Quantifying trust in human–robot interaction is a significant challenge due to the lack of a universally accepted definition of “trust” in this domain. Existing approaches often rely on static self-report measures, which are limited in capturing dynamic trust behaviors in safety critical contexts such as advanced air mobility. To address this gap, we introduce a novel empirical framework for quantifying trust in this domain. The proposed framework integrates performance, behavioral, and data-driven metrics to assess how users calibrate their trust during simulated flight missions. Four distinct trust-measurement tools are presented: efficiency factor, obstacle clearance factor, manual/automatic mode usage, and geometric difference. In a user study with 40 participants across 30 simulation runs, the results show that 1) trust in the automation increases with operator experience, as evidenced by improved efficiency ($r = -0.23$,$p < 10^{-12}$) and fewer manual interventions ($r = -0.22$,$p = 7.6\times 10^{-15}$); 2) stable individual differences (e.g., risk tolerance) significantly influence trust-related metrics, with obstacle clearance distances remaining consistent for most participants across runs ($r = -0.13$,$p = 1.9\times 10^{-5}$); and 3) the geometric difference metric captures nuanced behavioral variations, revealing distinct trust-based operational strategies among users. By integrating performance metrics and behavioral patterns, this multidimensional framework offers practical tools for enhancing the trustworthiness of autonomous systems. It fills a key gap in current trust assessment approaches, supports the development of safer and more effective advanced air mobility systems, and contributes to the broader advancement of human–robot interaction research.

In this paper, an Active Cooperative Localization system for Quadrotor Unmanned Aerial Vehicles is developed. The optimal trajectories are determined by minimizing the uncertainty in position estimation by Extended Kalman Filter. In this system, a piecewise polynomial parameterization of trajectories is adopted for the optimizer, and the underlying state estimator is updated with appropriate models of sensors and quadrotor dynamics. This system is verified in extensive simulations in the scenario of a team of quadrotors with heterogeneous GNSS capabilities. These simulations answer an open question, showing that solving for trajectories by minimizing Kalman covariance computed in a noiseless environment is reasonable and that the optimized trajectories offer visible reductions in positioning uncertainty in the presence of noise.

In drone racing, the time-minimum trajectory is affected by the drone’s capabilities, the layout of the race track, and the configurations of the gates (e.g., their shapes and sizes). However, previous studies neglect the configuration of the gates, simply rendering drone racing a waypoint-passing task. This formulation often leads to a conservative choice of paths through the gates, as the spatial potential of the gates is not fully utilized. To address this issue, we present a time-optimal planner that can faithfully model gate constraints with various configurations and thereby generate a more time-efficient trajectory while considering the single-rotor-thrust limits. Our approach excels in computational efficiency which only takes a few seconds to compute the full state and control trajectories of the drone through tracks with dozens of different gates. Extensive simulations and experiments confirm the effectiveness of the proposed methodology, showing that the lap time can be further reduced by taking into account the gate’s configuration. We validate our planner in real-world flights and demonstrate super-extreme flight trajectory through race tracks.

This article presents a distributed, efficient, scalable, and real-time motion planning algorithm for a large group of agents moving in 2-D or 3-D spaces. This algorithm enables autonomous agents to generate individual trajectories independently with only the relative position information of neighboring agents. Each agent applies a force-based control that contains two main terms: 1) collision avoidance and 2) navigational feedback. The first term keeps two agents separate with a certain distance, while the second term attracts each agent toward its goal location. Compared with existing collision-avoidance algorithms, the proposed force-based motion planning (FMP) algorithm can find collision-free motions with lower transition time, free from velocity state information of neighboring agents. It leads to less computational overhead. The performance of proposed FMP is examined over several dense and complex 2-D and 3-D benchmark simulation scenarios, with results outperforming existing methods.

In this article, the fault-tolerant synchronization and time-varying tracking control problem is investigated for nonlinear multivehicle systems (MVSs) in the presence of partial loss-of-control-effectiveness (LoCE) faults. Based on the graph theory, a two-level fault-tolerant cooperative control framework is proposed, namely, the low-level distributed nominal control scheme and the high-level topology reconfiguration protocols. The low-level scheme is developed to guarantee system performances in the fault-free scenario. With the low-level scheme, the high-level topology reconfiguration protocols, each of which corresponds to one partial LoCE fault scenario, are then proposed to mitigate the fault impact by adjusting the underlying topology. Accordingly, without modifying the structure or the design parameter of the low-level control scheme, the proposed framework can guarantee the synchronization and tracking errors of the MVS asymptotically convergent to zero in both fault-free and fault scenarios. Finally, the effectiveness of the proposed control method is verified via a simulation study of three degree-of-freedom helicopters.

This paper studies the cooperative hunting problem, where a group of agents encircle a target while avoiding collisions with each other and with obstacles in the environment. The paper deals with obstacle rich environments and dynamic (moving obstacle) environments by formulating the problem as both a control problem and a planning problem. A model predictive control (MPC) method is proposed which integrates a multi-agent planner with the cooperative hunting objective while also accounting for UAV dynamics. The effectiveness of the proposed method is verified through a comparative analysis with optimal reciprocal collision avoidance (ORCA), and then validated through experiments with quadrotor UAVs. Using the proposed method, agents no longer get stuck in local minima for obstacle rich environments and capture the target faster with shorter trajectories in moving obstacle environments.

This paper presents a simultaneous localization and mapping (SLAM) algorithm that allows a recursive state estimation process to be both distributed and decentralized in a sparse robot network that is never guaranteed to be fully connected (communication-wise). In such a sparse network, a robot may not always have the latest odometry and measurements from other robots. Our approach allows robots to obtain a temporary (localization and map) estimate at the current timestep using information available locally, but we also ensure that the centralized-equivalent estimate can always be recovered by all robots at a later time; we do not require a robot to keep track of what other robots know when it applies the Markov property to discard past information. Our method is validated through a hardware SLAM experiment where we distribute data association hypotheses amongst a team of robots. Estimate errors are shown to validate the performance of our approach. We also discuss the trade-offs and show comparisons between our distributed approach versus a non-distributed one.

Communication among robots is key to performance in cooperative multi-robot systems. In practice, communication connections for information exchange between all robots are not always guaranteed, which adds difficulty to state estimation. This paper examines the decentralized cooperative simultaneous localization and mapping (SLAM) problem under a sparsely-communicating and dynamic network. We mathematically prove how the centralized-equivalent estimate can be obtained by all robots in the network in a decentralized manner. Furthermore, a robot only needs to consider its own knowledge of the network topology to detect when the centralized-equivalent estimate is obtainable. Our approach is validated through more than 250 minutes of experiments using a team of real robots, with accurate groundtruth data of all robots and landmark features.

Finite-range sensing and communication are factors in the connectivity of a dynamic mobile-robot network. State estimation becomes a difficult problem when communication connections allowing information exchange between all robots are not guaranteed. This paper presents a decentralized state-estimation algorithm guaranteed to work in dynamic robot networks without connectivity requirements. We prove that a robot only needs to consider its own knowledge of network topology in order to produce an estimate equivalent to the centralized state estimate whenever possible while ensuring that the same can be performed by all other robots in the network. We prove certain properties of our technique and then it is validated through simulations. We present a comprehensive set of results, indicating the performance benefit in different network connectivity settings, as well as the scalability of our approach.

Finite-range sensing and communication are factors in the connectivity of a dynamic mobile robot network. State estimation becomes a difficult problem when communication connections for information exchange between all robots are not guaranteed. This paper presents a decentralized state estimation algorithm guaranteed to work in dynamic networks without connectivity requirements. We show that a robot only needs to consider its own knowledge of network topology in order to produce an estimate equivalent to the centralized state estimate whenever possible, while ensuring the same can be performed by all other robots in the network. Our technique is validated through simulations.

In this paper, an enhanced hybrid control algorithm is proposed to control the rotation of a flexible beam while suppressing the beam's vibration. The control law combines an enhanced PD feedback with nonlinear differentiator to derive high-quality velocity signal to control gross motion of the beam, and a vibration control by PZT actuators bonded on the surface of the beam. The significance of the proposed method are threefold: i) The enhanced PD control is a non-model based control, and appears to be more robust against the noise; ii) The linear velocity in contrast the angular velocity is used in the PZT actuator control, a signal which is easily available; iii) A unique solution is provided for examination of actuator placement, based on the analysis of mode shape functions. Experimental results validate these theoretical analyses.

In a practical controller design problem, several different performance requirements may be encountered together. The goal is to find a controller such that the multiple design specifications, which technically represent the requirements, can be met simultaneously. Such a control problem is called the multiple simultaneous specification (MSS) design problem. Many controller design approaches are proposed to improve the system performance. However, in the robot control area, there is no single design method that can treat a wide range of specifications simultaneously. This paper is concerned with controller design which solves the MSS problem. In this proposed convex combination method, the compromise solution is obtained by properly combining the existing controllers (or control techniques). The design strategy is straightforward and easily implemented. As an illustration, a robotic system is given as an example, and a set of specifications is simultaneously satisfied with the application of this proposed method.