Trust Repair in Human-Swarm Teams+

Swarm robots are coordinated via simple control laws to generate emergent behaviors such as flocking, rendezvous, and deployment. Human-swarm teaming has been widely proposed for scenarios, such as human-supervised teams of unmanned aerial vehicles (UAV) for disaster rescue, UAV and ground vehicle cooperation for building security, and soldier-UAV teaming in combat. Effective cooperation requires an appropriate level of trust, between a human and a swarm. When an UAV swarm is deployed in a real-world environment, its performance is subject to real-world factors, such as system reliability and wind disturbances. Degraded performance of a robot can cause undesired swarm behaviors, decreasing human trust. This loss of trust, in turn, can trigger human intervention in UAVs' task executions, decreasing cooperation effectiveness if inappropriate. Therefore, to promote effective cooperation we propose and test a trust-repairing method (Trust-repair) restoring performance and human trust in the swarm to an appropriate level by correcting undesired swarm behaviors. Faulty swarms caused by both external and internal factors were simulated to evaluate the performance of the Trust-repair algorithm in repairing swarm performance and restoring human trust. Results show that Trust-repair is effective in restoring trust to a level intermediate between normal and faulty conditions

Industry Led Use-Case Development for Human-Swarm Operations

In the domain of unmanned vehicles, autonomous robotic swarms promise to deliver increased efficiency and collective autonomy. How these swarms will operate in the future, and what communication requirements and operational boundaries will arise are yet to be sufficiently defined. A workshop was conducted with 11 professional unmanned-vehicle operators and designers with the objective of identifying use-cases for developing and testing robotic swarms. Three scenarios were defined by experts and were then compiled to produce a single use case outlining the scenario, objectives, agents, communication requirements and stages of operation when collaborating with highly autonomous swarms. Our compiled use case is intended for researchers, designers, and manufacturers alike to test and tailor their design pipeline to accommodate for some of the key issues in human-swarm ininteraction. Examples of application include informing simulation development, forming the basis of further design workshops, and identifying trust issues that may arise between human operators and the swarm.Comment: Accepted at AAAI 2022 Spring Symposium Series (Putting AI in the Critical Loop: Assured Trust and Autonomy in Human-Machine Teams

Industry Led Use-Case Development for Human-Swarm Operations

In the domain of unmanned vehicles, autonomous robotic swarms promise to deliver increased efficiency and collective autonomy. How these swarms will operate in the future, and what communication requirements and operational boundaries will arise are yet to be sufficiently defined. A workshop was conducted with 11 professional unmanned-vehicle operators and designers with the objective of identifying use-cases for developing and testing robotic swarms. Three scenarios were defined by experts and were then compiled to produce a single use case outlining the scenario, objectives, agents, communication requirements and stages of operation when collaborating with highly autonomous swarms. Our compiled use case is intended for researchers, designers, and manufacturers alike to test and tailor their design pipeline to accommodate for some of the key issues in human-swarm ininteraction. Examples of application include informing simulation development, forming the basis of further design workshops, and identifying trust issues that may arise between human operators and the swarm

Human–Autonomy Teaming: Definitions, Debates, and Directions

Researchers are beginning to transition from studying human–automation interaction to human–autonomy teaming. This distinction has been highlighted in recent literature, and theoretical reasons why the psychological experience of humans interacting with autonomy may vary and affect subsequent collaboration outcomes are beginning to emerge (de Visser et al., 2018; Wynne and Lyons, 2018). In this review, we do a deep dive into human–autonomy teams (HATs) by explaining the differences between automation and autonomy and by reviewing the domain of human–human teaming to make inferences for HATs. We examine the domain of human–human teaming to extrapolate a few core factors that could have relevance for HATs. Notably, these factors involve critical social elements within teams that are central (as argued in this review) for HATs. We conclude by highlighting some research gaps that researchers should strive toward answering, which will ultimately facilitate a more nuanced and complete understanding of HATs in a variety of real-world contexts

Swarm-Based Drone-as-a-Service for Delivery

There has been a growing interest in the applications of drones as a cost-effective, efficient, and environmentally friendly alternative in various domains. Particularly in the context of delivery services, the demand for contactless and efficient delivery solutions has surged. Drone delivery offers faster and greener deliveries. However, existing methods focus primarily on point-to-point delivery, limiting their potential for optimisation. This thesis proposes a novel approach to servitise drone delivery by operating through a skyway network composed of building rooftops, enabling drones to traverse between source and destination while recharging at intermediate nodes. Although single drone delivery offers numerous advantages, it faces significant challenges in scenarios where multiple packages require simultaneous delivery. Flight regulations, which often limit the carrying capacity of individual drones, necessitate the exploration of alternative solutions. Therefore, this thesis presents a novel Swarm-Based Drone-as-a-Service (SDaaS) model and framework for multiple package delivery. The proposed framework prioritises the composition of services that optimise Quality of Service (QoS) factors, such as delivery time and energy consumption. This thesis identifies swarm-specific constraints and leverages the unique characteristics of drone swarms. It explores swarm formations, in-flight wireless charging between drones, and allocation problems to maximise drone utilisation for consumer deliveries. Furthermore, this research investigates the recommendation of services to consumers based on their preferences, aiming to increase their satisfaction. Moreover, the framework addresses the resilience of SDaaS by addressing issues related to drone soft failures and their impact on other swarm members. Ultimately, this work paves the way for the widespread adoption and optimisation of swarm-based drone services in the context of last-mile delivery