Search Results (159)

Global path planning for autonomous orchard robots must balance obstacle clearance, path smoothness, and trajectory trackability. This balance is difficult to achieve in unstructured orchards, where irregular tree rows, scattered trunks and ground obstacles, and narrow inter-row passages can cause conventional planners to generate low-clearance paths with frequent local turns. This study proposes SDF-Theta*, a safety- and smoothness-aware global path planning framework for orchard robots in unstructured environments. The framework constructs a Euclidean signed distance field (ESDF) from a two-dimensional occupancy planning map and defines a safe navigable domain using the robot width and a grid-discretization approximation margin. Within this domain, the bidirectional SDF-Theta* search performs candidate-node screening, applies Safe LOS checks along candidate connection segments, and uses safety–geometry multi-criteria state selection based on minimum clearance, mean clearance, path length, and turning cost. The resulting initial discrete path is processed through path skeleton refinement and local Bézier curve smoothing. Differential-flatness-based time parameterization then converts the smoothed geometric path into a time-indexed motion reference. In the Orchard Field Experiment, SDF-Theta* increased the minimum obstacle clearance by 16.6% compared with Theta* (ESDF) and achieved a safe path ratio of 100.00%. It also reduced the 99th-percentile curvature, maximum curvature, and total curvature variation by 54.3%, 85.5%, and 82.1%, respectively. Trajectory Tracking Validation yielded a path-overlap ratio of 94.65% at a nearest-path distance threshold of 0.03 m, with no physical collision or map-boundary violation. These results show that SDF-Theta* improved path safety and geometric smoothness and demonstrated trajectory trackability under the tested orchard conditions. Full article

This paper presents a Neural Backstepping control strategy for trajectory tracking of a differential-drive mobile robot. The proposed approach combines a dynamic-level backstepping controller with a lightweight single-hidden-layer adaptive neural network to compensate uncertain nonlinear dynamics through online adaptation. The backstepping component provides a Lyapunov-based stabilizing structure, whereas the neural approximator improves tracking performance without requiring deep architectures, offline training stages, or computationally demanding optimization procedures. The adaptive law for the neural output weights is derived from the stability analysis, ensuring bounded closed-loop signals and uniformly ultimately bounded tracking errors in the presence of bounded approximation uncertainties. The controller is evaluated through simulations using four reference trajectories: circular, lemniscate, Lissajous, and waypoint-based paths. The same control gains and neural network configuration are used in all cases, showing that the proposed scheme can track different trajectory geometries without trajectory-specific retuning. The simulation results show satisfactory tracking performance, with position RMSE values below $0.04$ m for all evaluated trajectories. These results indicate that the proposed Neural Backstepping controller provides a suitable balance between tracking accuracy, online adaptation capability, and implementation simplicity for differential-drive mobile robot trajectory tracking. Full article

To address high-precision trajectory tracking challenges in multi-joint robots facing model uncertainties, complex friction, and abrupt disturbances, this paper proposes a radial basis function (RBF) neural network-improved integral terminal sliding mode control scheme optimized by state-aware adaptive particle swarm optimization (ASPSO), denoted as ASPSO-optimized RBF-IITSMC. First, a fractional-memory integral terminal sliding surface incorporating a boundary-layer saturation mapping is constructed. The proposed terminal mapping is shown to be globally Lipschitz continuous, and an explicit approximation-error bound relative to the conventional terminal power mapping is established. Second, an RBF neural compensator driven by the sliding variable is incorporated into the reconstructed sliding dynamics to estimate lumped uncertainties and reduce the compensation burden on the robust feedback term. Furthermore, a state-aware adaptive PSO variant combining population-diversity monitoring and differential mutation is developed to jointly tune the 15-dimensional controller parameter vector. The practical finite-time reachability of the sliding variable and the uniform ultimate boundedness of the sliding variable and neural-weight estimation error are analyzed using a Lyapunov framework. Simulation results on a six-degree-of-freedom (6-DOF) robotic arm demonstrate improved tracking accuracy and disturbance-rejection performance, together with reduced high-frequency torque oscillations, compared with the evaluated baseline controllers. Full article

To address the challenges of lumped model uncertainties and tracking error constraints in biomimetic rehabilitation robot control, this paper proposes a novel high-order integral barrier function to construct an adaptive neural tracking control scheme. Radial basis function neural networks (NNs), inspired by the receptive field mechanism of motor neurons, feature local activation and can accurately approximate the nonlinear dynamics of such bionic rehabilitation devices. Distinct from traditional integral barrier Lyapunov functions, the presented high-order integral barrier function can accommodate both time-varying and time-invariant error constraints, while simplifying the controller derivation and ensuring full differentiability of virtual control laws throughout the backstepping framework. Supported by the derived barrier function theorems, the tracking error of the robot is theoretically proven to stay within predefined safe boundaries and converge exponentially to a compact neighborhood of the origin. Finally, comparative numerical simulations on a biomimetic rehabilitation robot validate the effectiveness of the proposed theorem and constrained adaptive neural control strategy. Full article

Remote center-of-motion mechanisms are essential in minimally invasive surgery because they allow surgical instruments or an endoscopic camera to pivot around a trocar entry point while eliminating lateral motion at the incision. This paper presents the design, kinematic modeling, prototype implementation and preliminary evaluation under laboratory conditions of a compact, spherical, remote center-of-motion robot for minimally invasive surgical orientation tasks. The proposed mechanism uses a spherical kinematic architecture actuated by a contra-rotating differential gearbox. This gearbox generates two coaxial output rotations of equal magnitude and opposite direction from a single input, mechanically synchronizing the opposed motion of the two base links and eliminating the need for cable-pulley transmission or dual electronically synchronized motors. A second actuator chain rotates the gearbox assembly around the base axis, thereby decoupling the extension–retraction motion from base-axis rotation. Forward and inverse kinematic formulations were derived for teleoperation of the robot using a 7 degrees of freedom haptic device and for remote center-of-motion orientation control using a 3-axis joystick. A proof-of-concept prototype was developed and integrated with a custom embedded controller, closed-loop motor control, a master-console interface and video feedback loop. The system was evaluated in a phantom-torso setup using a custom endoscopic camera, internal visual markers and an OptiTrack-based measurement of the remote center-of-motion accuracy. The qualitative experiment confirmed functional integration of the mechanical, electronic and software subsystems, while the optical-tracking measurement showed that the pivot constraint was maintained with a mean deviation of 1.69 mm and a root-mean-square deviation of 2.13 mm over the analyzed orientation sweep. The main limitations remain the 1:1 gearbox ratio, limited actuator torque, additively manufactured gearing and the absence of repeated-trial repeatability and full workspace characterization. Full article

Urban service robots require route planners that are not only collision-free but also consistent with the energetic behavior of differential drive locomotion. Conventional grid planners such as A* are efficient and reliable for geometric navigation, but their usual cost structure prioritizes path length and does not explicitly account for heading changes, concentrated turns, or localization risk near obstacles. This study presents an energy-aware route-planning formulation for differential-drive mobile robots operating in dense polygonal urban environments. The path is encoded through twelve continuous internal waypoints and is evaluated using an interpretable energy proxy that combines translational distance, cumulative absolute rotation, squared rotation, and a clearance-dependent localization risk term. Collision avoidance, boundary compliance, and maximum turn feasibility are handled through a feasibility-first dominance rule, and the resulting constrained problem is solved using a Genetic Algorithm and Particle Swarm Optimization. A* with clearance inflated occupancy grids is included as a deterministic baseline. The final experiments used a dense urban scenario with sixteen polygonal obstacles, an A* grid resolution of 0.10 m, a robot radius of 0.20 m, a safety clearance of 0.10 m, 80 GA individuals, 80 PSO particles, 100 iterations, and 10 independent runs. All methods achieved a 100% feasibility rate. PSO obtained the lowest average energy proxy, 18.505, compared with 18.645 for A* and 18.645 for GA, and reduced average rotation from 4.136 rad to 4.079 rad. However, A* remained much faster, 5.860 s on average, compared with 174.574 s for GA and 175.869 s for PSO. The ranking analysis shows that GA produced the best aggregate score when route quality, time, clearance, and feasibility were weighted equally, while PSO produced the best route quality. External perturbation tests indicate that open-loop execution in narrow corridors is sensitive to bias and waypoint noise, which motivates closed-loop tracking, online replanning, and physical validation in future work. Full article

This paper proposes a hybrid SMC + ESO + RBF control architecture designed to evaluate trajectory tracking and liquid sloshing suppression in tanker robots navigating complex terrains within a simulated environment. A multi-variable dynamic model integrates the differential drive mobile platform with an equivalent mass-spring-damper sloshing system under terrain disturbances. To achieve robust stability, an Extended State Observer (ESO) neutralizes baseline generalized disturbances, while a Radial Basis Function (RBF) neural network adaptively compensates for residual nonlinear coupled sloshing errors. Practical stability and uniform ultimate boundedness (UUB) of the closed-loop system are proven via Lyapunov theory under bounded network approximation errors and observer uncertainties. Numerical simulations in MATLAB/Simulink demonstrate that the proposed controller achieves a baseline Root Mean Square Error (RMSE) of 0.0109 m, representing an 84.1% improvement over traditional Sliding Mode Control (SMC). Parametric sensitivity analysis under variable liquid filling ratios (30%, 50%, and 70%) and a circular steering topology indicates notable adaptability, with the tracking RMSE bounded between 0.0085 m and 0.0129 m under the considered virtual scenarios. Within the simulated environment, the system successfully smooths control profiles and dampens liquid oscillations, demonstrating a promising potential to support transport safety and mitigate actuator chattering under virtual constraints. However, these qualitative observations serve as preliminary hypotheses and must be formally verified through future hardware-in-the-loop (HIL) experiments to evaluate the impact of physical non-idealities, including sensor noise, actuator saturation, communication delays, and wheel slip. These findings confirm the competitive analytical robustness of the SMC + ESO + RBF framework in stabilizing tanker robots within highly uncertain simulated operational environments. Full article

As automation increases, mechatronic manufacturing systems require supervisory solutions that combine precise control, intelligent diagnostics, and intralogistics awareness. This paper presents a networked sensor–actuator–information architecture integrating model predictive control (MPC), Random Forest (RF)-based diagnostics, and logistics-aware coordination for automated mechatronic manufacturing systems. The main contribution is the explicit coupling of logistics-related supervisory variables with the predictive control problem and the diagnostic feature space. Buffer occupancy, transport delay, and logistics-induced waiting state are incorporated into an augmented reduced-order model to support constrained control and health-state interpretation. The framework is evaluated through a comparative simulation-based feasibility study using a low-order model of a robotic production axis affected by disturbances, degradation, and logistics-related constraints. The proposed approach is compared with classical feedback control, predictive control without diagnostics, and predictive control with diagnostics but without explicit intralogistics coupling. In the reduced-order simulation scenario, the proposed method achieved the lowest mean RMSE of 0.330 ± 0.015 and the lowest mean constraint violation rate of 3.133 ± 0.280% across 40 repeated simulation runs. However, the improvement in nominal tracking accuracy over the strongest diagnostic-assisted MPC baseline was marginal. Adding logistics-related diagnostic features improved mean accuracy from 0.848 ± 0.014 to 0.874 ± 0.012 and mean F1-score from 0.844 ± 0.016 to 0.872 ± 0.013. The main advantage of the proposed architecture was observed in reliability- and continuity-oriented indicators, including reduced downtime, lower final damage accumulation, fewer cooling cycles, and improved differentiation between machine-related and logistics-induced abnormal conditions. Full article

This paper proposes a physically based hybrid architecture for controlling mobile robot drives. It combines a model-based controller, an adaptive neural network compensator for residual dynamics, and a Lyapunov-based stability supervision mechanism. Unlike existing hybrid control approaches, the proposed architecture implements a structured injection of neural network correction directly into the physical drive model with a controlled Lyapunov-based adaptation constraint. A mathematical model of the electromechanical drive of a differential mobile platform is developed, taking into account electrical and mechanical dynamics, wheel-to-surface contact interaction, and the system’s energy characteristics. Numerical simulation results demonstrate that the hybrid approach improves tracking accuracy, improves transient response, and ensures stable operation of the control system under parametric uncertainty, adhesion changes, and external disturbances. The proposed architecture maintains the physical interpretability of the model while simultaneously enhancing the system’s adaptability. The obtained results confirm the effectiveness of the developed method and its potential for application in control systems for mobile robotic platforms. Full article

This article investigates the trajectory tracking control of a differential-drive two-wheeled mobile robot (DDWMR) using its kinematic model, framed in the context of sustainability. A nonlinear-to-linear transformation based on differential flatness is employed to convert the original nonlinear system into two fully decoupled linear subsystems, enabling a simple and robust controller design. Unlike conventional flatness-based methods that rely on exact feedforward linearization around a reference trajectory, the proposed approach performs plant linearization, ensuring reliable tracking across a wide range of trajectories. The resulting two-loop architecture consists of an inner nonlinear loop implementing state prolongation and static feedback and an outer linear controller performing trajectory tracking of the linearized system. Simulation results on a circular reference trajectory demonstrate high tracking accuracy, with a maximum transient deviation of 0.28 m, a settling time of approximately 120 s, and a steady-state mean tracking error below 0.01 m. These results confirm that the plant-linearization-based framework provides accuracy, robustness, and practical applicability for DDWMR trajectory tracking, all within a responsible control environment. Full article

Resolving the kinematic redundancy of 7-DoF humanoid arms to generate natural, human-like motions remains a fundamental challenge in biomimetic robotics. This paper presents a hybrid inverse kinematics (IK) framework that learns a pose-dependent redundancy parameter and integrates it into a differential IK solver. Specifically, we employ the stereographic Shoulder–Elbow–Wrist (SEW) angle as a well-conditioned geometric parameterization. This formulation transforms the algorithmic singularity into a unidirectional half-line, which can be oriented outside the typical reachable workspace. To specify the optimal configuration within the self-motion manifold, a motion dataset was collected by teleoperating a humanoid arm via an anthropomorphic wearable exoskeleton. This approach translates operator-specific postural preferences into the robot’s joint space. A lightweight neural network was then trained to learn the mapping from end-effector poses to these operator-specific SEW angles. By incorporating the predicted SEW angle as a dynamic secondary objective in the null space of the primary tracking task, the proposed framework enables natural redundancy resolution while preserving end-effector tracking accuracy. Both simulations and real-robot experiments were conducted to validate the approach. Results show that, compared to the average performance of static fixed-parameter strategies, the proposed method improves the Joint Configuration Quality Index (CQI) by 22.5% and reduces energy costs by 11.3%. Moreover, the sub-millisecond inference latency (0.44 ms) facilitates seamless integration into real-time control pipelines. Full article

This paper focuses on the development of a mobile robot-based security surveillance and target-tracking application that combines image-processing algorithms with the Navigation Stack in the robot operating system (ROS). The proposed approach integrates a 3D camera with the MobileNet-SSD object detection model to estimate the target’s three-dimensional spatial coordinates in real time. These coordinates are continuously transmitted to the ROS Navigation Stack as dynamic goal points, enabling the robot to perform path planning and target-following while maintaining a predefined safety distance and avoiding obstacles. The proposed solution has been validated on a real differentially driven wheeled mobile robot. Experimental results demonstrate smooth and stable robot motion, accurate maintenance of the desired following distance, and reliable static obstacle avoidance while continuously tracking the target. These outcomes confirm the effectiveness and robustness of the integrated system for vision-based navigation tasks in indoor environments. Full article

Objectives: Bilateral robotic arm training (BRT) may enhance poststroke motor recovery by reducing interhemispheric inhibition and promoting bilateral motor network engagement. However, previous reviews have often pooled bilateral and unilateral robotic approaches, potentially masking differential effects. This systematic review and meta-analysis compared the effects of BRT with those of unilateral robotic training (URT) and conventional rehabilitation on upper-limb motor function after stroke. Methods: Randomized controlled trials were identified through systematic searches of major electronic databases and trial registries in accordance with PRISMA guidelines. The risk of bias was assessed via the Cochrane Risk of Bias 2 tool. Random effects meta-analyses were performed using standardized mean differences (SMDs). Predefined subgroup and sensitivity analyses were used to examine the influence of participant characteristics, training dose, intervention duration, and robotic device type. Results: Fourteen randomized controlled trials involving 440 participants were included. Overall, compared with control interventions, BRT did not significantly improve upper-limb motor function, as measured using the Fugl–Meyer Assessment for Upper Extremity (SMD = 0.18, 95% CI −0.01–0.36). Significant effects were observed in participants younger than 60 years, with training doses > 15 h, intervention durations > 4 weeks, and use of Bi-Manu-Track systems. Conclusions: BRT did not demonstrate a significant overall advantage over URT or conventional rehabilitation. However, subgroup analyses suggest that treatment effects may vary according to patient characteristics, training dose, duration of the intervention, and device type. Full article

Two-wheeled self-balancing robots exhibit nonlinear and inherently unstable dynamics due to their inverted-pendulum structure, making control design challenging under terrain variations and external disturbances. This paper proposes a hybrid master–slave fuzzy cascade controller with an additional wheel-synchronization loop to improve tracking performance and robustness. The architecture combines a master velocity PI loop with fuzzy-tuned integral action and a slave balance PD loop with fuzzy proportional control, while a differential synchronization mechanism compensates for motor mismatches without affecting the global balance dynamics. Local stability is analyzed through linearization and equivalent gain approximation within a sector-bounded framework. Experimental validation was conducted on an ESP32-based TWSBR under flat, uphill, and downhill conditions at reference velocities of $0.15$, $0.20$, and $0.30{\displaystyle \frac{\mathrm{m}}{\mathrm{s}}}$, including payload tests with additional masses of $0.279$ and $0.375\mathrm{kg}$. For each scenario, 30 independent trials were performed to compute the reported metrics. Compared with a conventional PID controller, the proposed strategy reduced the flat-terrain velocity RMSE from $0.0108$ to $0.0057{\displaystyle \frac{\mathrm{m}}{\mathrm{s}}}$, while also improving angular stabilization and robustness under slope and payload disturbances. Full article

This paper addresses the tracking control problem of robotic manipulators under a universal symmetry constraint framework in the presence of lumped uncertainties and external disturbances. Unlike conventional constrained control schemes that treat tracking error bounds and state bounds separately, the proposed method explicitly exploits the symmetric structure of the prescribed constraints and formulates both tracking error constraints and full-state constraints in a unified manner. Based on the Euler–Lagrange dynamics of robotic manipulators, a universal symmetry constraint transformation is introduced to convert the original constrained system into an equivalent unconstrained form while preserving the intrinsic symmetry of the admissible sets. To enhance robustness against uncertainties and disturbances, a sliding-mode extended state observer is designed to estimate the total disturbance online. Meanwhile, a tracking differentiator is incorporated into the recursive design to avoid repeated differentiation of virtual control signals. On this basis, a disturbance-compensated backstepping controller is developed for the transformed manipulator system. It is shown that all closed-loop signals remain bounded, the prescribed symmetric tracking error and state constraints are never violated, and the tracking error converges asymptotically when the observer and differentiator errors vanish asymptotically. Simulation results obtained from a robotic manipulator verify the effectiveness of the proposed control strategy. Full article