Dynamically extending planning models using an ontology

In this paper we couple a deterministic planner with an ontology, in order to adapt to new discoveries during plan execution and to reason about the affordances that are available to the planner as the set of known objects is updated. This allows us to extend the planning agent’s functionality during execution. We use as an example planning for persistent autonomous behaviour in underwater vehicles. Planning in this scenario takes place in a symbolic model of the environment, simulating sequences of possible decisions. Ensuring that the simulation remains robust requires careful matching of the model to the real world, including dynamically updating the model from continuous sensing actions. We describe how our system constructs an initial state for planning, using the ontology; how the ontology is also used to determine the results of each action performed by the planner; and finally demonstrate the performance of the system in a simulation, in which two AUVs are required to cooperate in an unknown environment, demonstrating that with additional reasoning the planning system is able to make new efficient choices, taking advantage of the environment in new ways

Dynamically extending planning models using an ontology

In this paper we couple a deterministic planner with an ontology, in order to adapt to new discoveries during plan execution and to reason about the affordances that are available to the planner as the set of known objects is updated. This allows us to extend the planning agent’s functionality during execution. We use as an example planning for persistent autonomous behaviour in underwater vehicles. Planning in this scenario takes place in a symbolic model of the environment, simulating sequences of possible decisions. Ensuring that the simulation remains robust requires careful matching of the model to the real world, including dynamically updating the model from continuous sensing actions. We describe how our system constructs an initial state for planning, using the ontology; how the ontology is also used to determine the results of each action performed by the planner; and finally demonstrate the performance of the system in a simulation, in which two AUVs are required to cooperate in an unknown environment, demonstrating that with additional reasoning the planning system is able to make new efficient choices, taking advantage of the environment in new ways

Multilevel Motion Planning: A Fiber Bundle Formulation

Motion planning problems involving high-dimensional state spaces can often be solved significantly faster by using multilevel abstractions. While there are various ways to formally capture multilevel abstractions, we formulate them in terms of fiber bundles, which allows us to concisely describe and derive novel algorithms in terms of bundle restrictions and bundle sections. Fiber bundles essentially describe lower-dimensional projections of the state space using local product spaces. Given such a structure and a corresponding admissible constraint function, we can develop highly efficient and optimal search-based motion planning methods for high-dimensional state spaces. Our contributions are the following: We first introduce the terminology of fiber bundles, in particular the notion of restrictions and sections. Second, we use the notion of restrictions and sections to develop novel multilevel motion planning algorithms, which we call QRRT* and QMP*. We show these algorithms to be probabilistically complete and almost-surely asymptotically optimal. Third, we develop a novel recursive path section method based on an L1 interpolation over path restrictions, which we use to quickly find feasible path sections. And fourth, we evaluate all novel algorithms against all available OMPL algorithms on benchmarks of eight challenging environments ranging from 21 to 100 degrees of freedom, including multiple robots and nonholonomic constraints. Our findings support the efficiency of our novel algorithms and the benefit of exploiting multilevel abstractions using the terminology of fiber bundles.Comment: Submitted to IJR

Topologically-Guided Robotic Information Gathering

Information gathering tasks, such as terrestrial search and rescue, aerial inspection, and marine monitoring, require robotic unmanned systems to make decisions on how to travel within an environment to maximize or minimize a path-dependent information objective function. The distribution of information throughout the environment is the result of various processes, either natural or human-caused, and so this distribution exhibits an underlying structure. Existing information gathering algorithms seek to implicitly exploit this structure by selecting paths which maximize the robot's time in high-value regions. We see an opportunity to improve the performance of robots in these information gathering tasks by explicitly reasoning over the structure of information, allowing robots to plan their information gathering missions more efficiently and effectively. Topological representations provide an elegant way to describe the structure of an environment using descriptors that are defined relative to a set of features in the environment. Since these descriptors are inherently global, they provide a way for robots to reason directly about their paths within the global context of their operational environments. This additional context enables robotic systems to efficiently plan non-myopically. To accomplish this goal, this thesis develops four contributions that allow robotic systems to reason about topological structure in field robotics tasks. The first contribution is a method for formalizing topological path constraints using a Mixed Integer Programming formulation to plan. Our second contribution is a system for exploiting expert-provided domain knowledge to track a topological feature using a team of heterogeneous robots. Both of these contributions provide ways to exploit the existence of topological features in the environment to motivate and constrain information gathering tasks. However, these methods require the features to be defined before planning. While methods to identify features exist for well-constructed indoor environments, they do not extend to the less-structured outdoor environments more common in field robotics applications. Our third and fourth contributions address this problem. The third contribution of this thesis is a hierarchical planning algorithm which identifies hotspot regions in an information function and uses them to construct a high-level planning graph, while the fourth is an algorithm for fitting a Topology-Aware Self-Organizing Map to an information function. The benefits of reasoning about the topology of the information field is demonstrated in simulation and field experiments. By incorporating global context about the information gathering task via topology, our methods are able to plan paths that collect more information than a naïve myopic planner. Furthermore, we are able to produce comparable or superior paths more quickly than state-of-the-art planners that do consider the entire path, such as combinatorial branch and bound algorithms

Computing Energy Optimal Paths in Time-Varying Flows

Autonomous marine vehicles (AMVs) are typically deployed for long periods of time in the ocean to monitor different physical, chemical, and biological processes. Given their limited energy budgets, it makes sense to consider motion plans that leverage the dynamics of the surrounding flow field so as to minimize energy usage for these vehicles. In this paper, we present two graph search based methods to compute energy optimal paths for AMVs in two-dimensional (2-D) time-varying flows. The novelty of the proposed algorithms lies in a unique discrete graph representation of the 3-D configuration space spanned by the spatio-temporal coordinates. This enables a more efficient traversal through the search space, as opposed to a full search of the spatio-temporal configuration space. Furthermore, the proposed strategy results in solutions that are closer to the global optimal when compared to greedy searches through the spatial coordinates alone. We demonstrate the proposed algorithms by computing optimal energy paths around the Channel Islands in the Santa Barbara bay using time-varying flow field forecasts generated by the Regional Ocean Model System. We verify the accuracy of the computed paths by comparing them with paths computed via an optimal control formulation

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

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

Satellite and UAV Platforms, Remote Sensing for Geographic Information Systems

The present book contains ten articles illustrating the different possible uses of UAVs and satellite remotely sensed data integration in Geographical Information Systems to model and predict changes in both the natural and the human environment. It illustrates the powerful instruments given by modern geo-statistical methods, modeling, and visualization techniques. These methods are applied to Arctic, tropical and mid-latitude environments, agriculture, forest, wetlands, and aquatic environments, as well as further engineering-related problems. The present Special Issue gives a balanced view of the present state of the field of geoinformatics

Efficient Motion and Inspection Planning for Medical Robots with Theoretical Guarantees

Medical robots enable faster and safer patient care. Continuum medical robots (e.g., steerable needles) have great potential to accomplish procedures with less damage to patients compared to conventional instruments (e.g., reducing puncturing and cutting of tissues). Due to their complexity and degrees of freedom, such robots are often harder and less intuitive for physicians to operate directly. Automating robot-assisted medical procedures can enable physicians and patients to harness the full potential of medical robots in terms of safety, efficiency, accuracy, and precision.Motion planning methods compute motions for a robot that satisfy various constraints and accomplish a specific task, e.g., plan motions for a mobile robot to move to a target spot while avoiding obstacles. Inspection planning is the task of planning motions for a robot to inspect a set of points of interest, and it has applications in domains such as industrial, field, and medical robotics. With motion and inspection planning, medical robots would be able to automatically accomplish tasks like biopsy and endoscopy while minimizing safety risks and damage to the patient. Computing a motion or inspection plan can be computationally hard since we have to consider application-specific constraints, which come from the robotic system due to the mechanical properties of the robot or come from the environment, such as the requirement to avoid critical anatomical structures during the procedure.I develop motion and inspection planning algorithms that focus on efficiency and effectiveness. Given the same computing power, higher efficiency would shorten the procedure time, thus reducing costs and improving patient outcomes. Additionally, for the automation of medical procedures to be clinically accepted, it is critical from a patient care, safety, and regulatory perspective to certify the correctness and effectiveness of the algorithms involved in procedure automation. Therefore, I focus on providing theoretical guarantees to certify the performance of planners. More specifically, it is important to certify if a planner is able to find a plan if one exists (i.e., completeness) and if a planner is able to find a globally optimal plan according to a given metric (i.e., optimality).Doctor of Philosoph