Evaluation of an acoustic detection algorithm for reactive collision avoidance in underwater applications

Thesis (S.B.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (page 33).This thesis sought to evaluate a vehicle detection algorithm based on a passive acoustic sensor, intended for autonomous collision avoidance in Unmanned Underwater Vehicles. By placing a hydrophone at a safe distance from a dock, it was possible to record the acoustic signature generated by a small motor boat as it navigated towards, and then away from the sensor. The time-varying sound intensity was estimated by Root Mean Square of the sound amplitude in discrete samples. The time-derivative of the sound intensity was then used to estimate the time to arrival, or collision, of the acoustic source. The algorithm was found to provide a good estimate of the time to collision, with a small standard deviation for the projected collision time, when the acoustic source was moving at approximately constant speed, providing validation of the model at the proof-of-concept level.by Oscar Alberto Viquez Rojas.S.B

Real Time Underwater Obstacle Avoidance and Path Re-planning Using Simulated Multi-beam Forward Looking Sonar Images for Autonomous Surface Vehicle

This paper describes underwater obstacle avoidance and path re-planning techniques for autonomous surface vehicle (ASV) based on simulated multi-beam forward looking sonar images. The sonar image is first simulated and then a circular obstacle is defined and created in the field of view of the sonar. In this study, the robust real-time path re-planning algorithm based on an A* algorithm is developed. Our real-time path re-planning algorithm has been tested to regenerate the optimal path for several updated frames with a proper update frequency between the start point and the goal point both in static and dynamical environments. The performance of proposed method is verified through simulations, and tank experiments using an actual ASV. While the simulation results are successful, the vehicle model can avoid both single obstacle, multiple obstacles and moving obstacle with the optimal trajectory. For tank experiments, the proposed method for underwater obstacle avoidance system is implemented with the ASV test platform. The vehicle is controlled in real-time and moderately succeeds in its avoidance against the obstacle simulated in the field of view of the sonar together with the proposed position stochastic estimation of the vehicle

Investigation of the Dynamic Buckling of Spherical Shell Structures Due to Subsea Collisions

This paper is the first to present the dynamic buckling behavior of spherical shell structures colliding with an obstacle block under the sea. The effect of deep water has been considered as a uniform external pressure by simplifying the effect of fluid–structure interaction. The calibrated numerical simulations were carried out via the explicit finite element package LS-DYNA using different parameters, including thickness, elastic modulus, external pressure, added mass, and velocity. The closed-form analytical formula of the static buckling criteria, including point load and external pressure, has been firstly established and verified. In addition, unprecedented parametric analyses of collision show that the dynamic buckling force (peak force), mean force, and dynamic force redistribution (skewness) during collisions are proportional to the velocity, thickness, elastic modulus, and added mass of the spherical shell structure. These linear relationships are independent of other parameters. Furthermore, it can be found that the max force during the collision is about 2.1 times that of the static buckling force calculated from the analytical formula. These novel insights can help structural engineers and designers determine whether buckling will happen in the application of submarines, subsea exploration, underwater domes, etc

A unified framework for trajectory planning, threat assessment, and semi-autonomous control of passenger vehicles

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2009.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 111-117).This thesis describes the design of an active safety framework that performs trajectory planning, threat assessment, and semi-autonomous control of passenger vehicles in hazard avoidance scenarios. The vehicle navigation task is formulated as a constrained optimal control problem with the constraints bounding a navigable region of the environment derived from forward -looking sensors. First, a constrained model predictive controller is designed to iteratively plan an optimal or "best-case" vehicle trajectory through the constrained corridor. This "best-case" scenario is then used to establish the minimum threat posed to the vehicle given its current state and driver inputs. Based on this threat assessment, the level of controller intervention required to prevent departure from the navigable corridor is calculated and driver/controller inputs are scaled accordingly. This approach minimizes controller intervention while ensuring that the vehicle does not depart from a navigable corridor. It also provides a unified architecture into which various vehicle models, actuation modes, trajectory-planning objectives, driver preferences, and levels of autonomy can be seamlessly integrated without changing the underlying controller structure. Simulated and experimental results are presented to demonstrate the framework's ability to incorporate multiple threat metrics and configurable intervention laws while sharing control with a human driver. Various maneuvers are tested, including lane-keeping, hazard avoidance, and multiple hazard avoidance and show that this framework capable of maintaining vehicle stability while semi-autonomously avoiding road hazards and conceding significant control to the human driver.by Sterling J. Anderson.S.M

Constraint-based navigation for safe, shared control of ground vehicles

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (p. 138-147).Human error in machine operation is common and costly. This thesis introduces, develops, and experimentally demonstrates a new paradigm for shared-adaptive control of human-machine systems that mitigates the effects of human error without removing humans from the control loop. Motivated by observed human proclivity toward navigation in fields of safe travel rather than along specific trajectories, the planning and control framework developed in this thesis is rooted in the design and enforcement of constraints rather than the more traditional use of reference paths. Two constraint-planning methods are introduced. The first uses a constrained Delaunay triangulation of the environment to identify, cumulatively evaluate, and succinctly circumscribe the paths belonging to a particular homotopy with a set of semi autonomously enforceable constraints on the vehicle's position. The second identifies a desired homotopy by planning - and then laterally expanding - the optimal path that traverses it. Simulated results show both of these constraint-planning methods capable of improving the performance of one or multiple agents traversing an environment with obstacles. A method for predicting the threat posed to the vehicle given the current driver action, present state of the environment, and modeled vehicle dynamics is also presented. This threat assessment method, and the shared control approach it facilitates, are shown in simulation to prevent constraint violation or vehicular loss of control with minimal control intervention. Visual and haptic driver feedback mechanisms facilitated by this constraint-based control and threat-based intervention are also introduced. Finally, a large-scale, repeated measures study is presented to evaluate this control framework's effect on the performance, confidence, and cognitive workload of 20 drivers teleoperating an unmanned ground vehicle through an outdoor obstacle course. In 1,200 trials, the constraint-based framework developed in this thesis is shown to increase vehicle velocity by 26% while reducing the occurrence of collisions by 78%, improving driver reaction time to a secondary task by 8.7%, and increasing overall user confidence and sense of control by 44% and 12%, respectively. These performance improvements were realized with the autonomous controller usurping less than 43% of available vehicle control authority, on average.by Sterling J. Anderson.Ph.D