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As autonomous systems begin to operate amongst humans, methods for safe interaction must be investigated. We consider an example of a small autonomous vehicle in a pedestrian zone that must safely maneuver around people in a free-form fashion. We investigate two key questions: How can we effectively integrate pedestrian intent estimation into our autonomous stack? Can we develop an online monitoring framework to give rigorous assurances on the safety of such human-robot interactions? We present a pedestrian intent estimation framework that can accurately predict future pedestrian trajectories given multiple possible goal locations. We integrate this into a reachability-based online monitoring and decision making scheme that formally assesses the safety of these interactions with nearly real-time performance (approximately 0.1s). These techniques are both tested in simulation and integrated on a test vehicle with a complete in-house autonomous stack, demonstrating safe interaction in real-world experiments.
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Unified Co-Simulation Framework for Autonomous UAVs
Autonomous drones (UAVs) have rapidly grown in popularity due to their form factor, agility, and ability to operate in harsh or hostile environments. Drone systems come in various form factors and configurations and operate under tight physical parameters. Further, it has been a significant challenge for architects and researchers to develop optimal drone designs as open-source simulation frameworks either lack the necessary capabilities to simulate a full drone flight stack or they are extremely tedious to setup with little or no maintenance or support. In this paper, we develop and present UniUAVSim, our fully open-source co-simulation framework capable of running software-in-the-loop (SITL) and hardware-in-the-loop (HITL) simulations concurrently. The paper also provides insights into the abstraction of a drone flight stack and details how these abstractions aid in creating a simulation framework which can accurately provide an optimal drone design given physical parameters and constraints. The framework was validated with real-world hardware and is available to the research community to aid in future architecture research for autonomous systems.
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- Award ID(s):
- 2103951
- PAR ID:
- 10514291
- Publisher / Repository:
- ACM
- Date Published:
- Journal Name:
- PEARC '23: Practice and Experience in Advanced Research Computing
- ISBN:
- 9781450399852
- Page Range / eLocation ID:
- 474 to 477
- Format(s):
- Medium: X
- Location:
- Portland OR USA
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3569951.3597544
