Robust hybrid control for autonomous vehicle motion planning

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2001.Includes bibliographical references (p. 141-150).This dissertation focuses on the problem of motion planning for agile autonomous vehicles. In realistic situations, the motion planning problem must be solved in real-time, in a dynamic and uncertain environment. The fulfillment of the mission objectives might also require the exploitation of the full maneuvering capabilities of the vehicle. The main contribution of the dissertation is the development of a new computational and modelling framework (the Maneuver Automaton), and related algorithms, for steering underactuated, nonholonomic mechanical systems. The proposed approach is based on a quantization of the system's dynamics, by which the feasible nominal system trajectories are restricted to the family of curves that can be obtained by the interconnection of suitably defined primitives. This can be seen as a formalization of the concept of "maneuver", allowing for the construction of a framework amenable to mathematical programming. This motion planning framework is applicable to all time-invariant dynamical systems which admit dynamic symmetries and relative equilibria. No other assumptions are made on the dynamics, thus resulting in exact motion planning techniques of general applicability. Building on a relatively expensive off-line computation phase, we provide algorithms viable for real-time applications. A fundamental advantage of this approach is the ability to provide a mathematical foundation for generating a provably stable and consistent hierarchical system, and for developing the tools to analyze the robustness of the system in the presence of uncertainty and/or disturbances.(cont.) In the second part of the dissertation, a randomized algorithm is proposed for real-time motion planning in a dynamic environment. By employing the optimal control solution in a free space developed for the maneuver automaton (or for any other general system), we present a motion planning algorithm with probabilistic convergence and performance guarantees, and hard safety guarantees, even in the face of finite computation times. The proposed methodologies are applicable to a very large class of autonomous vehicles: throughout the dissertation, examples, simulation and experimental results are presented and discussed, involving a variety of mechanical systems, ranging from simple academic examples and laboratory setups, to detailed models of small autonomous helicopters.by Emilio Frazzoli.Ph.D

Sample-based motion planning in high-dimensional and differentially-constrained systems

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student submitted PDF version of thesis.Includes bibliographical references (p. 115-124).State of the art sample-based path planning algorithms, such as the Rapidly-exploring Random Tree (RRT), have proven to be effective in path planning for systems subject to complex kinematic and geometric constraints. The performance of these algorithms, however, degrade as the dimension of the system increases. Furthermore, sample-based planners rely on distance metrics which do not work well when the system has differential constraints. Such constraints are particularly challenging in systems with non-holonomic and underactuated dynamics. This thesis develops two intelligent sampling strategies to help guide the search process. To reduce sensitivity to dimension, sampling can be done in a low-dimensional task space rather than in the high-dimensional state space. Altering the sampling strategy in this way creates a Voronoi Bias in task space, which helps to guide the search, while the RRT continues to verify trajectory feasibility in the full state space. Fast path planning is demonstrated using this approach on a 1500-link manipulator. To enable task-space biasing for underactuated systems, a hierarchical task space controller is developed by utilizing partial feedback linearization. Another sampling strategy is also presented, where the local reachability of the tree is approximated, and used to bias the search, for systems subject to differential constraints. Reachability guidance is shown to improve search performance of the RRT by an order of magnitude when planning on a pendulum and non-holonomic car. The ideas of task-space biasing and reachability guidance are then combined for demonstration of a motion planning algorithm implemented on LittleDog, a quadruped robot. The motion planning algorithm successfully planned bounding trajectories over extremely rough terrain.by Alexander C. Shkolnik.Ph.D

Mobile robots and vehicles motion systems: a unifying framework

Robots perform many different activities in order to accomplish their tasks. The robot motion capability is one of the most important ones for an autonomous be- havior in a typical indoor-outdoor mission (without it other tasks can not be done), since it drastically determines the global success of a robotic mission. In this thesis, we focus on the main methods for mobile robot and vehicle motion systems and we build a common framework, where similar components can be interchanged or even used together in order to increase the whole system performance

Behaviour-driven motion synthesis

Heightened demand for alternatives to human exposure to strenuous and repetitive labour, as well as to hazardous environments, has led to an increased interest in real-world deployment of robotic agents. Targeted applications require robots to be adept at synthesising complex motions rapidly across a wide range of tasks and environments. To this end, this thesis proposes leveraging abstractions of the problem at hand to ease and speed up the solving. We formalise abstractions to hint relevant robotic behaviour to a family of planning problems, and integrate them tightly into the motion synthesis process to make real-world deployment in complex environments practical. We investigate three principal challenges of this proposition. Firstly, we argue that behavioural samples in form of trajectories are of particular interest to guide robotic motion synthesis. We formalise a framework with behavioural semantic annotation that enables the storage and bootstrap of sets of problem-relevant trajectories. Secondly, in the core of this thesis, we study strategies to exploit behavioural samples in task instantiations that differ significantly from those stored in the framework. We present two novel strategies to efficiently leverage offline-computed problem behavioural samples: (i) online modulation based on geometry-tuned potential fields, and (ii) experience-guided exploration based on trajectory segmentation and malleability. Thirdly, we demonstrate that behavioural hints can be extracted on-the-fly to tackle highlyconstrained, ever-changing complex problems, from which there is no prior knowledge. We propose a multi-layer planner that first solves a simplified version of the problem at hand, to then inform the search for a solution in the constrained space. Our contributions on efficient motion synthesis via behaviour guidance augment the robots’ capabilities to deal with more complex planning problems, and do so more effectively than related approaches in the literature by computing better quality paths in lower response time. We demonstrate our contributions, in both laboratory experiments and field trials, on a spectrum of planning problems and robotic platforms ranging from high-dimensional humanoids and robotic arms with a focus on autonomous manipulation in resembling environments, to high-dimensional kinematic motion planning with a focus on autonomous safe navigation in unknown environments. While this thesis was motivated by challenges on motion synthesis, we have explored the applicability of our findings on disparate robotic fields, such as grasp and task planning. We have made some of our contributions open-source hoping they will be of use to the robotics community at large.The CDT in Robotics and Autonomous Systems at Heriot-Watt University and The University of EdinburghThe ORCA Hub EPSRC project (EP/R026173/1)The Scottish Informatics and Computer Science Alliance (SICSA

A COLLISION AVOIDANCE SYSTEM FOR AUTONOMOUS UNDERWATER VEHICLES

The work in this thesis is concerned with the development of a novel and practical collision avoidance system for autonomous underwater vehicles (AUVs). Synergistically, advanced stochastic motion planning methods, dynamics quantisation approaches, multivariable tracking controller designs, sonar data processing and workspace representation, are combined to enhance significantly the survivability of modern AUVs. The recent proliferation of autonomous AUV deployments for various missions such as seafloor surveying, scientific data gathering and mine hunting has demanded a substantial increase in vehicle autonomy. One matching requirement of such missions is to allow all the AUV to navigate safely in a dynamic and unstructured environment. Therefore, it is vital that a robust and effective collision avoidance system should be forthcoming in order to preserve the structural integrity of the vehicle whilst simultaneously increasing its autonomy. This thesis not only provides a holistic framework but also an arsenal of computational techniques in the design of a collision avoidance system for AUVs. The design of an obstacle avoidance system is first addressed. The core paradigm is the application of the Rapidly-exploring Random Tree (RRT) algorithm and the newly developed version for use as a motion planning tool. Later, this technique is merged with the Manoeuvre Automaton (MA) representation to address the inherent disadvantages of the RRT. A novel multi-node version which can also address time varying final state is suggested. Clearly, the reference trajectory generated by the aforementioned embedded planner must be tracked. Hence, the feasibility of employing the linear quadratic regulator (LQG) and the nonlinear kinematic based state-dependent Ricatti equation (SDRE) controller as trajectory trackers are explored. The obstacle detection module, which comprises of sonar processing and workspace representation submodules, is developed and tested on actual sonar data acquired in a sea-trial via a prototype forward looking sonar (AT500). The sonar processing techniques applied are fundamentally derived from the image processing perspective. Likewise, a novel occupancy grid using nonlinear function is proposed for the workspace representation of the AUV. Results are presented that demonstrate the ability of an AUV to navigate a complex environment. To the author's knowledge, it is the first time the above newly developed methodologies have been applied to an A UV collision avoidance system, and, therefore, it is considered that the work constitutes a contribution of knowledge in this area of work.J&S MARINE LT

Data-Driven Methods for Geometric Systems

The tools of geometric mechanics provide a compact representation of locomotion dynamics as ``the reconstruction equation''. We have found this equation yields a convenient form for estimating models directly from observation data. This convenience draws from the method's relatively rare feature of providing high accuracy models with little effort. By little effort, we point to the modeling process's low data requirements and the property that nothing about the implementation changes when substituting robot kinematics, material properties, or environmental conditions, as long as some intuitive baseline features of the dynamics are shared. We have applied data-driven geometric mechanics models toward optimizing robot behaviors both physical and simulated, exploring robots' ability to recover from injury, and efficiently creating libraries of maneuvers to be used as building blocks for higher-level robot tasks. Our methods employed the tools of data-driven Floquet analysis, providing a phase that we used as a means of grouping related measurements, allowing us to estimate a reconstruction equation model as a function of phase in the neighborhood of an observed behavior. This tool allowed us to build models at unanticipated scales of complexity and speed. Our use of a perturbation expansion for the geometric terms led to an improved estimation procedure for highly damped systems containing nontrivial but non-dominating amounts of momentum. Analysis of the role of passivity in dissipative systems led to another extension of the estimation procedure to robots with high degrees of underactuation in their internal shape, such as soft robots. This thesis will cover these findings and results, simulated and physical, and the surprising practicality of data-driven geometric mechanics.PHDRoboticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/168033/1/babitt_1.pd

On Providing Efficient Real-Time Solutions to Motion Planning Problems of High Complexity

The holy grail of robotics is producing robotic systems capable of efficiently executing all the tasks that are hard, or even impossible, for humans. Humans, undoubtedly, from both a hardware and software perspective, are extremely complex systems capable of executing many complicated tasks. Thus, the complexity of many state-of-the-art robotic systems is also expected to progressively increase, with the goal to match or even surpass human abilities. Recent developments have emphasized mostly hardware, providing highly complex robots with exceptional capabilities. On the other hand, they have illustrated that one important bottleneck of realizing such systems as a common reality is real-time motion planning. This thesis aims to assist the development of complex robotic systems from a computational perspective. The primary focus is developing novel methodologies to address real-time motion planning that enables the robots to accomplish their goals safely and provide the building blocks for developing robust advanced robot behavior in the future. The proposed methods utilize and enhance state-of-the-art approaches to overcome three different types of complexity: 1. Motion planning for high-dimensional systems. RRT+, a new family of general sampling-based planners, was introduced to accelerate solving the motion planning problem for robotic systems with many degrees of freedom by iteratively searching in lowerdimensional subspaces of increasing dimension. RRT+ variants computed solutions orders of magnitude faster compared to state-of-the-art planners. Experiments in simulation of kinematic chains up to 50 degrees of freedom, and the Baxter humanoid robot validate the effectiveness of the proposed technique. 2. Underwater navigation for robots in cluttered environments. AquaNav, a real-time navigation pipeline for robots moving efficiently in challenging, unknown, and unstructured environments, was developed for Aqua2, a hexapod swimming robot with complex, yet to be fully discovered, dynamics. AquaNav was tested offline in known maps, and online in unknown maps utilizing vision-based SLAM. Rigorous testing in simulation, inpool, and open-water trials show the robustness of the method on providing efficient and safe performance, enabling the robot to navigate by avoiding static and dynamic obstacles in open-water settings with turbidity and surge. 3. Active perception of areas of interest during underwater operation. AquaVis, an extension of AquaNav, is a real-time navigation technique enabling robots, with arbitrary multi-sensor configurations, to safely reach their target, while at the same time observing multiple areas of interest from a desired proximity. Extensive simulations show safe behavior, and strong potential for improving underwater state estimation, monitoring, tracking, inspection, and mapping of objects of interest in the underwater domain, such as coral reefs, shipwrecks, marine life, and human infrastructure

Motion planning and reactive control on learnt skill manifolds

We propose a novel framework for motion planning and control that is based on a manifold encoding of the desired solution set. We present an alternate, model-free, approach to path planning, replanning and control. Our approach is founded on the idea of encoding the set of possible trajectories as a skill manifold, which can be learnt from data such as from demonstration. We describe the manifold representation of skills, a technique for learning from data and a method for generating trajectories as geodesics on such manifolds. We extend the trajectory generation method to handle dynamic obstacles and constraints. We show how a state metric naturally arises from the manifold encoding and how this can be used for reactive control in an on-line manner. Our framework tightly integrates learning, planning and control in a computationally efficient representation, suitable for realistic humanoid robotic tasks that are defined by skill specifications involving high-dimensional nonlinear dynamics, kinodynamic constraints and non-trivial cost functions, in an optimal control setting. Although, in principle, such problems can be handled by well understood analytical methods, it is often difficult and expensive to formulate models that enable the analytical approach. We test our framework with various types of robotic systems – ranging from a 3-link arm to a small humanoid robot – and show that the manifold encoding gives significant improvements in performance without loss of accuracy. Furthermore, we evaluate the framework against a state-of-the-art imitation learning method. We show that our approach, by learning manifolds of robotic skills, allows for efficient planning and replanning in changing environments, and for robust and online reactive control

Cooperative control for multi-vehicle swarms

The cooperative control of large-scale multi-agent systems has gained a significant interest in recent years from the robotics and control communities for multi-vehicle control. One motivator for the growing interest is the application of spatially and temporally distributed multiple unmanned aerial vehicle (UAV) systems for distributed sensing and collaborative operations. In this research, the multi-vehicle control problem is addressed using a decentralised control system. The work aims to provide a decentralised control framework that synthesises the self-organised and coordinated behaviour of natural swarming systems into cooperative UAV systems. The control system design framework is generalised for application into various other multi-agent systems including cellular robotics, ad-hoc communication networks, and modular smart-structures. The approach involves identifying su itable relationships that describe the behaviour of the UAVs within the swarm and the interactions of these behaviours to produce purposeful high-level actions for system operators. A major focus concerning the research involves the development of suitable analytical tools that decomposes the general swarm behaviours to the local vehicle level. The control problem is approached using two-levels of abstraction; the supervisory level, and the local vehicle level. Geometric control techniques based on differential geometry are used at the supervisory level to reduce the control problem to a small set of permutation and size invariant abstract descriptors. The abstract descriptors provide an open-loop optimal state and control trajectory for the collective swarm and are used to describe the intentions of the vehicles. Decentralised optimal control is implemented at the local vehicle level to synthesise self-organised and cooperative behaviour. A deliberative control scheme is implemented at the local vehicle le vel that demonstrates autonomous, cooperative and optimal behaviour whilst the preserving precision and reliability at the local vehicle level

Integrated motion planning and model learning for mobile robots with application to marine vehicles

Thesis (Ph. D.)--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.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (p. 269-275).Robust motion planning algorithms for mobile robots consider stochasticity in the dynamic model of the vehicle and the environment. A practical robust planning approach balances the duration of the motion plan with the probability of colliding with obstacles. This thesis develops fast analytic algorithms for predicting the collision probability due to model uncertainty and random disturbances in the environment for a planar holonomic vehicle such as a marine surface vessel. These predictions lead to a robust motion planning algorithm that nds the optimal motion plan quickly and efficiently. By incorporating model learning into the predictions, the integrated algorithm exhibits emergent active learning strategies to autonomously acquire the model data needed to safely and eectively complete the mission. The motion planner constructs plans through a known environment by concatenating maneuvers based upon speed controller setpoints. A model-based feedforward/ feedback controller is used to track the resulting reference trajectory, and the model parameters are learned online with a least squares regression algorithm. The path-following performance of the vehicle depends on the effects of unknown environmental disturbances and modeling error. The convergence rate of the parameter estimates depends on the motion plan, as different plans excite different modes of the system.(cont.) By predicting how the collision probability is affected by the parameter covariance evolution, the motion planner automatically incorporates active learning strategies into the motion plans. In particular, the vehicle will practice maneuvers in the open regions of the configuration space before using them in the constrained regions to ensure that the collision risk due to modeling error is low. High-level feedback across missions allows the system to recognize configuration changes and quickly learn new model parameters as necessary. Simulations and experimental results using an autonomous marine surface vessel are presented.by Matthew Greytak.Ph.D