Due to the complexity of urban and rural drainage systems, although many types of robots have been designed for this purpose, the mainstream pipeline inspection robots are currently dominated by four-wheeled designs. In this study, the shortcomings of four-wheeled pipeline robots were analyzed, including poor passability, difficulties in spatial positioning and orientation, and the limited effectiveness of conventional two-degree-of-freedom observation systems. Based on these issues, the spatial pose mathematical model of the four-wheeled robot inside the pipeline was investigated, along with the spatial geometric constraints and speed characteristics during cornering. This study was intended to reveal the spatial geometric parameter limitations and the kinematic characteristics of the four-wheeled pipeline robot under these constraints, providing corresponding recommendations. To address the issue of the outdated two-degree-of-freedom vision component, a three-degree-of-freedom visual component was designed, and forward kinematics analysis was conducted using Standard-Denavit-Hartenberg parametric modeling, revealing its motion speed and characteristics. Based on this visual component, a new concept of in-pipeline robot vision was proposed, providing new references for the design of four-wheeled pipeline robots.

This paper presents the design and experimental validation of a robust flight control strategy for quadrotor unmanned aerial vehicles (UAVs) based on the Interconnection and Damping Assignment Passivity-Based Control (IDA-PBC) methodology. The proposed approach is specifically tailored to the Parrot Bebop 2, a commercial UAV. The IDA-PBC control law is derived using the Hamiltonian model of the UAV dynamics obtained from experimental data to represent the dynamics of all six degrees of freedom, including translational and rotational motions. The control strategy was validated through numerical simulations and experimental tests conducted in an indoor flight setup using MATLAB, Robot Operating System, and an OptiTrack motion capture system. Numerical and experimental results demonstrate that the controller effectively tracks desired flight trajectories, ensuring stable and robust performance.

This paper presents a general approach to synthesizing closed-loop robots for machining and manufacturing of complex quadric surfaces, such as toruses, helicoids, and helical tubes. The proposed approach begins by employing finite screw theory to describe the motion sets generated by prismatic, rotational, and helical joints. Subsequently, generatrices and generating lines are put forward and combined for type synthesis of serial kinematic limbs capable of generating single-DoF translations along spatial curves and two-DoF translations on complex quadric surfaces. Following this manner, the two-DoF translational motion patterns on these complex quadric surfaces are algebraically defined and expressed as finite screw sets. Type synthesis of close-loop robots having the newly defined motion patterns can thus be carried out based upon analytical computations of finite screws. As application of the presented approach, closed-loop robots for machining toruses are synthesized, resulting in four-DoF and five-DoF standard and derived limbs together with their corresponding assembly conditions. Additionally, brief descriptions of robots for machining helicoids and helical tubes are provided, along with a comprehensive list of all the feasible limbs for these kinds of robots. The robots synthesized in this paper have promised applications in machining and manufacturing of spatial curves and surfaces, enabling precise control of machining trajectories ensured by mechanism structures and achieving high precision with low cost.

This article introduces a dome-type soft tactile sensor that can autonomously adjust its stiffness to evaluate surface contact characteristics, including the elastic modulus, contact force, and the presence of abnormal hardness within soft materials, using a strain gauge as a single sensing element. The strain sensor element is placed at the tip of the dome to measure the deformations during contact that reflect the properties of the contacted object. Using machine learning techniques, the sensor system can accurately predict these characteristics in various materials with an error rate of less than approximately 8%. A hybrid approach that combines experimental and simulation data enables the sensor to be trained effectively, generating sufficient data for accurate predictions without extensive experiments. The high accuracy results of the machine learning models demonstrate that the sensor system can precisely calculate the elastic modulus and depth of the defect. The adaptability and precision of the proposed sensor make it ideal for applications in medical diagnostics and other fields requiring careful interaction with soft materials. Furthermore, its innovative approach can be referenced for exploiting the properties of soft materials to achieve task-specific morphology without redesigning soft sensors or soft robots.

In recent years, there has been growing interest in developing robots and autonomous systems that can interact with human in a more natural and intuitive way. One of the key challenges in achieving this goal is to enable these systems to manipulate objects and tools in a manner that is similar to that of humans. In this paper, we propose a novel approach for learning human-style manipulation skills by using adversarial motion priors, which we name HMAMP. The approach leverages adversarial networks to model the complex dynamics of tool and object manipulation and the aim of the manipulation task. The discriminator is trained using a combination of real-world data and simulation data executed by the agent, which is designed to train a policy that generates realistic motion trajectories that match the statistical properties of human motion. We evaluated HMAMP on one challenging manipulation task: hammering, and the results indicate that HMAMP is capable of learning human-style manipulation skills that outperform current baseline methods. Additionally, we demonstrate that HMAMP has potential for real-world applications by performing real robot arm hammering tasks. In general, HMAMP represents a significant step towards developing robots and autonomous systems that can interact with humans in a more natural and intuitive way, by learning to manipulate tools and objects in a manner similar to how humans do.

The path navigation of robot in an entirely known space is presented by various researchers in the recent times. The navigational complexity arises when a robot moves in a completely unknown and complex environment from one defined start to a designated desired location. As the success of the nature-inspired algorithms in the unclear navigational problem is better, therefore, an improved butterfly optimization algorithm (IBOA) to determine the optimal feasible path for a humanoid robot navigating through a platform cluttered with both known and unfamiliar barriers is presented in this study. The BOA is inspired by the food-gathering habits of butterflies, where the sense of smell is the vital parameter in the global optimal search. However, the performance of this technique in the complex environment is poor, as a result, the chances of being trapped in local minima are more. Hence, the BOA is improved by using a nonlinear weight reduction strategy in updating the position of the butterflies in every iteration. The simulation is carried out in the Webots platform by considering variable-legged robot, NAO, in an unfamiliar environment. The outcomes derived from the simulation and real assessments demonstrate the potential of the proposed technique and compare with other existing algorithms, which highlights the potential and efficacy of the proposed IBOA algorithm.

This paper focuses on the feature-based visual-inertial odometry (VIO) in dynamic illumination environments. While the performance of most existing feature-based VIO methods is degraded by the dynamic illumination, which leads to unstable feature association, we propose a tightly-coupled VIO algorithm termed RAFT-VINS, integrating a Lite-RAFT tracker into the visual inertial navigation system (VINS). The key module of this odometry algorithm is a lightweight optical flow network designed for accurate feature tracking with real-time operation. It guarantees robust feature association in dynamic illumination environments and thereby ensures the performance of the odometry. Besides, to further improve the accuracy of the pose estimation, a moving consistency check strategy is developed in RAFT-VINS to identify and remove the outlier feature points. Meanwhile, a tightly-coupled optimization-based framework is employed to fuse IMU and visual measurements in the sliding window for efficient and accurate pose estimation. Through comprehensive experiments in the public datasets and real-world scenarios, the proposed RAFT-VINS is validated for its capacity to provide trustable pose estimates in challenging dynamic illumination environments. Our codes are open-sourced on https://github.com/USTC-AIS-Lab/RAFT-VINS.

In response to the auxiliary requirements for the treatment and prevention of lumbar diseases, based on the biomechanical characteristics of the human waist, a novel unpowered rigid-flexible coupling waist exoskeleton with multiple degrees of freedom and its human-exoskeleton parallel wearable equivalent research prototype are proposed, further focusing on the encompassing kinematic compatibility and dynamic load-bearing effectiveness of the biomimetic coordination, an in-depth analysis is performed on the multi-body dynamic dimensional synthesis and its methodological research. Initially, based on the rigid-flexible coupling characteristics and experimental biomechanical data of the lumbar region in the sagittal plane, an accurate multi-body system dynamics model of the research prototype, which incorporates the rigid-flexible coupling characteristics, is systematically constructed. Subsequently, to effectively quantify the biomimetic coordination of the exoskeleton, a novel comprehensive optimization index, termed biomimetic load-bearing comfort, is proposed. Finally, by utilizing this index, the exoskeleton is optimized in dimension by employing a thorough combination of multi-dimensional spatial search algorithm and compression factor particle swarm algorithm. The simulation results validate the correctness and effectiveness of both the dynamic dimensional synthesis and its methodology. Furthermore, the study also reveals that the optimized exoskeleton’s passive working mode showcases favorable biomimetic coordination. These results are crucial for progressing the research on the biomimetic load-bearing capacities of other exoskeletons.

Cable-driven parallel robots (CDPRs) have been widely used as motion executers for their large workspace and lower inertia. However, there are few studies on structural optimization design considering its stability. This paper proposes a stability optimization method based on force-position workspace for a reconfigurable cable-driven parallel robot (RCDPR). First, the structural optimization analysis of RCDPR is carried out. Then, the forces distribution algorithm based on the feasibility of real-time control is determined, and the boundary contour algorithm (BCA) of the RCDPR force feasible workspace (FFW) on the central plane is proposed. Second, the stiffness and cables driving force space (CFS) models of RCDPR are established. Subsequently, the stability evaluation function is established to optimize the structure of RCDPR, which uses FFW and main task feasible workspace (MFW) as carriers and stiffness and CFS as weights. Finally, an experimental prototype of the developed robot is constructed, and motion performance and workspace verification experiments are conducted. The results demonstrate that the developed RCDPR has good motion accuracy and stable workspace, and the results also verify the feasibility of the stability evaluation function and BCA.

Variable topological space robots are essential for providing adaptability and flexibility, enabling the robot to adjust its morphology to perform a range of tasks in the unstructured environment of space. However, impact is a common consequence of topology transformation in space robotics, which may lead to irreversible damage, such as the shedding of solid lubrication on joints. Nevertheless, determining the precise force-time relationships of such impacts poses significant challenges, especially when accounting for various connection mechanisms. In this work, a docking strategy that optimizes the manipulator’s joint angle configuration to minimize the impulse when the topology changes is proposed. First, an estimation technique is developed to quantify the impulse generated by topology transformation, employing spatial operator algebra and generalized momentum balance equations. Based on this model, the impulse minimization is modelled as a bilevel optimization problem, which decomposes a complex multipolar problem into two simpler subproblems. Although this optimization model may compromise computational efficiency, it increases the probability of achieving an optimal solution. To address this, a bilevel solution strategy based on a heuristic algorithm is proposed. In this framework, the lower level uses particle swarm optimization to determine the global optimum, while the upper level adopts simulated annealing to enhance computational speed. Finally, simulations are conducted to validate the proposed approach. Results demonstrate that the proposed method substantially reduces impulse.

Due to the effects of tolerance, design, and manufacturing deviations, there are clearances in the revolute joints of mechanical arms. These clearances can easily lead to system impacts and vibrations, resulting in a decrease in dynamic performance and affecting the trajectory tracking accuracy of the end effector. The existing dynamic models of mechanisms with clearance in revolute joints lack comprehensiveness, universality, and systematicity, and have not addressed the impact of joint reaction forces within clearance revolute joints on the system. The impact collision problem of the revolute joints with clearance was systematically, accurately, and comprehensively modeled and simulated in this study based on multibody dynamics theory. Based on Hertz’s elastic theory, the LuGre friction model, and joint reaction forces, this paper constructs constraint and mechanical models of revolute joints with clearance based on the theory of multibody dynamics. To facilitate multibody dynamics analysis, the collision impact direction matrix is proposed and used for the first time to transform the mechanical model of revolute joints with clearance into external forces. The dynamic models of mobile parallel and double serial manipulators are then constructed. Through numerical simulations on different clearance amounts, tracking trajectories, and load parameters, the impact of revolute joint clearances on system dynamic performance is analyzed. The engineering significance of this research in dynamic analysis of mobile parallel manipulators under imperfect revolute joint conditions is also discussed.

This research proposes an adaptive human-robot interaction (HRI) that combines voice recognition, emotional context detection, decision-making, and self-learning. The aim is to overcome challenges in dynamic and noisy environments while achieving real-time and scalable performance. The architecture is based on a three-stage HRI system: voice input acquisition, feature extraction, and adaptive decision-making. For voice recognition, modern pre-processing techniques and mel-frequency cepstral coefficients are used to robustly implement the commands. Emotional context detection is governed by neural network classification on pitch, energy, and jitter features. Decision-making uses reinforcement learning where actions are taken and then the user is prompted to provide feedback that serves as a basis for re-evaluation. Iterative self-learning mechanisms are included, thereby increasing the adaptability as stored patterns and policies are updated dynamically. The experimental results show substantial improvements in recognition accuracy along with task success rates and emotional detection. The proposed system achieved 95% accuracy and a task success rate of 96%, even against challenging noise conditions. It is apparent that emotional detection achieves a high F1-score of 92%. Real-world validation showed the system’s ability to dynamically adapt, thus mitigating 15% latency through self-learning. The proposed system has potential applications in assistive robotics, interactive learning systems, and smart environments, addressing scalability and adaptability for real-world deployment. Novel contributions to adaptive HRI arise from the integration of voice recognition, emotional context detection, and self-learning mechanisms. The findings act as a bridge between the theoretical advancements and the practical utility of further system improvements in human-robot collaboration.

This paper introduces a distributed online learning coverage control algorithm based on sparse Gaussian process regression for addressing the problem of multi-robot area coverage and source localization in unknown environments. Considering the limitations of traditional Gaussian process regression in handling large datasets, this study employs multiple robots to explore the task area to gather environmental information and approximate the posterior distribution of the model using variational free energy methods, which serves as the input for the centroid Voronoi tessellation algorithm. Additionally, taking into consideration the localization errors, and the impact of obstacles, buffer factors and centroid Voronoi tessellation algorithms with separating hyperplanes are introduced for dynamic robot task area planning, ultimately achieving autonomous online decision-making and optimal coverage. Simulation results demonstrate that the proposed algorithm ensures the safety of multi-robot formations, exhibits higher iteration speed, and improves source localization accuracy, highlighting the effectiveness of model enhancements.

Parallel manipulators (PMs) are adopted in different fields due to their superior characteristics compared to serial manipulators. PMs with flexible links are likely more energy efficient and have high dynamic performance since they are lighter than those with rigid links. On the other hand, due to their lightweight design, the flexibility can lead to undesired deformation and vibration, decreasing the tracking trajectory and transient errors. This work proposes a two-loop active vibration control strategy, using strain gauges and piezoelectric lead zirconate (PZT) actuators, to compensate for the undesired effect of the flexibility. A pose control loop exploits the sliding mode control using data collected from images acquired by an oCam-5CRO-U camera, while the active vibration control loop uses strain gauge sensors and PZT actuators. Strain gauges are responsible for measuring the deformation of each link, and after being treated by digital filters, these signals are applied to the PZT actuator. Combining both loops allows the manipulator to be guided over the desired trajectory with positive vibration attenuation. The results reveal that the presence of the PZT on both sides of the flexible links increases the links’ rigidity, yielding overshoot and vibration reduction during the manipulator’s motion. In addition, the maximum peak is significantly attenuated, and the overall oscillations are also positively reduced when using the two-loop active control strategy. The root mean square error quantifies this attenuation, showing an average reduction of 30% in the corresponding step input directions. Therefore, the proposal improves the system performance by enhancing the tracking trajectory with lower vibrations.

Locomotion control of inchworm robots presents significant challenges due to their highly nonlinear dynamics and complex interactions with the environment. Traditional control methods often struggle with achieving precise tracking and adaptive performance in dynamic conditions. To address these limitations, this article proposes a novel data-driven compound control system that integrates fractional proportional-integral derivative (FPID) control with Koopman operator theory. Unlike conventional approaches, which rely on direct nonlinear control or simplified linear approximations, our method leverages data-driven modeling to transform the nonlinear dynamics into a linear representation, making control design more systematic and scalable. A deep neural network is trained to identify the Koopman operator, enabling an FPID controller to operate within this transformed space for improved tracking accuracy and robustness. The proposed framework is validated using NVIDIA Isaac SIM simulation software, demonstrating superior locomotion efficiency and tracking performance compared to existing control strategies. This study advances the control of bio-inspired robots by bridging fractional-order control with data-driven Koopman-based modeling, addressing the fundamental challenge of achieving high-precision locomotion in complex environments.

Connecting individual robots to form an inter-reconfigurable system with a flexible base size enhances the ability to access and cover areas for cleaning and maintenance tasks. Given that increased configuration complexity expands the search space dimension, an optimal routing solution ensuring efficiency is essential. In this paper, we present an inter-reconfigurable multi-robot system capable of adjusting the bases of its two units, along with an optimal path planning approach for confined spaces based on a modified informed rapidly-exploring random tree algorithm by a greedy set (RIRRT*). We validate the navigation of the proposed inter-reconfigurable platform using RIRRT* for four informed dimensional search spaces as a case study in both simulated and real-world environments. The proposed path planning method for the inter-reconfigurable system outperformed conventional strategies, achieving significant reduction in both execution time and energy utilization.

The autonomous safe flight of fixed-wing unmanned aerial vehicles (UAVs in complex low-altitude environments presents significant challenges and holds practical application value. This paper proposes a motion planning method for agile fixed-wing UAVs to address safety issues in navigating narrow corridors within such environments. In the path planning phase, we introduce the Improved Batch Informed Trees (IBIT*) to enhance both the solving speed and quality of BIT*. The IBIT* incorporates strategies such as using Rapidly Exploring Random Tree (RRT)-Connect for initial pathfinding, informed sparse sampling, and re-selecting parent nodes. During the trajectory planning phase, we first decouple the roll angle of the UAV from its three-dimensional position based on the agility of fixed-wing UAVs; subsequently, we address constraints related to smoothness and mission time by leveraging the characteristics of the Minimum Control Effort; finally, we design a differentiable penalty function to satisfy the dynamic performance constraints of the UAV. The effectiveness and superiority of the proposed motion planning method are demonstrated through numerical simulations and physical flight experiments.

Outdoor mobile robots must navigate uneven terrains with obstacles that sometimes cannot be avoided; therefore, strategies have been developed for robots to overcome them. In most cases, these strategies have been modeled considering movement over horizontal surfaces and with the robot positioned directly in front of the obstacle, but this idealization does not occur in most real cases. Therefore, this article describes a strategy for obstacle overcoming, useful when the robot faces obstacles on inclined terrains and at an oblique angle relative to the robot’s trajectory, considering rollover stability during the process, based on the reaction force criterion. This strategy can be used by mobile robots with wheels and an articulated arm whose end effector can contact the ground, and it consists of a sequence of standard movements that include the use of the arm, whose variable location was defined through a system developed using fuzzy logic. The designed strategy was validated through simulations and then implemented on the Lázaro robot, verifying its effectiveness through experimental tests. With it, the robot can overcome obstacles such as steps, ramps, and ditches from any position; additionally, it increased the ability to overcome obstacles with a height close to twice the radius of the robot’s wheels.

The robot manipulator is commonly employed in the space station experiment cabinet for the disinfection task. The challenge lies in devising a motion trajectory for the robot manipulator that satisfies both performance criteria and constraints within the confined space of an experimental cabinet. To address this issue, this paper proposes a trajectory planning method in joint space. This method constructs the optimal trajectory by transforming the original problem into a constrained multi-objective optimization problem. This is then solved and integrated with the seventh-degree B-spline curve. The optimization algorithm utilizes an indicator-based adaptive differential evolution algorithm, enhanced with improved Tent chaotic mapping and opposition-based learning for population initialization. The method employed the Fréchet distance to design a trajectory selection strategy based on the Pareto solutions to ensure that the planned trajectory complies with Cartesian space requirements. This allows the robot manipulator end-effector to approximate the desired path in Cartesian space closely. The findings indicate that the proposed method can effectively design the robot manipulator trajectory, considering both joint motion performance and end-effector motion constraints. This ensures that the robot manipulator operates efficiently and safely within the experimental cabinet.

Traditional path planning algorithms often encounter challenges in complex dynamic environments, including local optima, excessive path lengths, and inadequate dynamic obstacle avoidance. Thus, the development of innovative path planning algorithms is essential. This article addresses the challenges of mobile robot path planning in complex environments, where traditional methods often converge to local optima, leading to suboptimal path lengths, and struggle with dynamic obstacle avoidance. To overcome these limitations, we propose an integrated algorithm, the enhanced sparrow search algorithm combined with the dynamic window approach (ESSA-DWA). The algorithm first utilizes ESSA for global path planning, followed by local path planning facilitated by the DWA. Specifically, ESSA incorporates Tent chaotic initialization to enhance population diversity, effectively mitigating the risk of premature convergence to local optima. Moreover, dynamic adjustments to the inertia weight during the search process enable an adaptive balance between exploration and exploitation. The integration of a local search strategy further refines individual updates, thereby improving local search performance. To enhance path smoothness, the Floyd algorithm is employed for path optimization, ensuring a more continuous trajectory. Finally, the combination of ESSA and DWA uses key nodes from the global path generated by ESSA as reference points for the local planning process of DWA. This approach ensures that the local path closely follows the global path while also enabling real-time dynamic obstacle detection and avoidance. The effectiveness of the algorithm has been validated through both simulations and practical experiments, offering an efficient and viable solution to the path planning problem.