Title: Collaborative Semantic Data Fusion with Dynamically Observable Decision Processes
This work presents novel techniques for tightly integrated online information fusion and planning in human-autonomy teams operating in partially known environments. Motivated by dynamic target search problems, we present a new map-based sketch interface for online soft-hard data fusion. This interface lets human collaborators efficiently update map information and continuously build their own highly flexible ad hoc dictionaries for making language-based semantic observations, which can be actively exploited by autonomous agents in optimal search and information gathering problems. We formally link these capabilities to POMDP algorithms for optimal planning under uncertainty, and develop a new Dynamically Observable Monte Carlo planning (DOMCP) algorithm as an efficient means for updating online sampling-based planning policies for POMDPs with non-static observation models. DOMCP is validated on a small scale robot localization problem, and then demonstrated with our new user interface on a simulated dynamic target search scenario in a partially known outdoor environment. more »« less
Burks, Luke; Loefgren, Ian; Barbier, Luke; Muesing, Jeremy; McGinley, Jamison; Vunnam, Sousheel; Ahmed, Nisar(
, 2018 21st International Conference on Information Fusion (FUSION))
null
(Ed.)
In search applications, autonomous unmanned vehicles must be able to efficiently reacquire and localize mobile targets that can remain out of view for long periods of time in large spaces. As such, all available information sources must be actively leveraged - including imprecise but readily available semantic observations provided by humans. To achieve this, this work develops and validates a novel collaborative human-machine sensing solution for dynamic target search. Our approach uses continuous partially observable Markov decision process (CPOMDP) planning to generate vehicle trajectories that optimally exploit imperfect detection data from onboard sensors, as well as semantic natural language observations that can be specifically requested from human sensors. The key innovation is a scalable hierarchical Gaussian mixture model formulation for efficiently solving CPOMDPs with semantic observations in continuous dynamic state spaces. The approach is demonstrated and validated with a real human-robot team engaged in dynamic indoor target search and capture scenarios on a custom testbed.
Ren, Zhongqiang; Rathinam, Sivakumar; Choset, Howie(
, Proceedings of Robotics: Science and Systems)
NA
(Ed.)
Conventional Multi-Agent Path Finding (MAPF)
problems aim to compute an ensemble of collision-free paths
for multiple agents from their respective starting locations to
pre-allocated destinations. This work considers a generalized
version of MAPF called Multi-Agent Combinatorial Path Finding
(MCPF) where agents must collectively visit a large number of
intermediate target locations along their paths before arriving at
destinations. This problem involves not only planning collisionfree
paths for multiple agents but also assigning targets and
specifying the visiting order for each agent (i.e. multi-target
sequencing). To solve the problem, we leverage the well-known
Conflict-Based Search (CBS) for MAPF and propose a novel
framework called Conflict-Based Steiner Search (CBSS). CBSS
interleaves (1) the conflict resolving strategy in CBS to bypass
the curse of dimensionality in MAPF and (2) multiple traveling
salesman algorithms to handle the combinatorics in multi-target
sequencing, to compute optimal or bounded sub-optimal paths
for agents while visiting all the targets. Our extensive tests verify
the advantage of CBSS over baseline approaches in terms of
computing shorter paths and improving success rates within a
runtime limit for up to 20 agents and 50 targets. We also evaluate
CBSS with several MCPF variants, which demonstrates the
generality of our problem formulation and the CBSS framework.
In this thesis we propose novel estimation techniques for localization and planning problems, which are key challenges in long-term autonomy. We take inspiration in our methods from non-parametric estimation and use tools such as kernel density estimation, non-linear least-squares optimization, binary masking, and random sampling. We show that these methods, by avoiding explicit parametric models, outperform existing methods that use them. Despite the seeming differences between localization and planning, we demonstrate in this thesis that the problems share core structural similarities. When real or simulation-sampled measurements are expensive, noisy, or high variance, non-parametric estimation techniques give higher-quality results in less time. We first address two localization problems. In order to permit localization with a set of ad hoc-placed radios, we propose an ultra-wideband (UWB) graph realization system to localize the radios. Our system achieves high accuracy and robustness by using kernel density estimation for measurement probability densities, by explicitly modeling antenna delays, and by optimizing this combination with a non-linear least squares formulation. Next, in order to then support robotic navigation, we present a flexible system for simultaneous localization and mapping (SLAM) that combines elements from both traditional dense metric SLAM and topological SLAM, using a binary "masking function" to focus attention. This masking function controls which lidar scans are available for loop closures. We provide several masking functions based on approximate topological class detectors. We then examine planning problems in the final chapter and in the appendix. In order to plan with uncertainty around multiple dynamic agents, we describe Monte-Carlo Policy-Tree Decision Making (MCPTDM), a framework for efficiently computing policies in partially-observable, stochastic, continuous problems. MCPTDM composes a sequence of simpler closed-loop policies and uses marginal action costs and particle repetition to improve cost estimates and sample efficiency by reducing variance. Finally, in the appendix we explore Learned Similarity Monte-Carlo Planning (LSMCP), where we seek to enhance the sample efficiency of partially observable Monte Carlo tree search-based planning by taking advantage of similarities in the final outcomes of similar states and actions. We train a multilayer perceptron to learn a similarity function which we then use to enhance value estimates in the planning. Collectively, we show in this thesis that non-parametric methods promote long-term autonomy by reducing error and increasing robustness across multiple domains.
Recent work has considered personalized route planning based on user profiles, but none of it accounts for human trust. We argue that human trust is an important factor to consider when planning routes for automated vehicles. This article presents a trust-based route-planning approach for automated vehicles. We formalize the human-vehicle interaction as a partially observable Markov decision process (POMDP) and model trust as a partially observable state variable of the POMDP, representing the human’s hidden mental state. We build data-driven models of human trust dynamics and takeover decisions, which are incorporated in the POMDP framework, using data collected from an online user study with 100 participants on the Amazon Mechanical Turk platform. We compute optimal routes for automated vehicles by solving optimal policies in the POMDP planning and evaluate the resulting routes via human subject experiments with 22 participants on a driving simulator. The experimental results show that participants taking the trust-based route generally reported more positive responses in the after-driving survey than those taking the baseline (trust-free) route. In addition, we analyze the trade-offs between multiple planning objectives (e.g., trust, distance, energy consumption) via multi-objective optimization of the POMDP. We also identify a set of open issues and implications for real-world deployment of the proposed approach in automated vehicles.
Ghasemi, Mahsa; Scope Crafts, Evan; Zhao, Bo; and Topcu, Ufuk(
, International Conference on Automated Planning and Scheduling)
null
(Ed.)
In planning problems, it is often challenging to fully model the desired specifications. In particular, in human-robot interaction, such difficulty may arise due to human's preferences that are either private or complex to model. Consequently, the resulting objective function can only partially capture the specifications and optimizing that may lead to poor performance with respect to the true specifications. Motivated by this challenge, we formulate a problem, called diverse stochastic planning, that aims to generate a set of representative---small and diverse---behaviors that are near-optimal with respect to the known objective. In particular, the problem aims to compute a set of diverse and near-optimal policies for systems modeled by a Markov decision process. We cast the problem as a constrained nonlinear optimization for which we propose a solution relying on the Frank-Wolfe method. We then prove that the proposed solution converges to a stationary point and demonstrate its efficacy in several planning problems.
Burks, L., and Ahmed, N. Collaborative Semantic Data Fusion with Dynamically Observable Decision Processes. Retrieved from https://par.nsf.gov/biblio/10222330. 2019 22th International Conference on Information Fusion (FUSION) .
Burks, L., & Ahmed, N. Collaborative Semantic Data Fusion with Dynamically Observable Decision Processes. 2019 22th International Conference on Information Fusion (FUSION), (). Retrieved from https://par.nsf.gov/biblio/10222330.
Burks, L., and Ahmed, N.
"Collaborative Semantic Data Fusion with Dynamically Observable Decision Processes". 2019 22th International Conference on Information Fusion (FUSION) (). Country unknown/Code not available. https://par.nsf.gov/biblio/10222330.
@article{osti_10222330,
place = {Country unknown/Code not available},
title = {Collaborative Semantic Data Fusion with Dynamically Observable Decision Processes},
url = {https://par.nsf.gov/biblio/10222330},
abstractNote = {This work presents novel techniques for tightly integrated online information fusion and planning in human-autonomy teams operating in partially known environments. Motivated by dynamic target search problems, we present a new map-based sketch interface for online soft-hard data fusion. This interface lets human collaborators efficiently update map information and continuously build their own highly flexible ad hoc dictionaries for making language-based semantic observations, which can be actively exploited by autonomous agents in optimal search and information gathering problems. We formally link these capabilities to POMDP algorithms for optimal planning under uncertainty, and develop a new Dynamically Observable Monte Carlo planning (DOMCP) algorithm as an efficient means for updating online sampling-based planning policies for POMDPs with non-static observation models. DOMCP is validated on a small scale robot localization problem, and then demonstrated with our new user interface on a simulated dynamic target search scenario in a partially known outdoor environment.},
journal = {2019 22th International Conference on Information Fusion (FUSION)},
author = {Burks, L. and Ahmed, N.},
editor = {null}
}
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