System Design, Motion Modelling and Planning for a Recon figurable Wheeled Mobile Robot

Over the past ve decades the use of mobile robotic rovers to perform in-situ scienti c investigations on the surfaces of the Moon and Mars has been tremendously in uential in shaping our understanding of these extraterrestrial environments. As robotic missions have evolved there has been a greater desire to explore more unstructured terrain. This has exposed mobility limitations with conventional rover designs such as getting stuck in soft soil or simply not being able to access rugged terrain. Increased mobility and terrain traversability are key requirements when considering designs for next generation planetary rovers. Coupled with these requirements is the need to autonomously navigate unstructured terrain by taking full advantage of increased mobility. To address these issues, a high degree-of-freedom recon gurable platform that is capable of energy intensive legged locomotion in obstacle-rich terrain as well as wheeled locomotion in benign terrain is proposed. The complexities of the planning task that considers the high degree-of-freedom state space of this platform are considerable. A variant of asymptotically optimal sampling-based planners that exploits the presence of dominant sub-spaces within a recon gurable mobile robot's kinematic structure is proposed to increase path quality and ensure platform safety. The contributions of this thesis include: the design and implementation of a highly mobile planetary analogue rover; motion modelling of the platform to enable novel locomotion modes, along with experimental validation of each of these capabilities; the sampling-based HBFMT* planner that hierarchically considers sub-spaces to better guide search of the complete state space; and experimental validation of the planner with the physical platform that demonstrates how the planner exploits the robot's capabilities to uidly transition between various physical geometric con gurations and wheeled/legged locomotion modes

Receding-horizon motion planning of quadrupedal robot locomotion

Quadrupedal robots are designed to offer efficient and robust mobility on uneven terrain. This thesis investigates combining numerical optimization and machine learning methods to achieve interpretable kinodynamic planning of natural and agile locomotion. The proposed algorithm, called Receding-Horizon Experience-Controlled Adaptive Legged Locomotion (RHECALL), uses nonlinear programming (NLP) with learned initialization to produce long-horizon, high-fidelity, terrain-aware, whole-body trajectories. RHECALL has been implemented and validated on the ANYbotics ANYmal B and C quadrupeds on complex terrain. The proposed optimal control problem formulation uses the single-rigid-body dynamics (SRBD) model and adopts a direct collocation transcription method which enables the discovery of aperiodic contact sequences. To generate reliable trajectories, we propose fast-to-compute analytical costs that leverage the discretization and terrain-dependent kinematic constraints. To extend the formulation to receding-horizon planning, we propose a segmentation approach with asynchronous centre of mass (COM) and end-effector timings and a heuristic initialization scheme which reuses the previous solution. We integrate real-time 2.5D perception data for online foothold selection. Additionally, we demonstrate that a learned stability criterion can be incorporated into the planning framework. To accelerate the convergence of the NLP solver to locally optimal solutions, we propose data-driven initialization schemes trained using supervised and unsupervised behaviour cloning. We demonstrate the computational advantage of the schemes and the ability to leverage latent space to reconstruct dynamic segments of plans which are several seconds long. Finally, in order to apply RHECALL to quadrupeds with significant leg inertias, we derive the more accurate lump leg single-rigid-body dynamics (LL-SRBD) and centroidal dynamics (CD) models and their first-order partial derivatives. To facilitate intuitive usage of costs, constraints and initializations, we parameterize these models by Euclidean-space variables. We show the models have the ability to shape rotational inertia of the robot which offers potential to further improve agility

Reconfigurable and Agile Legged-Wheeled Robot Navigation in Cluttered Environments with Movable Obstacles

Legged and wheeled locomotion are two standard methods used by robots to perform navigation. Combining them to create a hybrid legged-wheeled locomotion results in increased speed, agility, and reconfigurability for the robot, allowing it to traverse a multitude of environments. The CENTAURO robot has these advantages, but they are accompanied by a higher-dimensional search space for formulating autonomous economical motion plans, especially in cluttered environments. In this article, we first review our previously presented legged-wheeled footprint reconfiguring global planner. We describe the two incremental prototypes, where the primary goal of the algorithms is to reduce the search space of possible footprints such that plans that expand the robot over the low-lying wide obstacles or narrow into passages can be computed with speed and efficiency. The planner also considers the cost of avoiding obstacles versus negotiating them by expanding over them. The second part of this article presents our new work on local obstacle pushing, which further increases the number of tight scenarios the planner can solve. The goal of the new local push-planner is to place any movable obstacle of unknown mass and inertial properties, obstructing the previously planned trajectory from our global planner, to a location devoid of obstruction. This is done while minimising the distance traveled by the robot, the distance the object is pushed, and its rotation caused by the push. Together, the local and global planners form a major part of the agile reconfigurable navigation suite for the legged-wheeled hybrid CENTAURO robot

Learning for Humanoid Multi-Contact Navigation Planning

Humanoids' abilities to navigate uneven terrain make them well-suited for disaster response efforts, but humanoid motion planning in unstructured environments remains a challenging problem. In this dissertation we focus on planning contact sequences for a humanoid robot navigating in large unstructured environments using multi-contact motion, including both foot and palm contacts. In particular, we address the two following questions: (1) How do we efficiently generate a feasible contact sequence? and (2) How do we efficiently generate contact sequences which lead to dynamically-robust motions? For the first question, we propose a library-based method that retrieves motion plans from a library constructed offline, and adapts them with local trajectory optimization to generate the full motion plan from the start to the goal. This approach outperforms a conventional graph search contact planner when it is difficult to decide which contact is preferable with a simplified robot model and local environment information. We also propose a learning approach to estimate the difficulty to traverse a certain region based on the environment features. By integrating the two approaches, we propose a planning framework that uses graph search planner to find contact sequences around easy regions. When it is necessary to go through a difficult region, the framework switches to use the library-based method around the region to find a feasible contact sequence faster. For the second question, we consider dynamic motions in contact planning. Most humanoid motion generators do not optimize the dynamic robustness of a contact sequence. By querying a learned model to predict the dynamic feasibility and robustness of each contact transition from a centroidal dynamics optimizer, the proposed planner efficiently finds contact sequences which lead to dynamically-robust motions. We also propose a learning-based footstep planner which takes external disturbances into account. The planner considers not only the poses of the planned contact sequence, but also alternative contacts near the planned contact sequence that can be used to recover from external disturbances. Neural networks are trained to efficiently predict multi-contact zero-step and one-step capturability, which allows the planner to generate contact sequences robust to external disturbances efficiently.PHDRoboticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/162908/1/linyuchi_1.pd

Robot Localization Using Visual Image Mapping

One critical step in providing the Air Force the capability to explore unknown environments is for an autonomous agent to be able to determine its location. The calculation of the robot\u27s pose is an optimization problem making use of the robot\u27s internal navigation sensors and data fusion of range sensor readings to find the most likely pose. This data fusion process requires the simultaneous generation of a map which the autonomous vehicle can then use to avoid obstacles, communicate with other agents in the same environment, and locate targets. Our solution entails mounting a Class 1 laser to an ERS-7 AIBO. The laser projects a horizontal line on obstacles in the AIBO camera\u27s field of view. Range readings are determined by capturing and processing multiple image frames, resolving the laser line to the horizon, and extract distance information to each obstacle. This range data is then used in conjunction with mapping a localization software to accurately navigate the AIBO

NeBula: Team CoSTAR's robotic autonomy solution that won phase II of DARPA Subterranean Challenge

This paper presents and discusses algorithms, hardware, and software architecture developed by the TEAM CoSTAR (Collaborative SubTerranean Autonomous Robots), competing in the DARPA Subterranean Challenge. Specifically, it presents the techniques utilized within the Tunnel (2019) and Urban (2020) competitions, where CoSTAR achieved second and first place, respectively. We also discuss CoSTAR¿s demonstrations in Martian-analog surface and subsurface (lava tubes) exploration. The paper introduces our autonomy solution, referred to as NeBula (Networked Belief-aware Perceptual Autonomy). NeBula is an uncertainty-aware framework that aims at enabling resilient and modular autonomy solutions by performing reasoning and decision making in the belief space (space of probability distributions over the robot and world states). We discuss various components of the NeBula framework, including (i) geometric and semantic environment mapping, (ii) a multi-modal positioning system, (iii) traversability analysis and local planning, (iv) global motion planning and exploration behavior, (v) risk-aware mission planning, (vi) networking and decentralized reasoning, and (vii) learning-enabled adaptation. We discuss the performance of NeBula on several robot types (e.g., wheeled, legged, flying), in various environments. We discuss the specific results and lessons learned from fielding this solution in the challenging courses of the DARPA Subterranean Challenge competition.The work is partially supported by the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (80NM0018D0004), and Defense Advanced Research Projects Agency (DARPA)

NeBula: TEAM CoSTAR’s robotic autonomy solution that won phase II of DARPA subterranean challenge

This paper presents and discusses algorithms, hardware, and software architecture developed by the TEAM CoSTAR (Collaborative SubTerranean Autonomous Robots), competing in the DARPA Subterranean Challenge. Specifically, it presents the techniques utilized within the Tunnel (2019) and Urban (2020) competitions, where CoSTAR achieved second and first place, respectively. We also discuss CoSTAR’s demonstrations in Martian-analog surface and subsurface (lava tubes) exploration. The paper introduces our autonomy solution, referred to as NeBula (Networked Belief-aware Perceptual Autonomy). NeBula is an uncertainty-aware framework that aims at enabling resilient and modular autonomy solutions by performing reasoning and decision making in the belief space (space of probability distributions over the robot and world states). We discuss various components of the NeBula framework, including (i) geometric and semantic environment mapping, (ii) a multi-modal positioning system, (iii) traversability analysis and local planning, (iv) global motion planning and exploration behavior, (v) risk-aware mission planning, (vi) networking and decentralized reasoning, and (vii) learning-enabled adaptation. We discuss the performance of NeBula on several robot types (e.g., wheeled, legged, flying), in various environments. We discuss the specific results and lessons learned from fielding this solution in the challenging courses of the DARPA Subterranean Challenge competition.Peer ReviewedAgha, A., Otsu, K., Morrell, B., Fan, D. D., Thakker, R., Santamaria-Navarro, A., Kim, S.-K., Bouman, A., Lei, X., Edlund, J., Ginting, M. F., Ebadi, K., Anderson, M., Pailevanian, T., Terry, E., Wolf, M., Tagliabue, A., Vaquero, T. S., Palieri, M., Tepsuporn, S., Chang, Y., Kalantari, A., Chavez, F., Lopez, B., Funabiki, N., Miles, G., Touma, T., Buscicchio, A., Tordesillas, J., Alatur, N., Nash, J., Walsh, W., Jung, S., Lee, H., Kanellakis, C., Mayo, J., Harper, S., Kaufmann, M., Dixit, A., Correa, G. J., Lee, C., Gao, J., Merewether, G., Maldonado-Contreras, J., Salhotra, G., Da Silva, M. S., Ramtoula, B., Fakoorian, S., Hatteland, A., Kim, T., Bartlett, T., Stephens, A., Kim, L., Bergh, C., Heiden, E., Lew, T., Cauligi, A., Heywood, T., Kramer, A., Leopold, H. A., Melikyan, H., Choi, H. C., Daftry, S., Toupet, O., Wee, I., Thakur, A., Feras, M., Beltrame, G., Nikolakopoulos, G., Shim, D., Carlone, L., & Burdick, JPostprint (published version

From visuomotor control to latent space planning for robot manipulation

Deep visuomotor control is emerging as an active research area for robot manipulation. Recent advances in learning sensory and motor systems in an end-to-end manner have achieved remarkable performance across a range of complex tasks. Nevertheless, a few limitations restrict visuomotor control from being more widely adopted as the de facto choice when facing a manipulation task on a real robotic platform. First, imitation learning-based visuomotor control approaches tend to suffer from the inability to recover from an out-of-distribution state caused by compounding errors. Second, the lack of versatility in task definition limits skill generalisability. Finally, the training data acquisition process and domain transfer are often impractical. In this thesis, individual solutions are proposed to address each of these issues. In the first part, we find policy uncertainty to be an effective indicator of potential failure cases, in which the robot is stuck in out-of-distribution states. On this basis, we introduce a novel uncertainty-based approach to detect potential failure cases and a recovery strategy based on action-conditioned uncertainty predictions. Then, we propose to employ visual dynamics approximation to our model architecture to capture the motion of the robot arm instead of the static scene background, making it possible to learn versatile skill primitives. In the second part, taking inspiration from the recent progress in latent space planning, we propose a gradient-based optimisation method operating within the latent space of a deep generative model for motion planning. Our approach bypasses the traditional computational challenges encountered by established planning algorithms, and has the capability to specify novel constraints easily and handle multiple constraints simultaneously. Moreover, the training data comes from simple random motor-babbling of kinematically feasible robot states. Our real-world experiments further illustrate that our latent space planning approach can handle both open and closed-loop planning in challenging environments such as heavily cluttered or dynamic scenes. This leads to the first, to our knowledge, closed-loop motion planning algorithm that can incorporate novel custom constraints, and lays the foundation for more complex manipulation tasks

An Ontology-Based Expert System for the Systematic Design of Humanoid Robots

Die Entwicklung humanoider Roboter ist eine zeitaufwendige, komplexe und herausfordernde Aufgabe. Daher stellt diese Thesis einen neuen, systematischen Ansatz vor, der es erlaubt, Expertenwissen zum Entwurf humanoider Roboter zu konservieren, um damit zukünftige Entwicklungen zu unterstützen. Der Ansatz kann in drei aufeinanderfolgende Schritte unterteilt werden. Im ersten Schritt wird Wissen zum Entwurf humanoider Roboter durch die Entwicklung von Roboterkomponenten und die Analyse verwandter Arbeiten gewonnen. Dieses Wissen wird im zweiten Schritt formalisiert und in Form einer ontologischen Wissensbasis gespeichert. Im letzten Schritt wird diese Wissensbasis von einem Expertensystem verwendet, um Lösungsvorschläge zum Entwurf von Roboterkomponenten auf Grundlage von Benutzeranforderungen zu generieren. Der Ansatz wird anhand von Fallstudien zu Komponenten des humanoiden Roboters ARMAR-6 evaluiert: Sensor-Aktor-Controller-Einheiten für Robotergelenke und Roboterhände

Development of an assisted-teleoperation system for a dual-manipulator nuclear decommissioning robot

This thesis concerns a robotic platform that is being used for research into assisted tele–operation for common nuclear decommissioning tasks, such as remote handling and pipe cutting. The machine consists of dual, seven–function, hydraulically actuated HYDROLEK manipulators mounted (in prior research) on a mobile BROKK base unit. Whilst the original system was operated by remote control, the present thesis focusses on the development of a visual servoing system, in which the user selects the object of interest from an on–screen image, whilst the computer control system determines and implements via feedback control the required position and orientation of the manipulators. Novel research contributions are made in three main areas: (i) the development of a detailed mechanistic model of the system; (ii) the development and preliminary testing in the laboratory of the new assisted–teleoperation user interface; and (iii) the development of improved control systems for joint angle set point tracking, and their systematic, quantitative comparison via simulation and experiment. The mechanistic model builds on previous work, while the main novelty in this thesis relates to the hydraulic component of the model, and the development and evaluation of a multi–objective genetic algorithm framework to identify the unknown parameter values. To improve on the joystick direct teleoperation currently used as standard in the nuclear industry, which is slow and requires extensive operator training, the proposed assisted–teleoperation makes use of a camera mounted on the robot. Focussing on pipe cutting as an example, the new system ensures that one manipulator automatically grasps the user–selected pipe, and appropriately positions the second for a cutting operation. Initial laboratory testing (using a plastic pipe) shows the efficacy of the approach for positioning the manipulators, and suggests that for both experienced and inexperienced users, the task is completed significantly faster than via tele-operation. Finally, classical industrial, fuzzy logic, and novel state dependent parameter approaches to control are developed and compared, with the aim being to determine a relatively simple controller that yields good performance for the hydraulic manipulators. An improved, more structured method of dealing with the dead–zone characteristics is developed and implemented, replacing the rather ad hoc approach that had been utilised in previous research for the same machine