EKF) implementation of the concept ran online on-board an OceanServer Iver2 AUV while supported by an autonomous surface vehicle moving adaptively. The second contribution of this paper is to provide a quantitative performance comparison of three estimators: particle filtering (PF), non-linear least-squares optimization (NLS), and the EKF for a mission using three autonomous surface craft (two operating in the AUV role). Our results indicate that the PF and NLS estimators outperform the EKF, with NLS providing the best performance.

Cooperative AUV Navigation Using a Single Surface Craft

Maintaining accurate localization of an autonomous underwater vehicle (AUV) is difficult because electronic signals such as GPS are highly attenuated by water making established land-based localization systems, such as GPS, useless underwater. Instead we propose an alternative approach which integrates position information of other vehicles to reduce the error and uncertainty of the on-board position estimates of the AUV. This approach uses the WHOI Acoustic Modem to exchange vehicle localization estimates—albeit at low transmission rates—while simultaneously estimating inter-vehicle range. The performance capabilities of the system were tested using Oceanserver’s Iver2 and the MIT Scout kayaks.

An Architecture for Online Affordance-based Perception and Whole-body Planning

The DARPA Robotics Challenge Trials held in December 2013 provided a landmark demonstration of dexterous mobile robots executing a variety of tasks aided by a remote human operator using only data from the robots sensor suite transmitted over a constrained, field-realistic communications link. We describe the design considerations, architecture, implementation, and performance of the software that Team MIT developed to command and control an Atlas humanoid robot. Our design emphasized human interaction with an efficient motion planner, where operators expressed desired robot actions in terms of affordances fit using perception and manipulated in a custom user interface. We highlight several important lessons we learned while developing our system on a highly compressed schedule.

Efficient scene simulation for robust monte carlo localization using an RGB-D camera

This paper presents Kinect Monte Carlo Localization (KMCL), a new method for localization in three dimensional indoor environments using RGB-D cameras, such as the Microsoft Kinect. The approach makes use of a low fidelity a priori 3-D model of the area of operation composed of large planar segments, such as walls and ceilings, which are assumed to remain static. Using this map as input, the KMCL algorithm employs feature-based visual odometry as the particle propagation mechanism and utilizes the 3-D map and the underlying sensor image formation model to efficiently simulate RGB-D camera views at the location of particle poses, using a graphical processing unit (GPU). The generated 3D views of the scene are then used to evaluate the likelihood of the particle poses. This GPU implementation provides a factor of ten speedup over a pure distance-based method, yet provides comparable accuracy. Experimental results are presented for five different configurations, including: (1) a robotic wheelchair, (2) a sensor mounted on a person, (3) an Ascending Technologies quadrotor, (4) a Willow Garage PR2, and (5) an RWI B21 wheeled mobile robot platform. The results demonstrate that the system can perform robust localization with 3D information for motions as fast as 1.5 meters per second. The approach is designed to be applicable not just for robotics but other applications such as wearable computing.

Sensor fusion for flexible human-portable building-scale mapping

This paper describes a system enabling rapid multi-floor indoor map building using a body-worn sensor system fusing information from RGB-D cameras, LIDAR, inertial, and barometric sensors. Our work is motivated by rapid response missions by emergency personnel, in which the capability for one or more people to rapidly map a complex indoor environment is essential for public safety. Human-portable mapping raises a number of challenges not encountered in typical robotic mapping applications including complex 6-DOF motion and the traversal of challenging trajectories including stairs or elevators. Our system achieves robust performance in these situations by exploiting state-of-the-art techniques for robust pose graph optimization and loop closure detection. It achieves real-time performance in indoor environments of moderate scale. Experimental results are demonstrated for human-portable mapping of several floors of a university building, demonstrating the systems ability to handle motion up and down stairs and to organize initially disconnected sets of submaps in a complex environment.

Acoustic Source Localization and Tracking of a Time-Varying Number of Speakers

Particle filter-based acoustic source tracking algorithms track (online and in real-time) the position of a sound source-a person speaking in a room-based on the current data from a distributed microphone array as well as the previously recorded data. This paper develops a multi-target tracking (MTT) methodology to allow for an unknown and time-varying number of speakers in a fully probabilistic manner and in doing so does not resort to independent modules for new target proposal or target number estimation as in previous works. The approach uses the concept of an existence grid to propose possible regions of activity before tracking is carried out with a variable dimension particle filter-which also explicitly supports the concept of a null particle, containing no target states, when no speakers are active. Examples demonstrate typical tracking performance in a number of different scenarios with simultaneously active speech sources.

Drift-free humanoid state estimation fusing kinematic, inertial and LIDAR sensing

This paper describes an algorithm for the probabilistic fusion of sensor data from a variety of modalities (inertial, kinematic and LIDAR) to produce a single consistent position estimate for a walking humanoid. Of specific interest is our approach for continuous LIDAR-based localization which maintains reliable drift-free alignment to a prior map using a Gaussian Particle Filter. This module can be bootstrapped by constructing the map on-the-fly and performs robustly in a variety of challenging field situations. We also discuss a two-tier estimation hierarchy which preserves registration to this map and other objects in the robots vicinity while also contributing to direct low-level control of a Boston Dynamics Atlas robot. Extensive experimental demonstrations illustrate how the approach can enable the humanoid to walk over uneven terrain without stopping (for tens of minutes), which would otherwise not be possible. We characterize the performance of the estimator for each sensor modality and discuss the computational requirements.

A measurement distribution framework for cooperative navigation using multiple AUVs

In recent years underwater survey and surveillance missions with more than a single Autonomous Underwater Vehicle (AUV) have become more common thanks to more reliable and cheaper platforms, as well as the addition of remote command and control communications using, for example, the WHOI acoustic modem. However cooperative navigation of AUVs has thus far been limited to a single AUV supported by a dedicated surface vehicle with access to GPS. In this paper a scalable and modular framework is presented in which any number of vehicles can broadcast, forward and acknowledge range, dead-reckoning, feature and GPS measurements so that the full fleet of AUVs can navigate and cooperate in a consistent and accurate manner. The approach is independent of the resultant application—such as recursive state estimation or full pose optimization. Trade-offs between the number of vehicles, the condition of the communication channel and rate at which updates are available are also discussed. Finally performance is illustrated in a realistic experiment.