In this paper, a region-determined localization method applied for unmanned ground vehicles (UGVs) is presented. The method aims to solve GNSS-denied localization problem using pole-like feature such as trees or street lights. The approach includes three parts: mapping, bounding, and localization. To map and reconstruct the environment, the hector mapping approach and circle-fitting method are adopted for the occupancy mapping and feature mapping. To bound out the available working region, we define the intersection area of features' enlarged radius and desired operating area as negative and positive virtual boundaries. While the robot is cruising, the likelihood detection method is adopted for obstacle searching and comparing. Using the detection's searching results as feedback reference, the Extended Kalman Filter (EKF) can modify the shifting between the GNSS signal and the true waypoints of the mowing robot. Three cruising demonstrations are presented to show the mapping and optimizing results. Different cases of demonstration represent different situations and potential issues.

Automation application in crop harvesting has increased in the past decades. Various types of harvesting robots are emerging in both commercial and research areas. One of the main challenges is the precision alignment of the gripper and the target crop. An undesired dislocation can harm both the gripper and the crop, which is mainly caused by uncertainties from the sensors and the manipulator. To solve the problem, the dual-camera setup is designed and implemented on a self-built robot. The perception of the tomato is done by a fixed depth camera and a camera without depth on the gripper. The proposed dual-camera image-based visual servoing (IBVS) controller is designed to deal with the image feedback from both cameras and the proof of asymptotically convergence is provided. Furthermore, the cumulative error compensation reduces the time for the harvesting process. The experiments were conducted in the greenhouse and tested under various conditions. The time cost is formulated as a function and the success picking rate of tomatoes is 68.4%.

The inaccessibility of data from standard sensor suites on closed-platform unmanned aerial vehicles (UAVs) has been a hindrance to developing a compatible visual-inertial odometry (VIO). Despite the advance of recent VIO research, these works often emphasize fusing detailed sensor models with available sensor data at relatively high frequencies. To address this issue, in this paper, we derive an innovation signal for an orientation measurement model on the unit quaternion group ${\mathbb{S}^3}$ based on the error-state Kalman filter (ESKF) framework. Leveraging the error-state formulation, the innovation signal directly exploits the geometric error representation on ${\mathbb{S}^3}$ instead of treating unit quaternions as ℝ4vectors. Flight experiments on a small commercial UAV (Fig. 1) have been carried out to compare the performance of the proposed ESKF with quaternion measurements on ${\mathbb{S}^3}$ (ESKF-Q) against the original ESKF framework. Experimental results demonstrate that while both representations of unit quaternion measurements in ESKF framework improve orientation estimates with unperturbed orientation measurement model, only the proposed ESKF-Q exhibits convergent state estimates in the presence of uncertainties in the orientation measurement model.

Traditional vertical take-off and landing (VTOL) aircraft can not achieve optimal efficiency for various payload weights and has limited mobility due to its under-actuation. With the thrust-vectoring mechanism, the proposed modular team UAV is fully actuated at certain attitudes. However, the attainable force space (AFS) differs according to the team configuration, which makes the controller design difficult. We propose an approximation to the AFS and a full-pose tracking controller with an attitude planner and a force projection, which guarantees the control force is feasible. The proposed approach can be applied to UAVs having multiple thrust-vectoring effectors with homogeneous agents. The simulation and experiment demonstrate a tilting motion during hovering for a 4-agent team.

Industrial robots play potential and important roles on labor-intensive and high-risk jobs. For example, typical industrial robots have been used in grinding process. However, the automatic grinding process by robots is a complex process because it still relies on skillful engineers to adaptively adjust several key parameters. Moreover, it might take a lot of time and effort to yield better grinding quality. Hence, this paper proposed a new framework of cyber-physical robot system with automatic zero-tuning optimization of the process parameters to achieve the desired quality. To overcome the unexpected difference between reality and simulation, proper system calibration can help in precise positioning in real environment, and the cloud database is constructed to record the relative data during the grinding process simultaneously. The proposed zero-tuning methodology combines both neural network (NN) model and genetic algorithm (GA) to generate the best combination of corresponding parameters to meet the desired quality. Experimental results showed that the average error of the output result was 8.93%. To compare the CNC machine, our solution shows more prominent role and potential in plumbing industry.

Traffic safety and efficiency are major objectives for Intelligent Transportation Systems (ITS) [item 1) in the Appendix], [item 2) in the Appendix]. A lot of research has been done on autonomous vehicles and Advanced Driver Assistance Systems, which exploit a variety of sensors in order to support the driver and/or autonomous driving (e.g., [item 3) in the Appendix]–[item 7) in the Appendix]).

This paper presents a complete perception system of moving point detection and target tracking for robustly following target humans in an unknown indoor dynamic environment. To detect moving points under grid-based formulation, a modified inverse observation model is proposed to overcome several frequently happened detection limitations. Next, related human-extraction techniques are proposed to filter out less possible clusters for detecting potential human target from these moving points. Finally, the multiple hypothesis tracking algorithm is implemented to deal with the data association problem for enhancing the reliability and robustness of the human tracking when measurements are noisy. Different levels of experiments have been performed to evaluate the effectiveness of the proposed algorithm framework.

In a video-based surveillance system, a mobile camera can provide dynamical and wider monitoring range and the video data transmitted from cooperative mobile cameras can be used to actively detect the objects of interest. However, it is a difficult task to accurately detect the moving objects from the image frames captured by the mobile cameras and the data flow of surveillance video from multiple cameras could be huge. The camera motion usually causes the shifting of static background as well as the moving objects in the captured image frames. In order to correctly estimate the motion of moving objects, a voting-based motion estimation algorithm is proposed to process the image frames captured by the mobile camera. Based on the estimation, a content-based video transmission mechanism is then implemented to further effectively decrease encoding cost and bandwidth utilization. The overall approach consists of voting-based motion estimation, moving object edges detection and content-based sampling coding at temporal and spatial scales. Without knowing the prior knowledge of camera motion, the motion estimation algorithm only utilizes the shifting information of edges of static background to estimate the camera movement. The shifting information is determined based on the voting decision of several representative regions of interest and the estimated motion is then used to compensate for the visual content obtained from the captured image frames. The proposed algorithms have been experimentally tested on several practical scenarios and it is demonstrated that, under limited network bandwidth, the transmitted image quality can be progressively achieved and the transmission bandwidth utilization can be effectively decreased.

In this paper, the platform of a two-wheeled mobile robot (TWMR) is studied and the bias errors about postures of TWMR, i.e., effects of skidding and slipping, are classified and modeling. The data of range sensors are processed as localization methods to eliminate the external error which encoder odometry cannot be detected. Two concepts about localization method fusion and error elimination architecture are proposed. The first one combines results of localization methods and determines the best results based on the performance of localization methods in different scenarios. The second one contains the former and makes the posture information more reliable. In addition, the methods could correct the encoder odometry with the knowledge of skidding and slipping.

This paper presents the design of dispersion movement and concentration movement for a multi-robot team to search for targets in an unknown environment. The goal of the dispersion movement is for the multi-robot team to spread out as widely as possible and explore any unknown targets without breaking the communication links among these robots. The movement direction is made based on only minimal available communication data. After one of the team members finding a target, other robots are commanded by the concentration algorithm to move to the same target location to, for example, retrieve the explored target or transport the target cooperatively. The only information used for designing the movement algorithms is the number of robots within the communication range of the robot. The proposed algorithms have been extensively tested by the simulation study. The simulation results demonstrate the feasibility and fast convergence of the proposed algorithms in several typical scenarios presented in this paper. Potential applications of the proposed algorithms include rescue in a damaged area, planetary exploration, military reconnaissance, and geophysical mapping.

In this paper we present the development of a visual servo controller to laterally navigate a mobile vehicle tracking the lane marker on a ground path automatically. Based on linear approximation of the lane marker and kinematics of the vehicle, we first derive the equations of motion for the system of lateral navigation. Then, utilizing the feedback linearization technique, we design the visual servo controller and, through a stability analysis, we can guarantee the tracking errors converge to zero exponentially. In the implementation, we adopt an efficient lane-marker detection system to measure the position and orientation of the lane marker relative to a look-ahead position of the vehicle. Finally, several experimental results are provided to validate the control performance.

This paper studies the integration and implementation of digital image processing techniques on the roadside camera for traffic monitoring and vehicle tracking. The image processing framework developed in this study is mainly composed of five stages: (1) pre-processing, (2) foreground segmentation, (3) shadow removal, (4) tracking, and (5) traffic parameters extraction. During the pre-processing stage, the information of road geometry is obtained and the camera is calibrated. At the foreground segmentation stage and shadow removal stage, moving vehicles are segmented from the original input images. To make the system more robust, an alpha-beta filter is used at the multi-vehicle tracking stage. Subsequently, related traffic parameters are extracted at the end of each tracking mechanism. The experimental results show that this system is capable of successfully extracting the traffic parameters, including the trajectory of the moving vehicles based on the image sequences captured by a digital camera on a free flow traffic in the daytime..

When sensory and actuation devices in a control system are exchanging data through one common communication medium, the sharing of communication bandwidth will induce unavoidable data latency and might degrade the control performance. Hence, the utilization of communication resource and the requirement of control specification should be analyzed and properly designed when implementing a control system over a network architecture. In this paper, we analyze the performance of information sharing of multiple cooperative agents over one communication network, and propose design methodologies of guaranteeing acceptable control and communication performance in a networked control system. In particular, we study the relationship between the sampling rates of a control system,and the transmission rates of a communication network, and then utilize an integrated networked control design chart to help select design parameters and visualize overall system performance at different sampling and transmission rates. Based on the design parameters selected, the communication modules by utilizing deadband control and state estimation are presented for guaranteeing both control and communication performance. Simulation studies are conducted in a network-and-control simulation tool that is developed on the Matlab/Simulink platform and is used to demonstrate the proposed design methodologies. Both the analysis and simulation results illustrate the characteristics of designing mechanisms between control and communication performance and show the improvement of implementing the proposed communication modules.

This paper discusses a design methodology of cooperative trajectory generation for multi-robot systems. The trajectory of achieving cooperative tasks, i.e., with temporal constraints, is constructed by a nonlinear trajectory generation (NTG) algorithm. Three scenarios of multi-robot tasking are proposed at the cooperative task planning framework. The NTG algorithm is, then, used to generate real-time trajectory for desired robot activities. Given robot dynamics and constraints, the NTG algorithm first finds trajectory curves in a lower dimensional space and parameterizes the curves by a set of B-spline representations. The coefficients of the B-splines are further solved by sequential quadratic programming to satisfy the optimization objectives and constraints. The NTG algorithm has been implemented to generate real-time trajectories for a group of cooperative robots in the presence of spatial and temporal constraints. Finally, an illustrated example of cooperative task planning with temporal constraints is presented.

As has been realized, the flexible-link manipulators have attracted more and more attention from robot control theorists and/or robot users because of its various potential advantages. But since the control degree-of-freedom is much less than that of the system, many control strategies which succeed in the conventional rigid robot control cannot be directly used in the flexible robot control problems. In this paper, a nonlinear control scheme has been proposed as a solution to these control problems. In particular, to cope with the existing model uncertainty, an adaptive version of this nonlinear control law has been proposed. Stability proof of the overall closed-loop system is then given via Lyapunov analysis. In addition, extensive experimental results are also provided to demonstrate the effectiveness of the proposed controller.

In this paper, we tackle the problem of nonlinear adaptive hybrid control of constrained robots with flexible links. According to the physical properties of a flexible manipulator, a two time-scale approach, namely singular perturbation approach, is further utilized for thorough analysis and general controller design. It is shown that asymptotic motion tracking can be effectively achieved, whereas the force regulation errors can be made arbitrarily small. For demonstration of the controller performance, experiments of a two-link flexible manipulator were performed for the proposed controller and satisfactory results observed.

Because the control DOF (Degree of Freedom) is much less than the motion DOF when a flexible manipulator is commanded to track a desired trajectory, many control strategies that succeed in conventional rigid-robot control cannot be directly applied to solve the flexible robot control problem. In this work, an adaptive variable structure scheme has been proposed to solve such a problem. The full nonlinear dynamics of the whole system are all taken into account for the control design. To alleviate the chattering phenomenon commonly seen in variable structure type of control, a saturation type adaptive scheme has also been proposed. For verification of the effectiveness of the proposed controller, a two-link flexible manipulator is built up and the promise of the controller is experimentally demonstrated.