Ground Robots Research Articles

Active manipulation of a tethered drone using explainable AI

Tethered drones are currently finding a wide range of applications such as for aerial surveillance, traffic monitoring, and setting up ad-hoc communication networks. However, many technological gaps are required to be addressed for such systems. Most commercially available tethered drones hover at a certain position; however, the control task becomes challenging when the ground robot or station needs to move. In such a scenario, the drone is required to coordinate its motion with the moving ground vehicle without which the tether can destabilize the drone. Another challenging aspect is when the system is required to operate in GPS denied environments, such as in planetary exploration. In this paper, to address these issues, we take advantage of passive or force-based control in which the tension in the tether is sensed and used to drive the drone. Fuzzy logic is used to implement the force-based controller as a tool for explainable Artificial Intelligence. The proposed fuzzy logic controller takes tether force and its rate of change as the inputs and provides desired attitudes as the outputs. Via simulations and experiments, we show that the proposed controller allows effective coordination between the drone and the moving ground rover. The rule-based feature of fuzzy logic provides linguistic explainability for its decisions. Simulation and experimental results are provided to validate the novel controller. This paper additionally develops an adaptive controller for estimating unknown constant winds on these tethered drone systems using a proportional controller. The simulation results demonstrate the effectiveness of the proposed adaptive control scheme in addressing the effect of wind on a tethered drone.

A UWB-Ego-Motion Particle Filter for Indoor Pose Estimation of a Ground Robot Using a Moving Horizon Hypothesis.

Ultra-wideband (UWB) has gained increasing interest for providing real-time positioning to robots in GPS-denied environments. For a robot to act on this information, it also requires its heading. This is, however, not provided by UWB. To overcome this, either multiple tags are used to create a local reference frame connected to the robot or a single tag is combined with ego-motion estimation from odometry or Inertial Measurement Unit (IMU) measurements. Both odometry and the IMU suffer from drift, and it is common to use a magnetometer to correct the drift on the heading; however, magnetometers tend to become unreliable in typical GPS-denied environments. To overcome this, a lightweight particle filter was designed to run in real time. The particle filter corrects the ego-motion heading and location drift using the UWB measurements over a moving horizon time frame. The algorithm was evaluated offline using data sets collected from a ground robot that contains line-of-sight (LOS) and non-line-of-sight conditions. An RMSE of 13 cm and 0.12 (rad) was achieved with four anchors in the LOS condition. It is also shown that it can be used to provide the robot with real-time position and heading information for the robot to act on it in LOS conditions, and it is shown to be robust in both experimental conditions.

The robot grinding and polishing of additive aviation titanium alloy blades: a review

PurposeThe purpose of this review is to comprehensively consider the material properties and processing of additive titanium alloy and provide a new perspective for the robotic grinding and polishing of additive titanium alloy blades to ensure the surface integrity and machining accuracy of the blades.Design/methodology/approachAt present, robot grinding and polishing are mainstream processing methods in blade automatic processing. This review systematically summarizes the processing characteristics and processing methods of additive manufacturing (AM) titanium alloy blades. On the one hand, the unique manufacturing process and thermal effect of AM have created the unique processing characteristics of additive titanium alloy blades. On the other hand, the robot grinding and polishing process needs to incorporate the material removal model into the traditional processing flow according to the processing characteristics of the additive titanium alloy.FindingsRobot belt grinding can solve the processing problem of additive titanium alloy blades. The complex surface of the blade generates a robot grinding trajectory through trajectory planning. The trajectory planning of the robot profoundly affects the machining accuracy and surface quality of the blade. Subsequent research is needed to solve the problems of high machining accuracy of blade profiles, complex surface material removal models and uneven distribution of blade machining allowance. In the process parameters of the robot, the grinding parameters, trajectory planning and error compensation affect the surface quality of the blade through the material removal method, grinding force and grinding temperature. The machining accuracy of the blade surface is affected by robot vibration and stiffness.Originality/valueThis review systematically summarizes the processing characteristics and processing methods of aviation titanium alloy blades manufactured by AM. Combined with the material properties of additive titanium alloy, it provides a new idea for robot grinding and polishing of aviation titanium alloy blades manufactured by AM.

Secure learning-based coordinated UAV–UGV framework design for medical waste transportation

A cost-effective solution with less human involvement must be developed for medical waste (MW) transportation. A learning-based coordinated unmanned aerial vehicle–unmanned ground vehicle (UAV–UGV) (CUU) framework, currently unavoidable use, with a transfer learning algorithm is suggested. A transfer learning algorithm is implemented for collision-free optimal path planning. In the framework, mobile ground robots collect medical waste from waste disposal centers through the pick-and-place technique. Then, networked drones lift the collected medical waste and fly through a predefined optimal trajectory. The framework considers the dynamic behavior of the environment and explores the actions for picking, placing, and dropping medical waste. A deep reinforcement learning mechanism has been incorporated for each successful or unsuccessful action by the framework to provide the rewards. With optimal policies, the coordinated UAV and UGV change their actions in dynamic conditions. An optimal cost of transportation of medical waste by the proposed framework is created by considering the weight of MW packets as the payload capacity of a CUU framework, the cost of steering the UAV and UGV, and the time required to transport the MW. The effectiveness of the CUU framework for MW transportation has been tested using MATLAB. The MW transportation data have been encrypted using an encryption key for security and authenticity.

Process Optimization of Robotic Grinding to Guarantee Material Removal Accuracy and Surface Quality Simultaneously

Abstract Simultaneously guaranteeing material removal accuracy and surface quality of robotic grinding is crucial. However, existing studies of robotic grinding process optimization have mainly focused on a single indicator that solely considers contour error or surface roughness, while studies that simultaneously investigate the impact of contact force, spindle speed, feed rate, inclination angle, and path space on the material removal profile (MRP) and the surface roughness are lacking. This paper proposes a hybrid optimization method that considers dimensional accuracy and surface quality constraints. First, an MRP model that considers the coupling influence of the contact force, spindle speed, feed rate, and inclination angle is presented. Then, a surface roughness model that considers the inclination angle is established. Finally, the contact force, feed rate, inclination angle, and path space are simultaneously optimized to satisfy the hybrid constraints of MRP accuracy and surface roughness. The proposed method ensures maximum grinding efficiency while satisfying dimensional accuracy and surface quality constraints. The proposed method is verified on an industrial robotics grinding system with a pneumatic force-controlled actuator. The results show that the proposed method has higher profile accuracy and lower surface roughness than traditional methods.

3-D LiDAR Localization Based on Novel Nonlinear Optimization Method for Autonomous Ground Robot

3D Light Detection and Ranging (LiDAR)-based localization for autonomous ground robot in unknown environment is a critical ability, which has been extensively studied. In this paper, we propose a novel nonlinear optimization method to promote the 3D LiDAR localization performance. Firstly, due to that the objective function is the summation of point correspondence matching error items, a novel item quality evaluation criterion is proposed. The quality of each item is directly proportional to the degree of its matching error distance descent during the optimization process. Then, an attention mechanism based on the criterion is proposed and the objective function is iteratively refined by putting different attention weights on the matching error items. Thirdly, in the Gauss-Newton optimization framework for LiDAR localization, we point out that the Hessian matrix is essentially the sum of the Hessian matrices of high-quality and low-quality point correspondence subsets. To increase the dominance of the Hessian matrix derived from the high-quality point correspondences, the Hessian matrix of the estimated high-quality point correspondence subset in the last updating step is added to the current Hessian matrix. The proposed LiDAR localization is extensively evaluated on public and custom datasets. The experimental results demonstrate that the proposed mechanisms can effectively promote the LiDAR localization performance. We will make our source code open to serve as a new baseline for the robotic community.

Variable Time-Step Physics Engine with Continuous Compliance Contact Model for Optimal Robotic Grinding Trajectory Planning.

In the transition from virtual environments to real-world applications, the role of physics engines is crucial for accurately emulating and representing systems. To address the prevalent issue of inaccurate simulations, this paper introduces a novel physics engine uniquely designed with a compliant contact model designed for robotic grinding. It features continuous and variable time-step simulations, emphasizing accurate contact force calculations during object collision. Firstly, the engine derives dynamic equations considering spring stiffness, damping coefficients, coefficients of restitution, and external forces. This facilitates the effective determination of dynamic parameters such as contact force, acceleration, velocity, and position throughout penetration processes continuously. Secondly, the approach utilizes effective inertia in developing the contact model, which is designed for multi-jointed robots through pose transformation. The proposed physics engine effectively captures energy conversion in scenarios with convex contact surface shapes through the application of spring dampers during collisions. Finally, the reliability of the contact solver in the simulation was verified through bouncing ball experiments and robotic grinding experiments under different coefficients of restitution. These experiments effectively recorded the continuous variations in parameters, such as contact force, verifying the integral stability of the system. In summary, this article advances physics engine technology beyond current geometrically constrained contact solutions, enhancing the accuracy of simulations and modeling in virtual environments. This is particularly significant in scenarios wherein there are constant changes in the outside world, such as robotic grinding tasks.

Robotic grinding based on point cloud data: developments, applications, challenges, and key technologies

Robotic grinding based on point cloud data: developments, applications, challenges, and key technologies

A survey on socially aware robot navigation: Taxonomy and future challenges

Socially aware robot navigation is gaining popularity with the increase in delivery and assistive robots. The research is further fueled by a need for socially aware navigation skills in autonomous vehicles to move safely and appropriately in spaces shared with humans. Although most of these are ground robots, drones are also entering the field. In this paper, we present a literature survey of the works on socially aware robot navigation in the past 10 years. We propose four different faceted taxonomies to navigate the literature and examine the field from four different perspectives. Through the taxonomic review, we discuss the current research directions and the extending scope of applications in various domains. Further, we put forward a list of current research opportunities and present a discussion on possible future challenges that are likely to emerge in the field.

Study on the constant force control of aero-engine blade grinding robot considering time delay

Study on the constant force control of aero-engine blade grinding robot considering time delay

Collaborative improvement of profile accuracy and aerodynamic performance in robotic grinding of transonic compressor blade leading edge

Blade machining accuracy is critical to engine service performance. However, the intricate curvature characteristics and exceedingly thin dimensions of the leading edge present formidable challenges in achieving precise grinding. This study introduces a novel robotic belt grinding framework aimed at collaboratively enhancing the profile accuracy and aerodynamic performance of the leading edge. The irregular contact area and pressure distribution of the narrow rigid-flexible grinding interface are calculated based on the Semi-Hertz model. Additionally, a time-varying material removal contour (MRC) model is developed for precision grinding with the pyramid-structured abrasive belt. The non-linear equations governing dwell time are transformed into a convex quadratic optimization problem, and the solution is obtained using the fastest gradient method with constraints. Subsequently, the dwell time vector is converted into a feed rate value for robot programming. Experimental results demonstrate that the size and shape accuracy at three typical sections of the leading edge are effectively controlled within the desired range. The mean value of the line profile error is reduced to 0.019 mm, and the standard deviation of the surface profile error shows a noteworthy 32% improvement compared to the previous method. Furthermore, aerodynamic performance analyses are conducted for the ground airfoils using NUMECA. Numerical simulation results reveal that shape accuracy significantly influences the flow field state within the channel, given the precondition of qualified dimensional error. The isentropic efficiency is increased most by 4.67% by this dwell time control grinding.

An optimal reference iteration-based surface reconstruction framework for robotic grinding of additively repaired blade with local deformation

The additively repaired blade requires intelligent grinding process to restore the blade profile, and the essential prerequisite is to reconstruct a reliable reference surface at the repaired area with local deformation. In this paper, a novel surface reconstruction framework based on the optimal reference iteration in the v parameter direction is developed to overcome this challenging problem through three steps. In the framework, a parameter alignment algorithm based on the sorting features is proposed to align the fitted blade cross-section curves at first by considering the local deformation and curvature variation, while avoiding the alignment falling into local optimum. Then a curve discretization method based on the curvature features is presented to discretize the multi-section curves for preserving the high curvature variation features to the most extent. Based on these two steps, a curve fitting strategy based on the v-directional optimal reference iteration is suggested for surface reconstruction by virtue of the optimal reference principle and VMM (Variance-Minimization Matching) algorithm. Both the simulation and experimental results demonstrate the effectiveness of the proposed framework from the perspectives of the blade position errors and the profile accuracy after robotic grinding. The average point cloud error between the reconstructed model and the standard model is 0.018 mm, which is decreased by 51.7 % compared with the state-of-the-art method.

Evaluation of abrasive belt grinding performance in nickel-based superalloy robot grinding

ABSTRACT In order to evaluate the abrasive belt grinding performance, this paper proposes to conduct nickel-based superalloy robot abrasive belt grinding experiment based on different types of abrasive belts with multiple grit sizes. First, the single grinding performance evaluation is performed through five performance parameters: material removal rate, surface roughness, tangential force, specific grinding energy, and specific wear height. Then, based on the evaluation results of single grinding performance, a weighted evaluation function is established to comprehensively evaluate the grinding performance. It is found that the structured abrasive belt has relatively better grinding performance. The main factors affecting the abrasive belt grinding performance are identified through the evaluation results, with the effect of grit size being more significant. In addition, this paper analyzes the characteristics of material deformation based on the grinding subsurface microstructure, and discusses the effects of abrasive belts on material deformation.

Energy saving, load bearing and attachment mechanism on ice and frozen ground of biomimetic mechanical foot.

The frozen ground robot can be widely and prospectively applied in plentiful fields, such as military rescue and planet exploration. Based on the energy-saving, load-bearing, and attachment functions of reindeer hooves, we studied the kinematics of reindeer feet and designed a biomimetic energy-saving attachment mechanical foot (mechanical foot I) and two contrast mechanical feet (mechanical feet II and III). The energy-saving and load-bearing performances of the biomimetic mechanical foot were tested on a motion mechanics platform, which revealed this mechanical foot was adaptive to three types of ground (frozen ground, ice, and water ice lunar soil). Mechanical foot I possesses the functions of elastic energy storage and power consumption reduction, and its power range is from -2.77 to -27.85 W. Compared with mechanical foot III, the load-bearing ability of mechanical foot I was improved by the dewclaws, and the peak forces in the X, Y, and Z directions increased by about 2.54, 1.25 and 1.31 times, respectively. When mechanical foot I acted with more- smooth surface, the joint range of motion (ROM) increased, changes of the three-directional force at the foot junction decreased. The forces were the lowest on ice among the three types of ground, the X-, Y- and Z-directional changes were about 62.96, 83.7, and 319.85 N respectively, and the ROMs for the ankle joint and metatarsophalangeal joint of mechanical foot I were about 17.93° and 16.10°, respectively. This study revealed the active adaptation mechanism between the biomimetic mechanical foot and ice or frozen ground, and thus theoretically underlies research on the biomimetic mechanical foot.

Enhanced curve-based segmentation method for point clouds of curved and irregular structures

This paper proposes an improved method for model-based segmentation (MS) of curved and irregular mounded structures in 3D measurements. The proposed method divides the point cloud data into several levels according to the reasonable width calculated from the density of points. Then, it fits a curve model with 2D points for each level separately. The classification results of specific types are merged to obtain specific structural measurement data in 3D space. We use MS method, difference of normals based segmentation, region growing algorithm, constrained planar cuts, and locally convex connected patches as a control group. The results show that the proposed method achieves higher accuracy with a mean intersection merge ratio of more than 0.8238, at least 37.92% higher than other methods. The method proposed in this paper requires less time to process than other methods. Therefore, the proposed method effectively and efficiently segments the measurement data of curved and irregular mounded structures in 3D measurements. The method proposed in this paper has also been applied in the practical robotic grinding task. The root mean square error of the grinding amount is less than 2 mm, and good grinding results are achieved.

Self-reflective terrain-aware robot adaptation for consistent off-road ground navigation

Ground robots require the crucial capability of traversing unstructured and unprepared terrains and avoiding obstacles to complete tasks in real-world robotics applications such as disaster response. When a robot operates in off-road field environments such as forests, the robot’s actual behaviors often do not match its expected or planned behaviors, due to changes in the characteristics of terrains and the robot itself. Therefore, the capability of robot adaptation for consistent behavior generation is essential for maneuverability on unstructured off-road terrains. In order to address the challenge, we propose a novel method of self-reflective terrain-aware adaptation for ground robots to generate consistent controls to navigate over unstructured off-road terrains, which enables robots to more accurately execute the expected behaviors through robot self-reflection while adapting to varying unstructured terrains. To evaluate our method’s performance, we conduct extensive experiments using real ground robots with various functionality changes over diverse unstructured off-road terrains. The comprehensive experimental results have shown that our self-reflective terrain-aware adaptation method enables ground robots to generate consistent navigational behaviors and outperforms the compared previous and baseline techniques.

Terra-22: an aerial soil sampling in densely compacted agricultural fields

Soil sampling is used in agriculture to monitor fields and plan fertilizer application. This task is typically performed manually, but ground robots have recently been introduced. However, ground robots are often slow and heavy, which contributes to soil compaction. Fast-flying drones could provide an interesting alternative to ground robots. However, drones are severely limited in their payload and in the forces that they can apply to the soil. This paper presents the Terra-22, the first airborne system capable of sampling densely compacted agricultural soils. To do so, many challenges were addressed, including the development of (i) a high-power density drilling system that outperforms typical brushed DC gear motor by 39%, (ii) a drill design that is 48% lighter than traditional steel auger drills and that keeps cross-contamination under 4%, and (iii) a drill penetration rate controller that reduces the torque requirement by 33% and the axial force requirement by eight folds when compared to a constant penetration speed controller. Outdoor soil sampling tests in a corn field (sandy loam soil, compaction between 0.8 and 2 MPa) demonstrated a 94% success rate on flat terrain and a sampling duration under one minute.

Portable Planner For Enhancing Ground Robots Exploration Performance In Unstructured Environments

Portable Planner For Enhancing Ground Robots Exploration Performance In Unstructured Environments

Smart railways: the design and construction of an autonomous inspection and maintenance vehicle

The Railway Inspection and Maintenance Vehicle (RIMV) is a state-of-the-art autonomous ground robot for the railway environment. The robot has specialised hardware tailored for specific functionalities, ensuring optimal performance in its designated tasks. It incorporates a GPS, IMU, and encoder for robust localisation, and it integrates a LiDAR system and a depth camera for collision avoidance. Prioritising safety, the RIMV features an IP camera for real-time environmental monitoring and is equipped with emergency stop mechanisms. An Ubuntu Linux machine manages the robot’s data processing, complemented by a 5G Wi-Fi router for remote monitoring and operation when necessary. ROS2 is the primary platform for robot operations, standing out for its open-source solution and enhanced cybersecurity features. We validated the RIMV in a heritage railway setting with an ultrasonic inspection kit to detect rail-track defects. Beyond this specific application, the RIMV is versatile and designed to serve as a platform for other autonomous inspection and maintenance tasks within the railway sector. Its adaptability highlights its potential to enhance efficiency and safety in railway maintenance, offering invaluable assistance to human operators across varied scenarios.

The Application of SLAM in Life and Scientific Research

This article mainly focuses on the application of SLAM in technology and provides a literature review on the following aspects: 1) The application of SLAM in automatic parking of automobiles. This includes using AVP-SLAM for high-precision automatic parking in outdoor parking lots using convolutional neural networks, and using MOFISLM for high-precision automatic parking when indoor GPS signals are poor. 2) The application of SLAM in robotics. It includes three aspects. The first is using the GMapping algorithm to construct and locate maps with high accuracy, low computational complexity, and high robustness, enabling robots to plan the shortest path in unknown environments. The second is to use ORB-SLAM2, which can handle more complex situations, to navigate medical institution robots to improve hospital efficiency and reduce disease transmission rates. The third is for robots that perform tasks in complex and unknown terrains. The improved version of LAMP1.0, LAMP2.0, which is suitable for complex underground environments, can achieve multi robot collaborative cooperation with high accuracy and robustness. 3) Application of SLAM in the field of unmanned aerial vehicles. This article mainly elaborates on four aspects. The first is to use visual SLAM to achieve collaborative work between ground robots and drones, and to utilize the high camera advantage of drones to quickly build maps for ground robots, improve accuracy, and save time. The second is to utilize the application of VSLAM on unmanned aerial vehicles to contribute to agriculture. The third is ORB-SLAM3 to provide rapid and high-precision landing on deck for unmanned aerial vehicles in maritime missions. The fourth is using SLAM to assist unmanned aerial vehicles in landing in emergency or position situations (such, when GPS signals are lost).