skip to main content


Title: Unmanned Aircraft Applications in Radiological Surveys
Unmanned vehicles, equipped with radiation detection sensors, can serve as a valuable aid to personnel responding to radiological incidents. The use of tele-operated ground vehicles avoids human exposure to hazardous environments, which in addition to radioactive contamination, might present other risks to personnel. Autonomous unmanned vehicles using algorithms for radioisotope classification, source localization, and efficient exploration allow these vehicles to conduct surveys with reduced human supervision allowing teams to address larger areas in less time. This work presents systems for autonomous radiation search with results presented in several proof-of-concept demonstrations.  more » « less
Award ID(s):
1650465
NSF-PAR ID:
10086397
Author(s) / Creator(s):
; ; ; ; ; ; ;
Date Published:
Journal Name:
IEEE International Symposium on Technologies for Homeland Security
Page Range / eLocation ID:
1 to 5
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Zonta, Daniele ; Su, Zhongqing ; Glisic, Branko (Ed.)
    With the rapid development of smart cities, interest in vehicle automation continues growing. Autonomous vehicles are becoming more and more popular among people and are considered to be the future of ground transportation. Autonomous vehicles, either with adaptive cruise control (ACC) or cooperative adaptive cruise control (CACC), provide many possibilities for smart transportation in a smart city. However, traditional vehicles and autonomous vehicles will have to share the same road systems until autonomous vehicles fully penetrate the market over the next few decades, which leads to conflicts because of the inconsistency of human drivers. In this paper, the performance of autonomous vehicles with ACC/CACC and traditional vehicles in mixed driver environments, at a signalized intersection, were evaluated using the micro-simulator VISSIM. In the simulation, the vehicles controlled by the ACC/CACC and Wiedemann 99 (W99) model represent the behavior of autonomous vehicles and human driver vehicles, respectively. For these two different driver environments, four different transport modes were comprehensively investigated: full light duty cars, full trucks, full motorcycles, and mixed conditions. In addition, ten different seed numbers were applied to each model to avoid coincidence. To evaluate the driving behavior of the human drivers and autonomous vehicles, this paper will compare the total number of stops, average velocity, and vehicle delay of each model at the signalized traffic intersection based on a real road intersection in Minnesota. 
    more » « less
  2. Hybrid traffic which involves both autonomous and human-driven vehicles would be the norm of the autonomous vehicles’ practice for a while. On the one hand, unlike autonomous vehicles, human-driven vehicles could exhibit sudden abnormal behaviors such as unpredictably switching to dangerous driving modes – putting its neighboring vehicles under risks; such undesired mode switching could arise from numbers of human driver factors, including fatigue, drunkenness, distraction, aggressiveness, etc. On the other hand, modern vehicle-to-vehicle (V2V) communication technologies enable the autonomous vehicles to efficiently and reliably share the scarce run-time information with each other [1]. In this paper, we propose, to the best of our knowledge, the first efficient algorithm that can (1) significantly improve trajectory prediction by effectively fusing the run-time information shared by surrounding autonomous vehicles, and can (2) accurately and quickly detect abnormal human driving mode switches or abnormal driving behavior with formal assurance without hurting human drivers’ privacy.

    To validate our proposed algorithm, we first evaluate our proposed trajectory predictor on NGSIM and Argoverse datasets and show that our proposed predictor outperforms the baseline methods. Then through extensive experiments on SUMO simulator, we show that our proposed algorithm has great detection performance in both highway and urban traffic. The best performance achieves detection rate of\(97.3\% \), average detection delay of 1.2s, and 0 false alarm.

     
    more » « less
  3. Robots such as unmanned aerial vehicles (UAVs) deployed for search and rescue (SAR) can explore areas where human searchers cannot easily go and gather information on scales that can transform SAR strategy. Multi-UAV teams therefore have the potential to transform SAR by augmenting the capabilities of human teams and providing information that would otherwise be inaccessible. Our research aims to develop new theory and technologies for field deploying autonomous UAVs and managing multi-UAV teams working in concert with multi-human teams for SAR. Specifically, in this paper we summarize our work in progress towards these goals, including: (1) a multi-UAV search path planner that adapts to human behavior; (2) an in-field distributed computing prototype that supports multi-UAV computation and communication; (3) behavioral modeling that fields spatially localized predictions of lost person location; and (4) an interface between human searchers and UAVs that facilitates human-UAV interaction over a wide range of autonomy. 
    more » « less
  4. null (Ed.)
    The use of semi-autonomous Unmanned Aerial Vehicles (UAVs or drones) to support emergency response scenarios, such as fire surveillance and search-and-rescue, has the potential for huge societal benefits. Onboard sensors and artificial intelligence (AI) allow these UAVs to operate autonomously in the environment. However, human intelligence and domain expertise are crucial in planning and guiding UAVs to accomplish the mission. Therefore, humans and multiple UAVs need to collaborate as a team to conduct a time-critical mission successfully. We propose a meta-model to describe interactions among the human operators and the autonomous swarm of UAVs. The meta-model also provides a language to describe the roles of UAVs and humans and the autonomous decisions. We complement the meta-model with a template of requirements elicitation questions to derive models for specific missions. We also identify common scenarios where humans should collaborate with UAVs to augment the autonomy of the UAVs. We introduce the meta-model and the requirements elicitation process with examples drawn from a search-and-rescue mission in which multiple UAVs collaborate with humans to respond to the emergency. We then apply it to a second scenario in which UAVs support first responders in fighting a structural fire. Our results show that the meta-model and the template of questions support the modeling of the human-on-the-loop human interactions for these complex missions, suggesting that it is a useful tool for modeling the human-on-the-loop interactions for multi-UAVs missions. 
    more » « less
  5. 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. 
    more » « less