Search for: All records

Abstract This paper presents the development of an interface that enables humans to collaborate with robot swarms (RSs) to effectively accomplish missions in highly uncertain environments. Autonomous RS technology has advanced to the point where robots can perform various tasks without human intervention. However, there are still two major uncertainties in mission execution. The first uncertainty arises from the limitations of artificial intelligence (AI), such as the inaccuracy of computer vision systems. The second uncertainty comes from the decision-making aspect, where the mission requirements may not be accurately communicated to the RS. To overcome these uncertainties, it is crucial to design a system that allows humans to monitor the RS and actively participate in critical decision-making processes during missions. We design an interface to: 1) estimate human cognitive states to monitor the human partner, 2) determine the best communication methods to calibrate cognitive states, and 3) allocate functions among humans and robots to optimize team performance. We demonstrate this approach through drone search and rescue (SAR) missions. 

Finding collision-free paths is crucial for autonomous multi-robots (AMRs) to complete assigned missions, ranging from search operations to military tasks. To achieve this, AMRs rely on collaborative collision avoidance algorithms. Unfortunately, the robustness of these algorithms against false data injection attacks (FDIAs) remains unexplored. In this paper, we introduce Raven, a tool to identify effective and stealthy semantic attacks (eg, herding). Effective attacks minimize positional displacement and the number of false data injections by using temporal logic and stochastic optimization techniques. Stealthy attacks remain within sensor noise ranges and maintain spatiotemporal consistency. We evaluate Raven against two state-of-the-art collision avoidance algorithms, ORCA and GLAS. Our results show that a single false data injection impacts multi-robot systems by causing position deviation or even collisions. We evaluate Raven on three testbeds–a numerical simulator, a high-fidelity simulator, and Crazyflie drones. Our results reveal five design flaws in these algorithms and underscore the importance of developing robust defenses against FDIAs. Finally, we propose countermeasures to mitigate the attacks we have uncovered. 

This paper proposes a novel stochastic-skill-level-based shared control framework to assist human novices to emulate human experts in complex dynamic control tasks. The proposed framework aims to infer stochastic-skill-levels (SSLs) of the human novices and provide personalized assistance based on the inferred SSLs. SSL can be assessed as a stochastic variable which denotes the probability that the novice will behave similarly to experts. We propose a data-driven method which can characterize novice demonstrations as a novice model and expert demonstrations as an expert model, respectively. Then, our SSL inference approach utilizes the novice and expert models to assess the SSL of the novices in complex dynamic control tasks. The shared control scheme is designed to dynamically adjust the level of assistance based on the inferred SSL to prevent frustration or tedium during human training due to poorly imposed assistance. The proposed framework is demonstrated by a human subject experiment in a human training scenario for a remotely piloted urban air mobility (UAM) vehicle. The results show that the proposed framework can assess the SSL and tailor the assistance for an individual in real-time. The proposed framework is compared to practice-only training (no assistance) and a baseline shared control approach to test the human learning rates in the designed training scenario with human subjects. A subjective survey is also examined to monitor the user experience of the proposed framework.