381 research outputs found
Sampling-based motion planning with deterministic u-calculus specifications
In this paper, we propose algorithms for the online
computation of control programs for dynamical systems
that provably satisfy a class of temporal logic specifications.
Such specifications have recently been proposed in the literature
as a powerful tool to synthesize provably correct
control programs, for example for embedded systems and
robotic applications. The proposed algorithms, generalizing
state-of-the-art algorithms for point-to-point motion planning,
incrementally build finite transition systems representing a
discrete subset of dynamically feasible trajectories. At each
iteration, local -calculus model-checking methods are used
to establish whether the current transition system satisfies
the specifications. Efficient sampling strategies are presented,
ensuring the probabilistic completeness of the algorithms. We
demonstrate the effectiveness of the proposed approach on
simulation examples.Michigan/AFRL Collaborative Center on Control Sciences, AFOSR (grant no. FA 8650-07-2-3744
Leveraging Compositional Methods for Modeling and Verification of an Autonomous Taxi System
We apply a compositional formal modeling and verification method to an
autonomous aircraft taxi system. We provide insights into the modeling approach
and we identify several research areas where further development is needed.
Specifically, we identify the following needs: (1) semantics of composition of
viewpoints expressed in different specification languages, and tools to reason
about heterogeneous declarative models; (2) libraries of formal models for
autonomous systems to speed up modeling and enable efficient reasoning; (3)
methods to lift verification results generated by automated reasoning tools to
the specification level; (4) probabilistic contract frameworks to reason about
imperfect implementations; (5) standard high-level functional architectures for
autonomous systems; and (6) a theory of higher-order contracts. We believe that
addressing these research needs, among others, could improve the adoption of
formal methods in the design of autonomous systems including learning-enabled
systems, and increase confidence in their safe operations.Comment: 2023 International Conference on Assured Autonomy (ICAA
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Belief-Space Planning for Resourceful Manipulation and Mobility
Robots are increasingly expected to work in partially observable and unstructured environments. They need to select actions that exploit perceptual and motor resourcefulness to manage uncertainty based on the demands of the task and environment. The research in this dissertation makes two primary contributions. First, it develops a new concept in resourceful robot platforms called the UMass uBot and introduces the sixth and seventh in the uBot series. uBot-6 introduces multiple postural configurations that enable different modes of mobility and manipulation to meet the needs of a wide variety of tasks and environmental constraints. uBot-7 extends this with the use of series elastic actuators (SEAs) to improve manipulation capabilities and support safer operation around humans. The resourcefulness of these robots is complemented with a belief-space planning framework that enables task-driven action selection in the context of the partially observable environment. The framework uses a compact but expressive state representation based on object models. We extend an existing affordance-based object model, called an aspect transition graph (ATG), with geometric information. This enables object-centric modeling of features and actions, making the model much more expressive without increasing the complexity. A novel task representation enables the belief-space planner to perform general object-centric tasks ranging from recognition to manipulation of objects. The approach supports the efficient handling of multi-object scenes. The combination of the physical platform and the planning framework are evaluated in two novel, challenging, partially observable planning domains. The ARcube domain provides a large population of objects that are highly ambiguous. Objects can only be differentiated using multi-modal sensor information and manual interactions. In the dexterous mobility domain, a robot can employ multiple mobility modes to complete navigation tasks under a variety of possible environment constraints. The performance of the proposed approach is evaluated using experiments in simulation and on a real robot
Software tools for the cognitive development of autonomous robots
Robotic systems are evolving towards higher degrees of autonomy. This paper reviews the cognitive tools available nowadays for the fulfilment of abstract or long-term goals as well as for learning and modifying their behaviour.Peer ReviewedPostprint (author's final draft
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