Stochastic approaches to mobility prediction, path planning and motion control for ground vehicles in uncertain environments

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2009.Cataloged from PDF version of thesis.Includes bibliographical references (p. 107-111).The ability of autonomous or semi-autonomous unmanned ground vehicles (UGVs) to rapidly and accurately predict terrain negotiability, generate efficient paths online and have effective motion control is a critical requirement for their safety and use in unstructured environments. Most techniques and algorithms for performing these functions, however, assume precise knowledge of vehicle and/or environmental (i.e. terrain) properties. In practical applications, significant uncertainties are associated with the estimation of the vehicle and/or terrain parameters, and these uncertainties must be considered while performing the above tasks. Here, computationally inexpensive methods based on the polynomial chaos approach are studied that consider imprecise knowledge of vehicle and/or terrain parameters while analyzing UGV dynamics and mobility, evaluating safe, traceable paths to be followed and controlling the vehicle motion. Conventional Monte Carlo methods, that are relatively more computationally expensive, are also briefly studied and used as a reference for evaluating the computational efficiency and accuracy of results from the polynomial chaos-based techniques.by Gaurav Kewlani.S.M

Optimal planning and control for hazard avoidance of front-wheel steered ground vehicles

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 124-128).Hazard avoidance is an important capability for safe operation of robotic vehicles at high speed. It is also an important consideration for passenger vehicle safety, as thousands are killed each year in passenger vehicle accidents caused by driver error. Even when hazard locations are known, high-speed hazard avoidance presents challenges in real-time motion planning and control of nonlinear and potentially unstable vehicle dynamics. This thesis presents methods for planning and control of optimal hazard avoidance maneuvers for a bicycle model with front-wheel steering and wheel slip. The planning problem is posed as an optimization problem in which constrained dynamic quantities, such as friction circle utilization, are minimized, while ensuring a minimum clearance from hazards. These optimal trajectories can be computed numerically, though real-time computation requires simple models and constraints. To simplify the computation of optimal avoidance trajectories, analytical solutions to the optimal planning problem are presented for a point mass subject to an acceleration magnitude constraint, which is analogous to a tire friction circle constraint. The optimal point mass solutions are extended to a nonlinear bicycle model by defining a flatness-based trajectory tracking controller using tire force control. This controller decouples the bicycle dynamics into a point mass at the front center of oscillation with an additional degree of freedom related to the vehicle yaw dynamics. Structure is identified in the yaw dynamics and is exploited to characterize stability limits. Simulation results verify the stability properties of the yaw dynamics. These results were applied to a semi-autonomous driver assistance system and demonstrated experimentally on a full-sized passenger vehicle. Efficient computation of point mass avoidance maneuvers was used as a cost-to-go for real-time numerical optimization of trajectories for a bicycle model. The experimental system switches control authority between the driver and an automatic avoidance controller so that the driver retains control authority in benign situations, and the automatic controller avoids hazards automatically in hazardous situations.by Steven C. Peters.Ph.D

Aerial Vehicles

This book contains 35 chapters written by experts in developing techniques for making aerial vehicles more intelligent, more reliable, more flexible in use, and safer in operation.It will also serve as an inspiration for further improvement of the design and application of aeral vehicles. The advanced techniques and research described here may also be applicable to other high-tech areas such as robotics, avionics, vetronics, and space

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

Proceedings of the International Micro Air Vehicles Conference and Flight Competition 2017 (IMAV 2017)

The IMAV 2017 conference has been held at ISAE-SUPAERO, Toulouse, France from Sept. 18 to Sept. 21, 2017. More than 250 participants coming from 30 different countries worldwide have presented their latest research activities in the field of drones. 38 papers have been presented during the conference including various topics such as Aerodynamics, Aeroacoustics, Propulsion, Autopilots, Sensors, Communication systems, Mission planning techniques, Artificial Intelligence, Human-machine cooperation as applied to drones

Haptic Steering Interfaces for Semi-Autonomous Vehicles

Autonomous vehicles are predicted to significantly improve transportation quality by reducing traffic congestion, fuel expenditure and road accidents. However, until autonomous vehicles are reliable in all scenarios, human drivers will be asked to supervise automation behavior and intervene in automated driving when deemed necessary. Retaining the human driver in a strictly supervisory role, however, may make the driver complacent and reduce driver's situation awareness and driving skills which ironically, can further compromise the driver’s ability to intervene in safety-critical scenarios. Such issues can be alleviated by designing a human-automation interface that keeps the driver in-the-loop through constant interaction with automation and continuous feedback of automation's actions. This dissertation evaluates the utility of haptic feedback at the steering interface for enhancing driver awareness and enabling continuous human-automation interaction and performance improvement in semi-autonomous vehicles. In the first part of this dissertation, I investigate a driving scheme called Haptic Shared Control (HSC) in which the human driver and automation system share the steering control by simultaneously acting at the steering interface with finite mechanical impedances. I hypothesize that HSC can mitigate the human factors issues associated with semi-autonomous driving by allowing the human driver to continuously interact with automation and receive feedback about automation action. To test this hypothesis, I present two driving simulator experiments that are focused on the evaluation of HSC with respect to existing driving schemes during induced human and automation faults. In the first experiment, I compare obstacle avoidance performance of HSC with two existing control sharing schemes that support instantaneous transfers of control authority between human and automation. The results indicate that HSC outperforms both schemes in terms of obstacle avoidance, maneuvering efficiency, and driver engagement. In the second experiment, I consider emergency scenarios where I compare two HSC designs that provide high and low control authority to automation and an existing paradigm that decouples the driver input from the tires during collision avoidance. Results show that decoupling the driver invokes out-of-the-loop issues and misleads drivers to believe that they are in control. I also discover a `fault protection tradeoff': as the control authority provided to one agent increases, the protection against that agent's faults provided by the other agent reduces. In the second part of this dissertation, I focus on the problem of estimating haptic feedback from the road, or the road feedback. Road feedback is critical to making the driver aware of the state of the vehicle and road conditions, and its estimates are used in a variety of driver assist systems. However, conventional estimators only estimate road feedback on flat roads. To overcome this issue, I develop three estimators that enable road feedback estimation on uneven roads. I test and compare the performance of the three estimators by performing driving experiments on uneven roads such as road slopes and cleats. In the final part of this dissertation, I shift focus from physical human-automation interaction to human-human interaction. I present the evidence from the literature demonstrating that haptic feedback improves the performance of two humans physically collaborating on a shared task. I develop a control-theoretic model for haptic communication that can describe the mechanism by which haptic interaction facilitates performance improvement. The model creates a promising means to transfer the obtained insights to design robots or automation systems that can collaborate more efficiently with humans.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169975/1/akshaybh_1.pd

Onboard Localization of an Unmanned Aerial Vehicle in an Unknown Environment

Tato práce se zabývá onboard lokalizací bezpilotního letounu bez přístupu k službám globálních navigačhních systémů. Hlavní cíl této práce spočívá v návrhu a implementaci metody pro simultánní lokalizaci a mapování, která využívá laserové skeny z rotačního laserového dálkoměru k odhadování pozice letounu. Byla implementována technika pro odhadování posunutí a rotace mezi dvěma laserovými skeny pomocí zarovnání korespondujících měření ze zmíněných laserových skenů. Navržené řešení zahrnuje fúzi odhadu pozice z inercialní měřicí jednotky, relativní posunutí získané ze zarovnání po sobě jdoucích skenů a absolutní pozice získané ze zarovnání laserových skenů do postupně stavěné mapy. Fúzovaný odhad pozice uzavírá vnější zpětnovazební smyčku prediktivního řízení. Vyvinutý systém je nejprve posouzen v simulaci a poté jsou jeho schopnosti předvedeny na sadě hardwarových experimentů s reálným dronem.This thesis is concerned with onboard localization of an unmanned aerial vehicle without the access to global navigation satellite system services. The central focus of this work lies in design and implementation of simultaneous localization and mapping method that uses laser scans from a rotating laser rangefinder to estimate the position of the vehicle. A scan matching technique was implemented to estimate the displacement and rotation between two laser scans by aligning corresponding measurements from the two scans. The proposed solution involves fusion of position estimate from the inertial measurement unit, the relative displacement obtained by aligning successive laser scans, and the absolute position obtained by aligning laser scans into the gradually built map. The fused position estimate closes the outer feedback loop of the model predictive control. The developed system is first evaluated in simulations, and then its capabilities are demonstrated on a set of hardware experiments with a real drone

Sensitivity Study for UAV GPS-Denied Navigation in Uncertain Landmark Fields

This document provides two 2D simulation sensitivity analyses regarding a drone’s flight characteristic (state) errors within a GPS-denied region. The research focuses on a development and investigation of utilizing a camera to simultaneously determine a drone’s state while locating landmarks, where there is uncertainty in the landmarks’ exact positions prior to the mission (SLAM). This SLAM method is performed in regions with limited access to GPS. Furthermore, there is development and investigation of controlling the drone in conjunction with SLAM using potential error-reducing control parameters. Objectives are to quantitatively understand the UAV’s sensitivity of position errors to sensor grade and landmark characteristics as well as sensitivity of position errors to tuned control parameters

DEVELOPMENT AND EVALUATION OF AN ADVANCED REAL-TIME ELECTRICAL POWERED WHEELCHAIR CONTROLLER

Advances in Electric Powered Wheelchairs (EPW) have improved mobility for people with disabilities as well as older adults, and have enhanced their integration into society. Some of the issues still present in EPW lie in the difficulties when encountering different types of terrain, and access to higher or low surfaces. To this end, an advanced real-time electrical powered wheelchair controller was developed. The controller was comprised of a hardware platform with sensors measuring the speed of the driving, caster wheels and the acceleration, with a single board computer for implementing the control algorithms in real-time, a multi-layer software architecture, and modular design. A model based real-time speed and traction controller was developed and validated by simulation. The controller was then evaluated via driving over four different surfaces at three specified speeds. Experimental results showed that model based control performed best on all surfaces across the speeds compared to PID (proportional-integral-derivative) and Open Loop control. A real-time slip detection and traction control algorithm was further developed and evaluated by driving the EPW over five different surfaces at three speeds. Results showed that the performance of anti-slip control was consistent on the varying surfaces at different speeds. The controller was also tested on a front wheel drive EPW to evaluate a forwarding tipping detection and prevention algorithm. Experimental results showed that the tipping could be accurately detected as it was happening and the performance of the tipping prevention strategy was consistent on the slope across different speeds. A terrain-dependent EPW user assistance system was developed based on the controller. Driving rules for wet tile, gravel, slopes and grass were developed and validated by 10 people without physical disabilities. The controller was also adapted to the Personal Mobility and Manipulation Appliance (PerMMA) Generation II, which is an advanced power wheelchair with a flexible mobile base, allowing it to adjust the positions of each of the four casters and two driving wheels. Simulations of the PerMMA Gen II system showed that the mobile base controller was able to climb up to 8” curb and maintain passenger’s posture in a comfort position