An RL-NSGA-DP algorithm for optimization of robot placement and trajectory allocation in mobile robotic grinding of wind turbine blades

Lagrangian Neural Network-Based Control: Improving Robotic Trajectory Tracking via Linearized Feedback

Imitation learning is a promising paradigm for training robot agents; however, standard approaches typically require substantial data acquisition – via numerous demonstrations or random exploration – to ensure reliable performance. Although exploration reduces human effort, it lacks safety guarantees and often results in frequent collisions – particularly in clearance-limited tasks (e.g. peg-in-hole) – thereby, necessitating manual environmental resets and imposing additional human burden. This study proposes Self-Augmented Robot Trajectory (SART), a framework that enables policy learning from a single human demonstration, while safely expanding the dataset through autonomous augmentation. SART consists of two stages: (1) human teaching only once, where a single demonstration is provided and precision boundaries – represented as spheres around key waypoints – are annotated, followed by one environment reset; (2) robot self-augmentation, where the robot generates diverse, collision-free trajectories within these boundaries and reconnects to the original demonstration. This design improves the data collection efficiency by minimizing human effort while ensuring safety. Extensive evaluations in simulation and real-world manipulation tasks show that SART achieves substantially higher success rates than policies trained solely on human-collected demonstrations. Video results available at https://sites.google.com/view/sart-il.

To evaluate the feasibility, safety, and technical performance of robot-assisted CT-guided cryoablation for pulmonary metastases. A single-centre IDEAL stage 2a prospective development study of 26 participants (median age 62 years, IQR 47-71; 14 men) who underwent 30 procedures targeting 37 lung metastases using a robotic navigation system. Median tumour diameter was 9.8 mm (IQR 5.1-12.8). All procedures were performed under general anaesthesia with high-frequency jet ventilation. Feasibility, safety, and technical performance (targeting accuracy, manipulations, radiation dose) were recorded. Robotic guidance was successfully completed without conversion in 35/37 tumours (95%). One major complication occurred (3%, CTCAE grade 3 pneumothorax requiring 4 days of drainage); all others were grade 1-2. Pneumothoraces were managed by observation (n = 7) or prophylactic intraprocedural chest drain insertion (n = 11). No bronchopleural fistulas were observed. Median hospital stay was 1 night (IQR 1-2). A total of 54 cryoprobes were used. Median Euclidean targeting error on first insertion was 6.1 mm (IQR 2.9-9.7) and lateral error 4.2 mm (IQR 2.2-6.5). The median number of manipulations per probe was 1 (IQR 0-2.5), with one-third requiring no adjustment. Once integrated into the workflow, the "chopstick" technique was frequently applied, supporting conformal ablation. Median total procedure time was 66.5 min (IQR 56.6-92.8). Twelve-month local tumour progression-free survival was 97%. Robot-assisted CT-guided cryoablation of pulmonary metastases was feasible, safe, and accurate, achieving high targeting precision with minimal cryoprobe manipulation. These findings support evaluation in prospective comparative trials. Question Robotic-assisted CT-guided cryoablation of lung metastases is feasible and safe, achieving high targeting accuracy and minimal probe manipulation, even in anatomically challenging cases. Findings Robotic trajectory planning supported complex multiprobe configurations. Procedural refinements-including patient positioning, probe selection, and adoption of "chopstick" configurations-were introduced to address bleeding risk and optimise energy delivery. Clinical relevance Robot-assisted navigation is particularly advantageous in cryoablation, enabling minimal manipulations and accurate probe placement despite the often-necessary complex trajectories.

Intelligent adaptive fractional order controller for mobile robot trajectory tracking.

Quantifying Manual Adjustment of Foot Placement Under a Fixed Robotic Trajectory in Lower Limb Exoskeletons

In this study, a PID gain tuning approach using Deep Deterministic Policy Gradient (DDPG), a reinforcement learning (RL) algorithm, is proposed for trajectory tracking of delta parallel robots. Owing to their 3-degree-of-freedom (3-DOF) parallel kinematic structure, delta robots offer higher stiffness, precision, and speed capabilities than serial manipulators; they are therefore widely used in high-speed pick-and-place applications due to their low moving mass and the stiffness provided by the closed-chain mechanism. In this study, the proposed DDPG-PID approach is comparatively investigated against the conventional Ziegler–Nichols (ZN) and Cohen–Coon (CC) tuning methods; DDPG is designed to optimize the PID gains (Kp, Ki, Kd) within predefined bounds in a continuous action space. In simulations conducted on four different trajectories—circle, lemniscate, diamond, and star—RMSE, IAE, ISE, ITAE, and maximum error metrics are used for evaluation. According to the results, DDPG-PID achieves the lowest error on all trajectories, reducing RMSE by approximately 35–58% compared to ZN-PID and by approximately 79–82% compared to CC-PID; similarly, improvements are observed in IAE/ISE/ITAE and maximum error values. These findings indicate that DDPG-PID provides more stable and accurate tracking, particularly on complex trajectories involving sharp direction changes, and demonstrate that the proposed method offers a superior automatic PID tuning alternative to classical tuning rules for industrial parallel robot control applications.

The object of the study is the process of constructing and analyzing the trajectories of the gripping device of a collaborative robot-manipulator under spatial constraints and the presence of obstacles in a dynamic environment. The subject of the study is mathematical models, algorithmic and software for modeling the optimal motion of the manipulator end effector taking into account kinematic, dynamic and energy constraints. The aim of the research is to construct trajectories of the collaborative robot's gripping device, taking into account constraints and optimal control actions in continuous time, which ensure the construction of trajectories with minimal energy consumption, compliance with given spatial constraints, and avoidance of collisions with obstacles. The research methodology is based on the application of the Pontryagin maximum principle to form the conditions for optimal control and the construction of a system of differential equations with boundary conditions. A special cost functional has been developed to quantify energy consumption and take into account penalties for approaching prohibited zones. The numerical solution of the problem was implemented using the Euler method, and the optimization of the trajectory parameters with fixed final effector coordinates was implemented using the least squares method with constraints. The Python programming language and the Matplotlib library were used to visualize the results. As a result of the study, optimal trajectories of the gripping device were obtained, which ensure collision avoidance, compliance with spatial constraints, and reduced energy consumption when reaching the specified final effector positions. The simulation confirmed the effectiveness of the developed method and its resistance to changes in environmental parameters. The conclusions of the study indicate that the proposed approach allows for a comprehensive solution to the problem of planning the movement of collaborative robots in the optimal control mode taking into account constraints. The results obtained can be applied in Industry 5.0 production systems, robotic service complexes, automated warehouse systems, and robots that interact with humans in a limited space.

As a high-value fruit crop grown worldwide, apples require efficient harvesting solutions to maintain a stable supply. Intelligent harvesting robots represent a promising approach to address labour shortages. This study introduced a Cartesian robot integrated with a continuous-picking end-effector, providing a cost-effective and mechanically simpler alternative to complex articulated arms. The system employed a hand–eye calibration model to enhance positioning accuracy. To overcome the inefficiencies resulting from disordered harvesting sequences and excessive motion trajectories, the harvesting process was treated as a travelling salesman problem (TSP). The conventional fixed-plane return trajectory of Cartesian robots was enhanced using a three-dimensional continuous picking path strategy based on a fixed retraction distance (H). The value of H was determined through mechanical characterization of the apple stem’s brittle fracture, which eliminated redundant horizontal displacements and improved operational efficiency. Furthermore, an improved grey wolf optimizer (IGWO) was proposed for multi-fruit path planning. Simulations demonstrated that the IGWO achieved shorter path lengths compared to conventional algorithms. Laboratory experiments validated that the system successfully achieved vision-based localization and fruit harvesting through optimal path planning, with a fruit picking success rate of 89%. The proposed methodology provides a practical framework for automated continuous harvesting systems.

Dynamic Movement Primitives (DMPs) provide a flexible framework for robotic trajectory generation, offering adaptability, robustness to disturbances, and modulation of predefined motions. Yet achieving reliable spatial coupling among multiple DMPs in cooperative manipulation tasks remains a challenge. This paper introduces a graph-based trajectory planning framework that designs dynamic controllers to couple multiple DMPs while preserving formation. The proposed method is validated in both simulation and real-world experiments on a dual-arm UR5 robot performing tasks such as soft cloth folding and object transportation. Results show faster convergence and improved noise resilience compared to conventional approaches. These findings demonstrate the potential of the proposed framework for rapid deployment and effective trajectory planning in multi-robot manipulation.

Abstract Industrial robots are widely used in diverse manufacturing environments. Nonetheless, how to enable robots to automatically plan trajectories for changing tasks presents a considerable challenge. Further complexities arise when robots operate within work cells alongside machines, humans, or other robots. This paper presents a practical multi-level hybrid motion planning strategy combining operator demonstration-driven task-space planning with joint-space optimization that enforces key constraints such as reachability, joint limits, manipulability, and collision avoidance. The framework uses a supervisory agent to automatically switch between the planning modules, ensuring all generated robot trajectories are both feasible and suited for real manufacturing environments. Therefore, the derived hybrid motion planning policy generates a feasible trajectory that adheres to task constraints with simplistic demonstrations. Experimental validation in simulated and physical setups shows the approach reduces reconfiguration time and increases task success rates compared to conventional motion planning solutions.

To address the challenges of environmental and collaborative obstacle avoidance in dual-arm robot path optimization, a time-optimal trajectory optimization method based on a multi-strategy optimized Whale Optimization Algorithm (WOA) is proposed. First, the left arm is selected as the primary manipulator for path planning within the workspace, with its trajectory treated as an obstacle, thus enabling path optimization for the right arm. In this process, a collision detection mechanism is introduced, converting the path optimization problem into a constrained objective optimization problem. Subsequently, the multi-strategy optimized Whale Optimization Algorithm (WOA) is employed to solve the problem, yielding the optimized path trajectory for the dual-arm robot. Finally, the method’s effectiveness is validated through simulation experiments. Simulation results demonstrate that, compared to the traditional Whale Optimization Algorithm, the proposed method reduces the total movement distance of the manipulator by 17.59% and enhances path optimization efficiency by 51%.

AssembleNet: End-to-End Deep Learning Modelling of Robot Trajectories for Virtual Commissioning of Automotive Assembly

Intelligent robotic arm trajectory planning using improved classical Q-learning and LSTM

Abstract Non-standard workpieces and geometric deviations between CAD models and physical parts often cause offline-generated robot trajectories to fail to meet precision requirements. To address this challenge, we present a 3D vision–guided adaptive trajectory correction system that fuses global CAD priors with local point cloud refinements. The perception stage introduces a region-weighted Point Pair Feature (PPF) method with visibility-aware preprocessing and a two-stage verification strategy, enhancing matching accuracy in task-critical regions while maintaining robustness to occlusion. In the trajectory generation stage, a lightweight local correction module detects and refines deviated trajectory segments through direction-guided point cloud projection and Catmull–Rom interpolation, achieving smooth and precise path adjustments without full replanning. Experimental results on rigid workpieces with induced defects show a 100% pose estimation success rate in the evaluated scenarios, an average 41% reduction in Region-of-Interest Root Mean Square Error (ROI-RMSE) compared to baseline PPF, and sub-millimeter trajectory conformity with millisecond-level latency. The proposed framework maintains compatibility with standard offline programming workflows, offering a practical and scalable solution for high-precision robotic guidance in complex industrial environments.

To address the requirements for automation and intelligence of agricultural robots, this paper develops a 14degree-of-freedom dynamic model for wheeled agricultural robots. The model aims to provide a dynamic modeling foundation under the framework of modern control theory for the automation and intelligence of wheeled agricultural robots. It incorporates the Ackermann steering mechanism, MacPherson independent suspension system, tire model, and deformable soil model based on Bekker's formula. The vertical tire pressure is calculated using the deformable soil model via Bekker's formula, while tire forces are predicted by combining the tire slip angle and slip ratio with the Magic Formula Tire Model. By analyzing the force transmission effect of the suspension system, integrating the center-of-mass coupling effect analysis and the robot body model equations, the precise prediction of the attitude and motion trajectory of the wheeled agricultural robot is achieved. A co-simulation experiment using MATLAB and CarSim under the double lane change (DLC) condition is designed for validation. Experimental results demonstrate that the proposed model exhibits high consistency with the CarSim simulation results. The mean absolute errors (MAE) are 0.327° for steering wheel angle, 0.677°/s for yaw rate, 0.691° for body roll angle, and 0.944 m/s² for lateral acceleration. All errors are less than 1.5, meeting the requirements of dynamic simulation. This model can effectively predict the body attitude of wheeled agricultural robots and lay a foundation for the subsequent development of optimal control algorithms for agricultural robots.

This paper proposes Social-only Particle Swarm Optimization-based Iterative Learning Control (SO-PSO-ILC) to address the limitations of conventional Iterative Learning Control (ILC) in model dependency and manual parameter tuning. The proposed method autonomously optimizes the learning gain using a social-only PSO variant. Comparative results on four distinct trajectories demonstrate superior performance: SO-PSO-ILC achieved a final RMSE of 0.0008 m in the linear path test and a precision 4.6 times higher than the baseline in the waveform path test. It also exhibits the fastest convergence rate, outperforming PSO-ILC in tracking accuracy and computational complexity while avoiding the convergence issues observed in WSA-ILC. The simulation results validate that swarm-optimized ILC provides a robust framework for repetitive tasks requiring high accuracy.

In complex scenarios, single-sensor perception is unstable, trajectory planning lacks safety constraints, and there are problems with multi-source coordination. To address these issues, this paper proposes an intelligent robot trajectory decision-making method combining LiDAR and image processing, forming a multimodal perception system. This system requires a robot platform, proper extrinsic parameter calibration, and time synchronization. A LiDAR-Image feature acquisition and attention fusion network is designed from a unified BEV perspective to generate an environmental cost map that considers both geometry and semantics. Based on this cost map, an RL/MPC trajectory decision-making model is constructed, introducing chance constraints and dynamic boundaries to ensure safety margins. Simulations and real-world experiments included indoor corridors, office areas, and crowded places. Results show that the multimodal approach outperforms DWA and single-modal RL in terms of mAP, distance RMSE, minimum obstacle distance, and task completion rate. Furthermore, it can run continuously on embedded platforms, demonstrating the effectiveness of the proposed method and its value for engineering applications.

The subject matter is a method for determining the robot trajectory with four steering wheels to reach a given point on a terrain map. The research goal is to develop a method for determining the orientation of the wheels depending on the trajectory of the mobile platform to increase the maneuverability of an autonomous robotic vehicle in a limited production space. Tasks to be solved: to analyze similar solutions, describe the proposed design of the steering unit mechanism for a mobile robotic cart, describe the kinematics of a mobile robot with four steerable wheels, develop an algorithm for the steering unit control module, propose a method for controlling a mobile platform with four steerable wheels, and perform experimental studies on the application of the proposed method. Scientific novelty: a method for determining the orientation of the wheels to reach a given point on the terrain plan has been proposed. An algorithm for performing calculations using a software tool has been developed. A mathematical justification for the method of controlling individual wheel blocks of a mobile platform has been provided. Methods of the study: modeling methods and automatic control theory, methods for describing linear dynamic systems, analytical modeling methods, computer modeling in the Matlab/Simulink environment. Results and conclusions: The mobile platform movement principle using four independent steering wheels is considered. A method for determining the orientation of the steering wheels depending on the trajectory of movement is proposed, which is based on the geometric analysis of the position of the platform and the target point, which allows calculating the angle of rotation of each wheel in such a way as to ensure movement to a given point without lateral slippage. A mathematical model of the control system is built, a structural and functional diagram is developed, an algorithm for processing commands, calculating the angles of rotation is described, and a three-level control system is implemented: linear speed, wheel orientation angle and angular speed of the entire platform. The developed mock-up sample of the mechatronic steering wheel assembly is described. The simulation conducted in the Simulink environment confirmed the operability of the proposed system.

This work focuses on wire arc additive manufacturing for the rapid prototyping of shell-type parts such as sealed containers/capsules required in the manufacturing of metal components using hot isostatic pressing (HIP) of powder. The selected material was AISI 316L. The automatic generation step of robot trajectories from the CAD design of the part to be manufactured was addressed first. The mechanical and metallurgical properties of WAAM samples were then evaluated. Finally, a hollow cylindrical capsule manufactured by WAAM was used for the HIP sintering of powder to demonstrate the relevance of the hybrid technology. The main results are as follows: 1. The Ultimate Tensile Strength (UTS) of AISI 316L WAAM samples was measured be-19 tween 540 MPa (longitudinal direction) and 600 MPa (transverse direction). 2. The as-manufactured WAAM parts present a residual (δ) ferrite content of 5–7%. 3. HIP processing permitted to reset a fully austenitic structure within the WAAM wall/shell. 4. The grain size was found to be coarser in the WAAM walls and finer in the core of the part (made of sintered powder). Finally, the suggested hybrid process may become an alternative technology for the manufacture of medium-size metal components in the nuclear industry.