For the collaborative formation control problem of a multi-snake robot with uncertain sideslip and lumped unknowns, this study proposed collaborative line-of-sight (LOS) guidance and consensus-based path variable, which enables snake robots to track multiple parametric paths in a fixed formation. Meanwhile, the improved finite-time sideslip observer effectively compensates for the kinematic deviation, thereby improving the accuracy of the multi-snake robot’s path tracking. A robust adaptive controller based on an integral sliding mode manifold is developed to eliminate external disturbances. The model uncertainty and time-varying disturbances are comprehensively involved and incorporated as lumped unknowns in dynamics. The prediction-based RBF neural network estimators are derived to approximate unknowns and compensate controllers. The Lyapunov theory is adopted to prove the stability of formation errors for snake robots in the closed-loop system. Simulation and experiment results show the practicability and superiority of this work.Note to Practitioners—This paper is mainly motivated to study a cooperative formation control framework suitable for snake robots’ dynamics, especially for the application of resisting model uncertainty and external interference. In practice, the collaborative line of sight guidance law is employed to generate and adjust the reference direction and speed of robots in real time, and then the method is applied to the joint torque controller to guide robots to track the desired parameterized path. Meanwhile, the improved finite time sideslip observer corrects the deviation of the guidance law, ensuring the observation accuracy while reducing the computational time. During the control process, for highly coupled and complex snake robot models, solving external disturbances and model uncertainties remains a challenge. A neural network based state estimator is used to handle these unknowns to pre-compensate for underrotation/overrotation of the joint steering gear when disturbed. Simulation and experimental results validate that the proposed scheme can enable multiple snake robots to synergistically perform tasks, expressing high potential in unknown environments. In the future, we plan to design a three-dimensional spiral gait instead of the two-dimensional one used in this study. With this change, we will further improve the usability of snake robots in real-world scenarios.

Current multi-modal hybrid robots with flight and wheeled modes have fallen into the dilemma that they can only avoid obstacles by re-taking off when encountering obstacles due to the poor performance of wheeled obstacle-crossing. To tackle this problem, this paper presents a novel multi-modal hybrid robot with the ability to actively adjust the wheel’s size, which is inspired by the behavior of the turtle’s legs when it encounters obstacles, to enhance the traversal performance. In detail, we describe the hardware design that allows the robot to achieve a modal switch between flight and wheeled modes through foldable structures and variable wheel diameters; then, we present the architecture to control these two morphing mechanisms. After that, we establish the theoretical kinematic models for both the foldable arm and variable wheel and carry out extensive experiments to test the performance of the foldable arm, the variable-diameter wheel, as well as the traversal performance of the robot. Experimental results show that the proposed multimodal robot can realize the function of a quadrotor, respond quickly with full-scale folding within 0.9 s, climb a maximum slope of 36°, and traverse narrow passageways, which exhibit superior mobility and environmental adaptability.

Reinforcement learning tasks that involve visual input continue to pose a challenge when it comes to partial observability problems. Although prior research has introduced methods such as LSTM, GTrXL, and DNC, each of these approaches possesses its own limitations. To address the problem of partial observability in a more universal context, this paper proposed the Observation-Time-Action deep stacking algorithm. First, observations, actions, and time data were combined into a tuple and then stacked into a longer sequence. Then, convolution and fully connected layers were utilized to extract relevant features from the sequence, which were then fed into the algorithm for processing. We designed a number of experiments with partial observability, corresponding to different typical scenarios in reinforcement learning. The experiment results demonstrated that the proposed method obtained a higher success rate. Moreover, we also investigated the effect of stacking frame length and different reinforcement learning elements on the algorithm. Finally, we conducted a HITL (Hardware-in-the-Loop) experiment to further verify the effectiveness of our algorithm.

This paper investigates online trajectory planning technology for unmanned aerial vehicles (UAVs) in complex environments, focusing on dynamic target tracking tasks commonly encountered in peacekeeping and counter-terrorism fields. The proposed method addresses the challenges posed by obstacles and high dynamics of targets in mountainous or urban settings, which may lead to sensor failures and target loss. Utilizing differential evolution, the method optimizes for UAV stability, safety, and line-of-sight, crafting a finite-jump trajectory. Simulations in 3D mountainous scenarios confirm the UAV's ability to track, evade obstacles, and maintain sight, meeting jerk constraints for stable and safe flight.

Object detectors often struggle with accuracy and generalization when applied to aerial imagery, primarily due to the following challenges: 1) great scale variation of objects in aerial images: both extremely small and large objects are visible in the same image; and 2) an extreme imbalance of the training sample between positive and negative anchors: there are several positive ground truth (GT) anchors and an abundance of negative anchors. In this article, we propose a dynamic training sample selection network (DTSSNet) to solve the above-mentioned problems in two dimensions. An attention-enhanced feature module (AEFM) is proposed to enhance the basic features by focusing on both channel and semantic information related to targets. This module provides more valuable information for accurately classifying objects of different scales. To tackle the imbalance in training samples, this article implements a dynamic training sample selection (DTSS) module that divides the training samples based on GT information. This module dynamically selects samples, ensuring a more balanced representation of positive and negative anchors, leading to improved learning. Importantly, the combination of AEFM and DTSS does not introduce any additional computational costs. Experimental evaluations on the VisDrone2019-DET dataset demonstrate that DTSSNet outperforms base detectors and generic approaches. Furthermore, the effectiveness of DTSSNet is validated on the UAVDT benchmark dataset, where it achieves state-of-the-art performance.

This article reports a trajectory tracking control technique for snake robots with sideslip elimination and coefficient approximation. By introducing an integral part and virtual input variables to optimize the line-of-sight guidance law, a closed-loop trajectory tracking system with the functions of canceling disturbance and sideslip is designed. Besides, the method constructs the time-varying predicted values of virtual model variables and viscous friction coefficients to approximate the system's unmeasurable states. The approximation value can compensate for a snake robot's joint offset and torque input. Then, it is proved via the Lyapunov approach that the designed system is stable. The remarkable advantage of this strategy is that the accuracy of a snake robot tracking the ideal trajectory is optimized, which can improve the error's stability and the body's adaptability to the surroundings. The simulation and experimental results confirm the usefulness of the proposed technique.

This article studies the formation and trajectory tracking control of multiagent systems. We present a novel multilayer graph for the multiagent system to enable extensibility of the interaction network. Based on the multilayer graph, a formation control law by using the potential function approach is developed for autonomous formation, formation maintenance, collision, and obstacle avoidance. When the desired formation is achieved, the barycentric of the formation shape is viewed as a virtual leader, and a model predictive control (MPC) scheme is applied to the virtual leader for tracking a reference trajectory; meanwhile, the agents will maintain the desired angles and distances via the formation control law. By applying the proposed schemes, the tasks of formation maintenance and trajectory tracking in a constrained space are fulfilled. Comprehensive simulation studies under different environmental constraints and trajectories confirm the effectiveness of the proposed approaches in addressing the formation and trajectory tracking problems.

The paper presents the development of a novel deformable quad-rotor enabled aerial gripper. The mechanism of our deformable quad-rotor is based on simultaneous expansion or contraction of the quad-rotor body, which is generated by controlling a rigid elements based morphing structure (REMS). Such deformation results in a highly deformable quad-rotor that can not only perform morphological adaptation in response to environmental changes and obstacles, but also improve the flight performance by contracting to facilitate the agility/maneuverability or by expanding to enhance the stability. Meanwhile, inspired by the wasp grasping behavior, such controllable expansion and contraction from the REMS ingeniously enable a new function of aerial gripper. In this paper, we start to detail the mechanism and design of the REMS based deformable quad-rotor, then present the quad-rotor deformation enabled aerial gripper design, its dynamics modeling, the grasping function and analysis. The simulation was conducted in order to graphically show the elicited aerodynamic flow situation during expansion or contraction of the quad-rotor with and without carrying payload. Experiments were further implemented to validate the grasping function of the gripper and the flight performance of the quad-rotor. Finally, two case studies on the new aerial gripper were performed. All results demonstrate the excellent performance of the deformable quad-rotor enabled aerial gripper, that is, it has the advantages of both flight maneuverability and grasping capability during performing tasks.

To improve the obstacle surmounting performance of the quad-rotor vehicle, this paper focuses on designing, kinematically and dynamically characterizing a novel deformable quad-rotor that is based on the scissor-like foldable structures. The foldable structure allows that the volume of the quad-rotor can be tuned to dynamically adapt variously sized obstacles and small spaces. To generate the controllable deformation, the actuated angulated elements that are the essential components of the scissor-like foldable structure play an important role. The element design, its actuation mechanism and the corresponding configuration patterns for the new quad-rotor are presented in the paper in detail. The simulations on deformation properties and obstacle surmounting ability are then performed to verify the deformation capability of the structure. In addition, experiments were extensively conducted to test the controlled deformation of the structure as well to investigate the deformation induced effects to the activated quad-rotor airframe and its aerodynamics. All implementation results validate the effectiveness of the proposed deformable quad-rotor design, that is, it enables the new quad-rotor having excellent obstacle surmounting performance, adaptability, flight maneuverability, as well as minimal aerodynamics influences during deforming.

In this paper, first of all, the existing localization algorithms of planetary rover are summarized. And then, the RCM (RSSI and Circle centre Mixed localization) Algorithm and its theoretical model, which mixed RSSI (Received Signal Strengt Indicator) Algorithm and the Circle of contact-Center Algorithm, are proposed based on the technology of wireless sensor network. The simulation results show the validity of RCM Algorithm which lowers the localization error down to 10% while the error spreading of RSSI Algorithm is even up to 50%.

In this paper, an image stabilization method based on the adaptive Gaussian mixture model (GMM) is presented in order to smooth down the airborne image vibration. Firstly, the projection algorithm is adopted for the motion estimation; Secondly, GMM parameter is obtained after analyzing characteristics of the first n images; Finally, a stable image sequence is achieved after GMM motion filter operates on the motion parameter. The experimental results show that the method has the advantage of fast speed and effectively smooth unwanted vibration of image sequences.

This paper proposes a new method to extract multilingual text in images through discriminating characters from non-characters based on the Gaussian mixture modeling of neighbor characters. The image is binarized and the morphological closing operation is performed on the binary image, in order that each character in it can be treated as a connected component; the neighborhood of connected components are computed based on the Voronoi partition of the image, and each connected component is labeled as character or non-character according to its neighbors. We applied the proposed text extraction method to Chinese and English text extraction, the effectiveness of which is confirmed by the experimental results.

This paper proposes a walk structure consisting of a dynamic pattern, a sensory reflex and a motion adjustment. The dynamic pattern is generated off-line based on the constraint of dynamic stability, assuming that the models of the humanoid and the environment are known. The sensory reflex is simple, but rapid motion programmed in respect to sensory information. The sensory reflex increases the humanoid adaptability to environmental uncertainties, but it may conflict with humanoid its constraints. To solve this problem, the walking constraints violated easily by the sensory reflex are formulated, and the method to coordinate the dynamic pattern and the sensory reflex through the motion adjustment is present. The effectiveness was confirmed by walk experiments of our developed 31 DOF humanoid.