Unifying model-based programming and randomized path planning through optimal search

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.Includes bibliographical references (p. 120-122).The deployment of robots at the World Trade Center (WTC) site after September 11, 2001, highlighted the potential for robots to aid in search and rescue missions that pose great threats and challenges to humans. However, robots that are tele-operated and tethered for power and communication are restricted in terms of their operational area. Thus, rescue robots must be equipped with onboard autonomy that enables them to select feasible plans on their own, within their physical and computational limitations. There are three main characteristics that a rescue robot's onboard system must posses. First, the system must be able to generate plans for mobile systems, that is, plans with activities and paths. Second, in order to operate as efficiently as possible, particularly in emergency situations, the system must be globally optimal. Third, the system must be able to generate plans quickly. This thesis introduces a novel autonomous control system that interleaves methods for spatial and activity planning, by merging model-based programming with roadmap-based path planning. The primary contributions are threefold. The first contribution is a model that represents possible mission strategies with activities that have cost and are constrained to a location. The second is an optimal pre-planner that reasons through the possible mission strategies in order to quickly find the optimal feasible strategy. The third contribution is a unified, global activity and path planning system. The system unifies the optimal pre-planner with a randomized roadmap-based path planner, in order to find the optimal feasible strategy to achieve a mission. The impact of these contributions is highlighted in the context of an urban search and rescue (USAR) mission.by Aisha Walcott.S.M

Cooperative Navigation for Low-bandwidth Mobile Acoustic Networks.

This thesis reports on the design and validation of estimation and planning algorithms for underwater vehicle cooperative localization. While attitude and depth are easily instrumented with bounded-error, autonomous underwater vehicles (AUVs) have no internal sensor that directly observes XY position. The global positioning system (GPS) and other radio-based navigation techniques are not available because of the strong attenuation of electromagnetic signals in seawater. The navigation algorithms presented herein fuse local body-frame rate and attitude measurements with range observations between vehicles within a decentralized architecture. The acoustic communication channel is both unreliable and low bandwidth, precluding many state-of-the-art terrestrial cooperative navigation algorithms. We exploit the underlying structure of a post-process centralized estimator in order to derive two real-time decentralized estimation frameworks. First, the origin state method enables a client vehicle to exactly reproduce the corresponding centralized estimate within a server-to-client vehicle network. Second, a graph-based navigation framework produces an approximate reconstruction of the centralized estimate onboard each vehicle. Finally, we present a method to plan a locally optimal server path to localize a client vehicle along a desired nominal trajectory. The planning algorithm introduces a probabilistic channel model into prior Gaussian belief space planning frameworks. In summary, cooperative localization reduces XY position error growth within underwater vehicle networks. Moreover, these methods remove the reliance on static beacon networks, which do not scale to large vehicle networks and limit the range of operations. Each proposed localization algorithm was validated in full-scale AUV field trials. The planning framework was evaluated through numerical simulation.PhDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113428/1/jmwalls_1.pd

Learning deep policies for physics-based robotic manipulation in cluttered real-world environments

This thesis presents a series of planners and learning algorithms for real-world manipulation in clutter. The focus is on interleaving real-world execution with look-ahead planning in simulation as an effective way to address the uncertainty arising from complex physics interactions and occlusions. We introduce VisualRHP, a receding horizon planner in the image space guided by a learned heuristic. VisualRHP generates, in closed-loop, prehensile and non-prehensile manipulation actions to manipulate a desired object in clutter while avoiding dropping obstacle objects off the edge of the manipulation surface. To acquire the heuristic of VisualRHP, we develop deep imitation learning and deep reinforcement learning algorithms specifically tailored for environments with complex dynamics and requiring long-term sequential decision making. The learned heuristic ensures generalization over different environment settings and transferability of manipulation skills to different desired objects in the real world. In the second part of this thesis, we integrate VisualRHP with a learnable object pose estimator to guide the search for an occluded desired object. This hybrid approach harnesses neural networks with convolution and recurrent structures to capture relevant information from the history of partial observation to guide VisualRHP future actions. We run an ablation study over the different component of VisualRHP and compare it with model-free and model-based alternatives. We run experiments in different simulation environments and real-world settings. The results show that by trading a small computation time for heuristic-guided look-ahead planning, VisualRHP delivers a more robust and efficient behaviour compared to alternative state-of-the-art approaches while still operating in near real-time

A Smart Products Lifecycle Management (sPLM) Framework - Modeling for Conceptualization, Interoperability, and Modularity

Autonomy and intelligence have been built into many of today’s mechatronic products, taking advantage of low-cost sensors and advanced data analytics technologies. Design of product intelligence (enabled by analytics capabilities) is no longer a trivial or additional option for the product development. The objective of this research is aimed at addressing the challenges raised by the new data-driven design paradigm for smart products development, in which the product itself and the smartness require to be carefully co-constructed. A smart product can be seen as specific compositions and configurations of its physical components to form the body, its analytics models to implement the intelligence, evolving along its lifecycle stages. Based on this view, the contribution of this research is to expand the “Product Lifecycle Management (PLM)” concept traditionally for physical products to data-based products. As a result, a Smart Products Lifecycle Management (sPLM) framework is conceptualized based on a high-dimensional Smart Product Hypercube (sPH) representation and decomposition. First, the sPLM addresses the interoperability issues by developing a Smart Component data model to uniformly represent and compose physical component models created by engineers and analytics models created by data scientists. Second, the sPLM implements an NPD3 process model that incorporates formal data analytics process into the new product development (NPD) process model, in order to support the transdisciplinary information flows and team interactions between engineers and data scientists. Third, the sPLM addresses the issues related to product definition, modular design, product configuration, and lifecycle management of analytics models, by adapting the theoretical frameworks and methods for traditional product design and development. An sPLM proof-of-concept platform had been implemented for validation of the concepts and methodologies developed throughout the research work. The sPLM platform provides a shared data repository to manage the product-, process-, and configuration-related knowledge for smart products development. It also provides a collaborative environment to facilitate transdisciplinary collaboration between product engineers and data scientists

Exploration, navigation and localization for mobile robots.

he main goal of this thesis is the advancement of the state of the art in mobile robot autonomy. In order to achieve this objective, several contributions have been presented that tackle well defined problems in the areas of localization, navigation and exploration. The very first contribution is focused on the task of robustly finding the localization of a mobile robot in an outdoor environment. Specifically, the presented technique introduces a key methodolgy to perform sensor fusion of a global localization sensor so ubiquitous as a GPS device, within the context of a particle filter based Monte Carlo localization system. We focus on the management of multiple sensor data sources under noisy and conflicting readings. This strategy allows for a reduced uncertainty in the robot pose estimation, as well as improved robustness of the system. The second contribution presents a completely integrated navigation system running within a constrained and highly dynamic platform like a quadrotor, applied to full 3D environments. The navigation stack comprises a Simultaneous Localization and Mapping (SLAM) system for RGB-D cameras that provides both the robot pose and an obstacle map of the environment, as well as a 4D path planner capable of finding obstacle free and kinematically feasible trajectories for the quadrotor to navigate this environment. The third contribution introduces a novel approach for autonomous exploration of unknown environments with robust homing. We present a technique to predict possible environment structures in the unseen parts of the robot's surroundings based on previously explored environments. We exploit this belief to predict possible loop closures that the robot may experience when exploring an unknown part of the scene. This allows the robot to actively reduce the uncertainty in its belief through its exploration actions. Also, we introduce a robust homing system that addresses the problem of returning a robot operating in an unknown environment to its starting position even if the underlying SLAM system fails. All contributions where designed, implemented and tested on real autonomous robots: a self-driving car, a micro aerial vehicle and an underground exploration platform

A generalized label correcting method for optimal kinodynamic motion planning

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2017.Cataloged from PDF version of thesis.Includes bibliographical references and index.Nearly all autonomous robotic systems use some form of motion planning to compute reference motions through their environment. An increasing use of autonomous robots in a broad range of applications creates a need for efficient, general purpose motion planning algorithms that are applicable in any of these new application domains. This thesis presents a resolution complete optimal kinodynamic motion planning algorithm based on a direct forward search of the set of admissible input signals to a dynamical model. The advantage of this generalized label correcting method is that it does not require a local planning subroutine as in the case of related methods. Preliminary material focuses on new topological properties of the canonical problem formulation that are used to show continuity of the performance objective. These observations are used to derive a generalization of Bellman's principle of optimality in the context of kinodynamic motion planning. A generalized label correcting algorithm is then proposed which leverages these results to prune candidate input signals from the search when their cost is greater than related signals. The second part of this thesis addresses admissible heuristics for kinodynamic motion planning. An admissibility condition is derived that can be used to verify the admissibility of candidate heuristics for a particular problem. This condition also characterizes a convex set of admissible heuristics. A linear program is formulated to obtain a heuristic which is as close to the optimal cost-to-go as possible while remaining admissible. This optimization is justified by showing its solution coincides with the solution to the Hamilton-Jacobi-Bellman equation. Lastly, a sum-of-squares relaxation of this infinite-dimensional linear program is proposed for obtaining provably admissible approximate solutions.by Brian A. Paden.Ph. D