An official website of the United States government
Flight-time failures of small Uncrewed Aerial Systems (sUAS) can have a severe impact on people or the environment. Therefore, sUAS applications must be thoroughly evaluated and tested to ensure their adherence to specified requirements, and safe behavior under real-world conditions, such as poor weather, wireless interference, and satellite failure. However, current simulation environments for autonomous vehicles, including sUAS, provide limited support for validating their behavior in diverse environmental contexts and moreover, lack a test harness to facilitate structured testing based on system-level requirements. We address these shortcomings by eliciting and specifying requirements for an sUAS testing and simulation platform, and developing and deploying it. The constructed platform, DroneWorld (\DW), allows sUAS developers to define the operating context, configure multi-sUAS mission requirements, specify safety properties, and deploy their own custom sUAS applications in a high-fidelity 3D environment. The DroneWorld Monitoring system collects runtime data from sUAS and the environment, analyzes compliance with safety properties, and captures violations. We report on two case studies in which we used our platform prior to real-world sUAS deployments, in order to evaluate sUAS mission behavior in various environmental contexts. Furthermore, we conducted a study with developers and found that DroneWorld simplifies the process of specifying requirements-driven test scenarios and analyzing acceptance test results.
more »
« less
Interlocking safety cases for unmanned autonomous systems in urban environments
The growing adoption of small unmanned aircraft systems (sUAS) for tasks such as eCommerce, aerial surveillance, and environmental monitoring introduces the need for new safety mechanisms in an increasingly cluttered airspace. Safety assurance cases (SAC) provide a state-of-the-art solution for reasoning about system and software safety in numerous safety-critical domains. We propose a novel approach based on the idea of interlocking safety cases. The sUAS infrastructure safety case (iSAC) specifies assumptions and applies constraints upon the behavior of sUAS entering the airspace. Each sUAS then demonstrates compliance to the iSAC by presenting its own (partial) safety case (uSAC) which connects to the iSAC through a set of interlock points. To enforce a “trust but verify” policy, sUAS conformance is monitored at runtime while it is in the airspace and its behavior is described using a reputation model based on the iSAC’s expectations of its behavior.
more »
« less
- Award ID(s):
- 1737496
- PAR ID:
- 10076566
- Date Published:
- Journal Name:
- 40th International Conference on Software Engineering
- Page Range / eLocation ID:
- 416 to 417
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Small Unmanned Aircraft Systems (sUAS) will be an important component of the smart city and intelligent transportation environments of the near future. The demand for sUAS related applications, such as commercial delivery and land surveying, is expected to grow rapidly in next few years. In general, sUAS traffic scheduling and management functions are needed to coordinate the launching of sUAS from different launch sites and plan their trajectories to avoid conflict while considering several other constraints such as expected arrival time, minimum flight energy, and availability of communication resources. However, as the airbone sUAS density grows in a certain area, it is difficult to foresee the potential airspace and communications resource conflicts and make immediate decisions to avoid them. To address this challenge, we present a temporal and spatial routing algorithm for sUAS trajectory management in a high density urban area. It plans sUAS movements in a spatial and temporal maze with the consideration of obstacles that are either static or dynamic in time. The routing allows the sUAS to avoid static no-fly areas (i.e. static obstacles) or other in-flight sUAS and areas that have congested communication resources (i.e. dynamic obstacles). The algorithm is evaluated using an agent-based simulation platform. The simulation results show that the proposed algorithm outperforms reference route management algorithms in many areas, especially in processing speed and memory efficiency. Detailed comparisons are provided for the sUAS flight time, the overall throughput, the conflict rate and communication resource utilization. The results demonstrate that our proposed algorithm can be used as a solution to improve the efficiency of airspace and communication resource utilization for next generation smart city and smart transportation.more » « less
-
Small Unmanned Aircraft Systems (sUAS) will be an important component of the smart city and intelligent transportation environments of the near future. The demand for sUAS related applications, such as commercial delivery and land surveying, is expected to grow rapidly in next few years. In general, sUAS traffic routing and management functions are needed to coordinate the launching of sUAS from different launch sites and determine their trajectories to avoid conflict while considering several other constraints such as expected arrival time, minimum flight energy, and availability of communication resources. However, as the airborne sUAS density grows in a certain area, it is difficult to foresee the potential airspace and communications resource conflicts and make immediate decisions to avoid them. To address this challenge, we present a temporal and spatial routing algorithm and simulation platform for sUAS trajectory management in a high density urban area that plans sUAS movements in a spatial and temporal maze taking into account obstacles that are either static or dynamic in time. The routing allows the sUAS to avoid static no-fly areas (i.e. static obstacles) or other in-flight sUAS and areas that have congested communication resources (i.e. dynamic obstacles). The algorithm is evaluated using an agent-based simulation platform. The simulation results show that the proposed algorithm outperforms other route management algorithms in many areas, especially in processing speed and memory efficiency. Detailed comparisons are provided for the sUAS flight time, the overall throughput, conflict rate and communication resource utilization. The results demonstrate that our proposed algorithm can be used to address the airspace and communication resource utilization needs for a next generation smart city and smart transportation.more » « less
-
Small uncrewed aerial systems, sUAS, provide an invaluable resource for performing a variety of surveillance, search, and delivery tasks in remote or hostile terrains which may not be accessible by other means. Due to the critical role sUAS play in these situations, it is vital that they are well configured in order to ensure a safe and stable flight. However, it is not uncommon for mistakes to occur in configuration and calibration, leading to failures or incomplete missions. To address this problem, we propose a set of self-adaptive mechanisms and implement them into a self-adaptive framework,CICADA, for Controller Instability-preventing Configuration Aware Drone Adaptation.CICADAdynamically detects unstable drone behavior during flight and adapts to mitigate this threat. We have built a prototype ofCICADAusing a popular open source sUAS flight control software and experimented with a large number of different configurations in simulation. We then performed a case study with physical drones to determine if our framework will work in practice. Experimental results show thatCICADA’sadaptations reduce controller instability and enable the sUAS to recover from up to 33.8% of poor configurations. In cases where we cannot complete the intended mission, invoking alternative adaptations may still help by allowing the vehicle to loiter or land safely in place, avoiding potentially catastrophic crashes. These safety-focused adaptations can mitigate unsafe behavior in 52.9% to 64.7% of dangerous configurations. We further show that rule-based approaches can be leveraged to automatically select an appropriate adaptation strategy based on the severity of instability encountered, with up to a 14.2% improvement over direct adaptation. Finally, we introduce a variation of our primary adaptation strategy designed to allow more cautious adaptation with limited configuration information, which gets within 6.7% of our primary adaptation strategy despite not requiring an optimal knowledge base.more » « less
-
In emergency response scenarios, autonomous small Unmanned Aerial Systems (sUAS) must be configured and deployed quickly and safely to perform mission-specific tasks. In this paper, we present \DR, a Software Product Line for rapidly configuring and deploying a multi-role, multi-sUAS mission whilst guaranteeing a set of safety properties related to the sequencing of tasks within the mission. Individual sUAS behavior is governed by an onboard state machine, combined with coordination handlers which are configured dynamically within seconds of launch and ultimately determine the sUAS' behaviors, transition decisions, and interactions with other sUAS, as well as human operators. The just-in-time manner in which missions are configured precludes robust upfront testing of all conceivable combinations of features -- both within individual sUAS and across cohorts of collaborating ones. To ensure the absence of common types of configuration failures and to promote safe deployments, we check vital properties of the dynamically generated sUAS specifications and coordination handlers before sUAS are assigned their missions. We evaluate our approach in two ways. First, we perform validation tests to show that the end-to-end configuration process results in correctly executed missions, and second, we apply fault-based mutation testing to show that our safety checks successfully detect incorrect task sequences.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3183440.3195035
