This letter proposes a novel scheme for resilient estimation under random sensor failures and colored measurement noise, without requiring hardware-redundant designs or complex adaptive mechanisms. The key insight is to leverage prior constraints to increase information redundancy. Resilience against both disturbances is achieved through a Kalman-Petovello filter, which is derived by whitening the colored noise via measurement differencing and incorporating the prior constraints as pseudo measurements. Theoretical analysis and numerical simulations demonstrate the efficacy of the proposed scheme.

Motivated by the practical challenges of tethered satellite formations (TSFs), this article focuses on the state estimation problem for systems with limited payload capacity, subject to probabilistic sensor failures and complex soft constraints. As prior knowledge, soft constraints reveal the interdependence of internal states, providing insights into observability preservation under probabilistic sensor failures. Unlike existing approaches, we propose a soft-constrained estimation scheme that fully leverages prior constraint knowledge to preserve observability and enhance performance via a constrained particle filter (CPF). Within the Bayesian framework, the CPF fully leverages the soft constraints to truncate both the prior and posterior distributions. The convergence analysis is also presented. Based on this scheme, we investigate the maximum tolerable sensor failures for TSF. Surprisingly, it is proven thatn-body TSF ($n \ge 3$) with typical configurations can tolerate up ton-1 positioning sensor failures. This proof enables mission designers to sustain system observability even with up ton-1 sensor failures, thereby obviating redundant configurations while ensuring orbital mission reliability. Extensive simulations validate the effectiveness of the proposed scheme and its filter performance.

Event cameras, when combined with inertial sensors, show significant potential for motion estimation in challenging scenarios, such as high-speed maneuvers and low-light environments. While numerous methods exist for producing such estimations, most boil down to solving a synchronous discrete-time fusion problem. However, the asynchronous nature of event cameras and their unique fusion mechanism with inertial sensors remain underexplored. In this article, we introduce a monocular event-inertial odometry method called asynchronous event-inertial odometry (AsynEIO), designed to fuse asynchronous event and inertial data within a unified Gaussian process (GP) regression framework. Our approach incorporates an event-driven front-end that tracks feature trajectories directly from raw event streams at a high temporal resolution. These tracked feature trajectories, along with various inertial factors, are integrated into the same GP regression framework to enable asynchronous fusion. With deriving analytical residual Jacobians and noise models, our method constructs a factor graph that is iteratively optimized and pruned using a sliding-window optimizer. Comparative assessments highlight the performance of different inertial fusion strategies, suggesting optimal choices for varying conditions. Experimental results on both public datasets and our own event-inertial sequences indicate that AsynEIO outperforms existing methods, especially in high-speed and low-illumination scenarios.

This article presents a passivity-based shared control for a multirobot teleoperation system to perform extravehicular assembly tasks. At the remote site, an eye-in-hand camera is integrated with three manipulators that cooperatively drive a customized tool. A haptic device at the local site allows a human operator to intervene when necessary via bilateral teleoperation. To enable intuitive user control, a homography-based method seamlessly integrates visual servoing and operator input to guide the remote camera. To accommodate different peg geometries and enable adaptive tool actuation, a partial pose synchronization method decouples roll from the other pose dimensions. It allows each manipulator to independently actuate a gripper by rotating its end-effector frame around the roll axis, without interfering with cooperative motion in position, pitch, and yaw. In addition, to minimize undesired internal forces, we formulate and solve a passivity-constrained interaction wrench optimization problem that enhances stability. The proposed control ensures smooth transitions between autonomous and teleoperated modes, supporting flexible human intervention. Theoretical analysis and experimental validation confirm the system’s stability and effectiveness in achieving precise, robust, and responsive multirobot teleoperation for complex space assembly tasks.

Recent works have combined monocular event camera and inertial measurement unit to estimate the SE(3) trajectory. However, the asynchronicity of event cameras brings a great challenge to conventional fusion algorithms. In this paper, we present an asynchronous event-inertial odometry under a unified Gaussian Process (GP) regression framework to naturally fuse asynchronous data associations and inertial measurements. A GP latent variable model is leveraged to build data-driven motion prior and acquire the analytical integration capacity. Then, asynchronous event-based feature associations and integral pseudo measurements are tightly coupled using the same GP framework. Subsequently, this fusion estimation problem is solved by underlying factor graph in a sliding-window manner. With consideration of sparsity, those historical states are marginalized orderly. A twin system is also designed for comparison, where the traditional inertial preintegration scheme is embedded in the GP-based framework to replace the GP latent variable model. Evaluations on public event-inertial datasets demonstrate the validity of both systems. Comparison experiments show competitive precision compared to the state-of-the-art synchronous scheme.

Robotic manipulation holds the potential to replace humans in the execution of tedious or dangerous tasks. However, control-based approaches are not suitable due to the difficulty of formally describing open-world manipulation in reality, and the inefficiency of existing learning methods. Therefore, applying manipulation in a wide range of scenarios presents significant challenges. In this study, we propose a novel framework for skill learning in robotic manipulation called Tactile Active Inference Reinforcement Learning (TactileAIRL), aimed at achieving efficient learning. To enhance the performance of reinforcement learning (RL), we introduce active inference, which integrates model-based techniques and intrinsic curiosity into the RL process. This integration improves the algorithm’s training efficiency and adaptability to sparse rewards. Additionally, we have designed universal tactile static and dynamic features based on vision-based tactile sensors, making our framework scalable to many manipulation tasks learning involving tactile feedback. Simulation results demonstrate that our method achieves significantly high training efficiency in objects pushing tasks. It enables agents to excel in both dense and sparse reward tasks with just few interaction episodes, surpassing the SAC baseline. Furthermore, we conduct physical experiments on a gripper screwing task using our method, which showcases the algorithm’s rapid learning capability and its potential for practical applications.

This paper proposes a distributed on-orbit spacecraft assembly algorithm, where future spacecraft can assemble modules with different functions on orbit to form a spacecraft structure with specific functions. This form of spacecraft organization has the advantages of reconfigurability, fast mission response and easy maintenance. Reasonable and efficient on-orbit self-reconfiguration algorithms play a crucial role in realizing the benefits of distributed spacecraft. This paper adopts the framework of imitation learning combined with reinforcement learning for strategy learning of module handling order. A robot arm motion algorithm is then designed to execute the handling sequence. We achieve the self-reconfiguration handling task by creating a map on the surface of the module, completing the path point planning of the robotic arm using A*. The joint planning of the robotic arm is then accomplished through forward and reverse kinematics. Finally, the results are presented in Unity3D.

The postcapture of a partially failed spacecraft by space manipulators will bring a mutation in the dynamics of the combination. Both the inertia properties and the thruster configuration matrix will change significantly. The unknown dynamics of the partially failed spacecraft also cause a tremendous technical challenge for attitude takeover control. Accordingly, this paper describes a novel reconfigurable control system for postcapture attitude takeover of a partially failed spacecraft with parametric uncertainties, whose fuel has been exhausted or whose actuators have partial malfunctions. First, the reconfigurable control law is designed by command filtering adaptive back-stepping control to guarantee the system performance and global asymptotic stability considering inertia parametric uncertainties. Second, the thrusters are reconstituted, without changing the thruster physical configuration. Finally, the thrusters' forces can be redistributed by the dynamic control reallocation method based on constrained quadratic programing. Numerical simulations validate the feasibility of the proposed approach for postcapture attitude takeover control of a partially failed spacecraft with parametric uncertainties. Note to Practitioners-This paper presents a methodology for a partially failed spacecraft with parametric uncertainties. It focuses on solving the attitude takeover control of the partially failed spacecraft perfectly, while considering the position and speed constraints of the actuator and avoiding the plume impact to the spacecraft. The proposed command filtering adaptive back-stepping control can be used for spacecraft's thruster reconfiguration considering the parametric uncertainties. Furthermore, the dynamic control reallocation method is particularly useful for future applications that include spacecraft with redundant actuators.

We present trajectory tracking and balance control of an autonomous bikebot. The bikebot is a single-track autonomous mobile robot that is designed to study unstable physical human-robot interactions. The controller is built on the property of the external-internal convertible (EIC) structure for the bikebot dynamics. We present two types of the designs and analyses of the control systems and also demonstrate their performance through extensive experiments. The comparison results with the human riding experiments show that the rider's motor skills generate the similar strategies as the proposed EIC-based balance control.

Facing the new challenges of the spacecraft developing, the concept of cellular space robot (CSR) for both space-craft system construction and on-orbit operation is presented in this paper. The system description and design principles are introduced to ensure the flexibility of the system. And dynamics model for takeover control is developed. After that, the regression models for the parameter identification are deduced based on the dynamics model. An interaction model identification algorithm is presented to solve the parameter identification problem for the distributed cells. Besides, the interactive model identification is validated by simulations. The simulations show that the interactive model identification method can achieve the consensus and convergence.

We present a drift-free pose estimation scheme for rigid body and its supporting platform by fusing only two gyroscopes and the relative complementary measurements. The fusion design not only provides robust relative attitude estimation between the rigid body and the platform, but also is capable of identifying partial global absolute attitudes without capturing any absolute attitude information. The pose estimation is built on a special design of the coupled kinematic model with the relative measurements between the rigid body and its supporting platform. We compare the fusion design with an alternative kinematic model and the posterior Cramer-Rao bound analyses are presented to show the completely different estimation performances. An extended Kalman filter (EKF) implementation of the fusion design is presented for the bicycle riding application.

Slip is the major cause of falls in human locomotion. We present a new bipedal modeling approach to capture and predict human walking locomotion with slips. Compared with the existing bipedal models, the proposed slip walking model includes the human foot rolling effects, the existence of the double-stance gait and active ankle joints. One of the major developments is the relaxation of the nonslip assumption that is used in the existing bipedal models. We conduct extensive experiments to optimize the gait profile parameters and to validate the proposed walking model with slips. The experimental results demonstrate that the model successfully predicts the human recovery gaits with slips.

We present the development of the gyroscopic-balanced control of an autonomous bikebot. The bikebot is an actively controlled bicycle-based robotic platform with a gyro-balancer developed to study human dynamic postural balance motor skills through unstable physical human-robot interactions. We also present a dynamic model and analysis for stationary bikebot. A nonlinear balancing controller is designed to stabilize the underactuated stationary bikebot on an orbital trajectory around the unstable equilibrium point that is coupled with another orbit of the actuated gyro-balancer. We then demonstrate the analysis and control design with experimental validations. Finally, we present a set of human riding experiments to show how the bikebot can be used to perturb and excite human sensorimotor feedback loop for dynamic postural balance motor skills.

We report the development of a human whole-body pose estimation scheme with application to rider-bicycle interactions. The estimation scheme is built on the fusion of measurements of a monocular camera on the bicycle and a set of small wearable gyroscopes attached to the rider's upper- and lower-limb and the trunk. A single feature point is collocated with each wearable gyroscope and also on the segment link where the gyroscope is not attached. An extended Kalman filter is designed to fuse the vision-inertial measurements to obtain accurate whole-body poses. The estimation design also incorporates a set of constraints from human anatomy and the physical rider-bicycle interactions. We demonstrate and compare the performance of the estimation design through multiple subjects riding experiments.

In this study, the authors present a novel low backhaul load cooperative transmission framework for the two‐cell multiple‐input–single‐output systems. In this framework, the neighbouring two base stations (BSs) take turns to transmit data in two consecutive slots. In each slot, only one BS is active, transmitting the preprocessed data symbols of both its own serving user and the cooperative user in neighbouring cell, and sharing its preprocessed data symbols to the other cooperative BS for next transmission. Linear constellation spreading is utilised for preprocessing which helps the system to exploit the macro‐diversity without reducing the multiplexing gain. Besides, zero‐forcing beamforming is applied in each transmission slot so as to cancel the multiuser interference. In this way, the inter‐cell links become beneficial rather than detrimental. Pairwise error probability analysis demonstrates that the multi‐cell spatial diversity gain can be achieved for each data stream. Both theoretical analysis and simulation results confirm that the proposed scheme outperforms the existing relevant strategies with less channel estimation overhead. It is shown that because of the higher diversity order it achieved, the proposed scheme can significantly improve the error performance in a distributed manner while maintaining the same multiplexing gain.

We present balancing control analysis of a stationary riderless motorcycle. We first present the motorcycle dynamics with an accurate steering mechanism model with consideration of lateral movement of the tire/ground contact point. A nonlinear balance controller is then designed. We estimate the domain of attraction (DOA) of motorcycle dynamics under which the stationary motorcycle can be stabilized by steering. For a typical motorcycle/bicycle configuration, we find that the DOA is relatively small and thus balancing control by only steering at stationary is challenging. The balance control and DOA estimation schemes are validated by experiments conducted on the Rutgers autonomous motorcycle. The attitudes of the motorcycle platform are obtained by a novel estimation scheme that fuses measurements from global positioning systems (GPS) and inertial measurement units (IMU). We also present the experiments of the GPS/IMU-based attitude estimation scheme in the paper.