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Visual servoing (VS) control has seen wide adoption in harvesting robots. However, parameter calibration is cumbersome, which makes the use of VS robotic systems inconvenient. Besides, dynamic fruits usually lead to a degeneration of control while tracking. To overcome the drawbacks, we present a new image-based uncalibrated visual servoing (IBUVS) control approach, consisting of a hybrid visual configuration and an adaptive tracking controller, referred to as hybrid-IBUVS. Specifically, our hybrid-IBUVS employs an eye-in-hand camera and a fixed red–green–blue-depth camera to construct a hybrid VS system, basing on multiobject detection and edge-computing technologies. Meanwhile, we also propose adaptive laws to online estimate the uncalibrated parameters of the cameras and robot dynamics. Furthermore, our hybrid-IBUVS uses an adaptive tracking controller to guarantee the harvesting robot to track a predefined trajectory to approach a fruit target. By Lyapunov stability theory, asymptotic convergence of the proposed control scheme is rigorously proven. Experimental results demonstrate the effectiveness of the proposed scheme. All shown results supported the research claims. [ABSTRACT FROM AUTHOR]

Loop closure detection is the crucial issue of simultaneous localization and mapping in the field of autonomous driving and robotics. In outdoor large-scale and complex environments, the existing LiDAR-based methods still inevitably suffer from viewpoint, condition changes, and perceptual aliasing. To effectively fill the aforementioned drawbacks, in this article, a novel LiDAR-based multimodule cascaded Siamese convolutional neural networks is developed, named MMCS-Net, which simulates the human-eye mechanism to extract more discriminative and generic feature descriptors. The MMCS-Net is mainly composed of three complementary modules: Siamese full convolutional (CA_SFC) module with cascaded attention, rotation-invariant and topological feature enhancement (RT_E) module, and feature uniqueness enhancement and aggregation compression (UE_AC) module. In particular, the graph structure employed in RT_E can explicitly encode the local topological correlations of point clouds in terms of intensity and geometric clues in parallel. Extensive comparative experiments on KITTI, NCLT, LGSVL, and real vehicle datasets prove that our proposed method outperforms the state-of-the-art methods, and shows high robustness while ensuring the real-time requirements of resource-constrained robots. [ABSTRACT FROM AUTHOR]

In this article, we presents an effective semantic grounding scheme to long-term mobile robots for dynamic object search in open/dynamic home environments. The challenge of this strategy lies in dealing with situations where the robot suffers the home environment with an unknown layout of the object and the target object is moved without the robot’s knowledge. Therefore, a general probabilistic model and a semantic model are established by investigating typical spatial location relations between object and room type, in order to guide the robot to prioritize the search effort for spaces that are most promising to find the target object. Furthermore, a semantic grounding solution is proposed to address the mutual independence between objects, endowing the robot with inference capability for dynamic object search. Also, our proposal can be shared with other robots that have not explored the environment in order to perform object search tasks. The presented scheme is fully evaluated by extensive experiments in the real-world environment. The results demonstrate the validity of our approach by comparing it with the other three schemes in terms of search time and trajectory length, and show that the proposed method allows the mobile robot to efficiently and robustly find the dynamic object while achieving the human-like performance. [ABSTRACT FROM AUTHOR]

In this article, a new stabilization control strategy is designed for a kind of nonholonomic system under full-state constraints and external disturbances. First, a stabilization controller for the first input is proposed to divide the entire control process into two stages. Then, using state transformations, the remaining original system is converted into an unconstrained system for which the stabilization controller is designed based on dynamic surface control. Stability analysis has established that the developed stabilization control algorithm can achieve the desired stabilization control objectives and the predefined full-state constraints. Meanwhile, all closed-loop signals keep bounded in the whole control process. Simulation and experiment results are given finally to demonstrate the effectiveness of the proposed stabilization control algorithm. [ABSTRACT FROM AUTHOR]

In this article, we address the distributed event-triggered leader–follower formation tracking control problem of general linear multiagent systems with a dynamic leader in sampled-data settings. A novel locally computable state-estimate-based event generator is established for each follower agent to regulate the interagent communication at each sampling instant. Then, we propose a distributed formation tracking protocol based on the triggered sampled information such that the formation tracking control problem can be formulated as a stability-analysis problem of the closed-loop formation error dynamics. The event generator and formation tracking controller gains can then be co-designed using the feasible linear matrix inequality conditions that are derived from Lyapunov-based stability-analysis methods that guarantee the ultimate boundedness of the closed-loop formation error dynamics. Finally, numerical simulations along with experiment implementations were conducted for a group of linearized unicycle-type mobile robots to demonstrate the effectiveness and advantages of the proposed method. [ABSTRACT FROM AUTHOR]

Ballbots are omnidirectional self-balancing platforms that can be exploited in many applications to detect, track, or interact with objects or humans, such as a service robot. Ballbot will enable mobile robots to stand tall and move elegantly through busy environments. However, maintaining equilibrium through synchronization of motion between the ball and the body of a Ballbot is still an open research problem. This article presents a synchronization control (SC) design, with synchronization and coupling errors for Ballbots to stabilize the body and control ball transfer simultaneously. The proposed SC method is applied to the two 2-D planar models of a Ballbot robot. The dynamic model of the Ballbot is derived, and parameters are identified online using the intelligent particle swarm optimization method. The proposed controller is proven to guarantee asymptotic convergence to zero errors in tracking and synchronization. The stabilizing and transferring problems are investigated through several simulations and experiments by using an actual Ballbot platform. Moreover, the controller performance is compared with an augmented proportional derivative controller and a partial feedback linearization controller. The results and comparisons demonstrate a superior stabilization accuracy of the proposed SC method. [ABSTRACT FROM AUTHOR]

An adaptive robot control approach that can achieve an accurate approximation of the complex robot dynamics without knowledge unlearning in the case of multiple sequential tasks is proposed in this article. A locally weighted learning (LWL) model with automatic structure growth according to the size of the learning domain is introduced to approximate unknown robot dynamics, and a composite learning technique with regressor extension is applied to improve parameter convergence. The LWL ensures that learning in one area of the learning domain does not cause unlearning in another area, and the composite learning theoretically guarantees the identification performance of the LWL model. By the exploitation of both stored data in memory and instantaneous data, parameter convergence in the LWL model is achieved under a more achievable interval-excitation condition than the stringent persistent-excitation condition. This further enhances the trajectory tracking performance for multiple sequential tasks, which is generally not achievable by existing approximation-based adaptive robot control approaches. Experimental studies have verified the superiority of the proposed approach over prevalent approaches. [ABSTRACT FROM AUTHOR]

Obstacle avoidance is a sizable challenge for robots working in a multiple dynamic obstacle environment. Conventional obstacle avoidance methods often require complex calculations to process all dynamic obstacles detected in the scene. Avoiding dangerous moving objects in time is often difficult when obstacles are many. In this article, we propose a robot obstacle avoidance method based on motion saliency for dynamic environments. First, we use segmented dynamic objects to calculate the saliency of dynamic objects and segment dangerous dynamic objects. Then, we use the B-spline curve to predict the movement of dangerous dynamic objects and combine it with the nonlinear model predictive control method to avoid dangerous obstacles in the dynamic environment of the robot. Considering the motion behavior of different dynamic objects, our obstacle avoidance strategy is to generate obstacle-free paths by adjusting the speed of the robot or connecting the centers of the rolling variable-size circles. Finally, we conduct a series of experiments to verify the effectiveness of our method. [ABSTRACT FROM AUTHOR]

This article presents the field-programmable gate array (FPGA) implementation of a particle swarm optimization (PSO)-based nonlinear model-predictive control (NMPC) for applications with millisecond timescales and resource-constrained embedded systems. A custom hardware circuit architecture of an NMPC controller, which mainly includes phase-locked loop clock, universal asynchronous receiver transmitter interface, trigonometric function, random number generator, objective function, and PSO solver modules, is implemented on the FPGA based on a hardware description language specification. It employs parallelism, pipelining, and specialized numerical formats to enhance the online computation performance of NMPC. In addition, the partial parallel expansion calculations of the fitness (objective) function and the particle position update are adopted for trading off the computational performance against resource usage. The hardware-in-the-loop experimental results through controlling a wheeled mobile robot model demonstrate that the design is capable of computing the required control action in real time by using a midrange FPGA, while still offering good control performance. [ABSTRACT FROM AUTHOR]

In this article, we studied an optimal formation control problem of general integrator chain multiagent systems with strongly convex local cost functions. The control goal is to design appropriate controllers for the agents such that they reach a desired formation shape and the global cost function is minimized. To achieve the goal, a modular control scheme with two modules is proposed. First, in Module I, an optimal formation signal generator is designed, whose outputs asymptotically converge to the minimizer of the global cost function. Second, in Module II, by employing the outputs into the feedback loop as the agents’ reference trajectories, some linear-tracking controllers are designed for the agents to track the references asymptotically. With both modules, the proposed control scheme realizes the optimal formation control goal asymptotically. Moreover, the proposed control scheme is also applied to solving an optimal formation control problem of multiple mobile robots, where the robots’ total “relative” weighted squared moving distance requires minimization. The simulations and experiments validate the effectiveness and practicality of the proposed control scheme. [ABSTRACT FROM AUTHOR]

The four-wheel omnidirectional mobile robot usually suffers disturbed or unstable lateral motion under harsh terrain conditions (such as uneven or oiled ground). Generally for such a challenging situation, the lumped disturbances and interconnected states render available coupling solutions difficult to achieve demand-satisfied performance. This article proposes a novel decoupled fractional supertwisting sliding mode control (FST-SMC) method by i) constructing an inverse system-based decoupling to form a pseudolinear composition system; ii) presenting an enhanced nominal sliding law for chattering mitigation, and iii) designing an unbiased multilayer fuzzy estimator with gain-learning capacity to compensate for the lumped disturbances actively. Given that the identified disturbances can be directly reflected in the FST-SMC law, this method guarantees an accurate and robust control without causing gain overestimation. Theoretical analysis is offered to verify the asymptotic stability. Under harsh terrain conditions, experimental results validate the effectiveness of the proposed FST-SMC method. [ABSTRACT FROM AUTHOR]

Human–robot collaborative assembly (HRCA) is vital for achieving high-level flexible automation for mass personalization in today’s smart factories. However, existing works in both industry and academia mainly focus on the adaptive robot planning, while seldom consider human operator’s intentions in advance. Hence, it hinders the HRCA transition toward a proactive manner. To overcome the bottleneck, this article proposes a multimodal transfer-learning-enabled action prediction approach, serving as the prerequisite to ensure the proactive HRCA. First, a multimodal intelligence-based action recognition approach is proposed to predict ongoing human actions by leveraging the visual stream and skeleton stream with short-time input frames. Second, a transfer-learning-enabled model is adapted to transfer learnt knowledge from daily activities to industrial assembly operations rapidly for online operator intention analysis. Third, a dynamic decision-making mechanism, including robotic decision and motion control, is described to allow mobile robots to assist operators in a proactive manner. Finally, an aircraft bracket assembly task is demonstrated in the laboratory environment, and the comparative study result shows that the proposed approach outperforms other state-of-the-art ones for efficient action prediction. [ABSTRACT FROM AUTHOR]

In this article, an effective solution is presented for efficient robotic object search by leveraging metric-topological map. Based on the reference of objects, we construct a novel metric-topological map that is particularly suitable for object search, where the topological nodes represent the object-related locations in the environment. To effectively determine the creation of nodes on the map, a rapid adjustment method of the robot viewing angle is introduced. Compared with traditional maps, the map built in this article not only removes redundant nodes irrelevant to objects, but also facilitates the mobile robot to locate the target object. Besides, inspired by human search behavior, a search strategy is proposed by considering the tradeoff between the current position of the robot and the distance from the robot to the promising location of finding the target object, in order to further improve the search efficiency. The effectiveness and efficiency of our solution are verified through comparative experiments in both simulation and real-world environments. Results show that the presented approach always allows the mobile robot to find the target object efficiently and reliably while achieving humanlike search behavior. [ABSTRACT FROM AUTHOR]

In this article, we propose a deep reinforcement learning (DRL) algorithm as well as a novel tailored neural network architecture for mobile robots to learn navigation policies autonomously. We first introduce a new feature extractor to better acquire critical spatiotemporal features from raw depth images. In addition, we present a double-source scheme so that the experiences are collected from both the proposed model and a conventional planner alternatively based on a switching criterion to provide more diverse and comprehensive samples for learning. Moreover, we also propose a dual-soft-actor–critic architecture to train two sets of networks with different purposes simultaneously. Specifically, the primary network aims to learn the autonomous navigation policy, while the secondary network aims to learn the depth feature extractor. In this way, the learning performance can be improved through decoupling representation learning from policy learning and training the feature extractor separately with more specific goals. Experimental results have demonstrated the remarkable performance of the proposed model. It outperforms both the conventional and the state-of-the-art DRL-based methods consistently in terms of success rate. Meanwhile, it also exhibits higher trajectory quality and better generalization capability compared to existing DRL-based methods. Videos of our experiments are available at https://youtu.be/evjU6bOU3UY or OneDrive. [ABSTRACT FROM AUTHOR]

The rapidly exploring random tree and its variants (RRTs) have been widely adopted as the motion planning algorithms for mobile robots. However, the trap space problem, such as mazes and S-shaped corridors, hinders their planning efficiency. In this article, we present a generalized Voronoi diagram (GVD)-based heuristic path planning algorithm to generate a heuristic path, guide the sampling process of RRTs, and further improve the motion planning efficiency of RRTs. Different from other heuristic algorithms that only work in certain environments or depend on specified parameter setting, the proposed algorithm can automatically identify the environment feature and provide a reasonable heuristic path. First, the given environment is initialized with a lightweight feature extraction from the GVD, which guarantees that any state in the free space can be connected to the feature graph without any collision. Second, to remove the redundancy of feature nodes, a feature matrix is proposed to represent connections among feature nodes and a corresponding feature node fusion technique is utilized to delete the redundant nodes. Third, based on the GVD feature matrix, a heuristic path planning algorithm is presented. This heuristic path is then used to guide the sampling process of RRTs and achieve real-time motion planning. The proposed GVD feature matrix can be also utilized to improve the efficiency of the replanning. Through a series of simulation studies and real-world implementations, it is confirmed that the proposed algorithm achieves better performance in heuristic path planning, feature extraction of free space, and real-time motion planning. [ABSTRACT FROM AUTHOR]

This article introduces a secure tunnel fast marching tree motion planning algorithm (ST-FMT*) to provide a secure and optimal path quickly for a mobile robot. The proposed ST-FMT* consists of preprocessing and exploring procedures, which are responsible for establishing a secure tunnel and optimizing the path, respectively. In the preprocessing process, the generalized Voronoi graph is adopted to build an equidistant roadmap and generates an initial collision-free solution rapidly. Then, a secure tunnel is established via the minimum distance from the obstacles to the initial solution to facilitate the concentration of sampling. In the exploration process, the FMT* with uniform sampling within the secure tunnel is utilized to find the optimal solution with high computational efficiency. The theoretical analyses of the ST-FMT* are provided. In a series of scenarios evaluation, the ST-FMT* exhibits fast convergence to the optimal solution in different environments. Besides, the effectiveness of the ST-FMT* is verified in a transportation experiment using a Turtlebot2 mobile robot. [ABSTRACT FROM AUTHOR]

In this article, the image-based visual servoing (IBVS) problem for mobile robots with visibility constraints is studied by using a policy-based deep reinforcement learning (DRL) approach. First, the classical IBVS (C-IBVS) method and its feature-loss problem are introduced. Then, a DRL-based IBVS method is presented to solve the feature-loss problem and improve the servo efficiency.Specifically, the formulation of the C-IBVS controller is inherited by the designed controller to ensure the analytical stability, and a policy-based DRL algorithm is proposed to design an adaptive law for tuning the controller gain in the continuous space, which can maintain the feature in the field of the view of the camera as well as improving the servo efficiency. Finally, the effectiveness of the proposed method is demonstrated by various comparative experiments. [ABSTRACT FROM AUTHOR]

Mobile medical robots have been widely used in various structured scenarios, such as hospital drug delivery, public area disinfection, and medical examinations. Considering the challenge of environment modeling and controller design, how to achieve the information from the human demonstration in a structured environment directly arouse our interests. Learning skills is a powerful way that can reduce the complexity of algorithm in searching space. This is especially true when naturally acquiring new skills, as mobile medical robot must learn from the interaction with a human being or the environment with limited programming effort. In this article, a learning scheme with nonlinear model predictive control (NMPC) is proposed for mobile robot path tracking. The learning-by-imitation system consists of two levels of hierarchy: in the first level, a multivirtual spring-dampers system is presented for imitation of the mobile robot's trajectories; and in the second level, the NMPC method is used in the motion control system. The NMPC strategy utilizes a varying-parameter one-layer projection neural network to solve an online quadratic programming optimization via iteration over a limited receding horizon. The proposed algorithm is evaluated on a mobile medical robot with an emulated trajectory in simulation and three scenarios used in the experiment. [ABSTRACT FROM AUTHOR]

This article presents a fixed-time leader–follower formation control protocol for a swarm of unicycle-type mobile robots with visibility and performance constraints. Under a vision-based formation control framework, mobile robots calculate their control signals with only the local relative distance and bearing angle provided by onboard stereo or RGB-D cameras. Since the sensing capability of the fixed onboard cameras is subject to limited range and angle of view, the field-of-view constraints are considered to ensure that each follower can always detect its leader and meanwhile avoid collision with the leader. The time-varying and asymmetric performance constraints on formation tracking errors are applied to guarantee the transient and stead-state performance. Furthermore, the proposed control protocol can be extended to address the control design problem without constraint requirements. Based on the fixed-time control design and Lyapunov analysis, formation tracking errors are shown to converge to a small neighborhood of zero in fixed settling time. Simulation and experiment studies are performed to illustrate the effectiveness of the formation control protocol. [ABSTRACT FROM AUTHOR]

This article proposes a robust model predictive control (MPC) algorithm for the tracking problem of wheeled mobile robots. The robots are subject to bounded disturbances and various practical constraints. Particularly, the incremental input constraint is introduced in the consideration of the safety and comfortability needs in real life. Conditions on the acceleration of the leader robot are derived to guarantee the satisfaction of the incremental input constraint of follower robot. To compensate for the effect of disturbances, a disturbance observer is designed to obtain the estimation of the disturbances, which together with the optimal control input of MPC optimization is contained in the actual control input. Also, a novel quadratic robustness constraint is developed to handle the disturbance estimation error, which allows the designer to balance the initial feasible region and control performance. The proposed algorithm can ensure recursive feasibility, robust constraint satisfaction, and closed-loop stability. Finally, both simulation and experiment results are provided to verify the theoretical properties. [ABSTRACT FROM AUTHOR]

This article proposes, a novel obstacle avoidance algorithm for a mobile robot based on finite memory filtering (FMF) in unknown dynamic environments. To overcome the limitations of the existing dynamic window approach (DWA), we propose a new version of the DWA, called the finite distribution estimation-based dynamic window approach (FDEDWA), which is an algorithm that avoids dynamic obstacles through estimating the overall distribution of obstacles. FDEDWA estimates the distribution of obstacles through the FMF, and predicts the future distribution of obstacles. The FMF is derived to minimize the effect of the measurement noise through the Frobenius norm, and covariance matrix adaptation evolution strategy. The estimated information is used to derive the control input for the robust mobile robot navigation effectively. FDEDWA allows for the fast perception of the dynamic environment, and superior estimation performance, and the mobile robot can be controlled by a more optimal path while maintaining real-time performance. To demonstrate the performance of the proposed algorithm, simulations, and experiments were carried out under dynamic environments by comparing the latest dynamic window for dynamic obstacle, and the existing DWA. [ABSTRACT FROM AUTHOR]

High accuracy of terrain identification is essential for intelligent control of tracked mobile robots. In this article, a learning-based identification framework is proposed to achieve precise driving torque prediction. Experiments under straight-line and steady-state turning maneuvers were conducted to develop terrain identification in three similar terrains. A multiple deep belief networks is applied as the identification layer with three kinds of signal sources. An equivalent weight algorithm with training experience effectively integrates the results from different signal sources to improve the identification accuracy. With the experiment and identification results, a method combining numerical approximation and Gaussian process (GP) is presented to predict driving torque. A combined Gaussian kernel with long- and short-term characteristics is selected to enhance prediction performance. The results from combined signal sources under straight-line maneuvers yield over 98% accuracy, which exceeds that from other sources. The integration algorithm obviously improves the identification accuracy and stability compared with a single signal source. The influence of window length in GP is explored with hyperparameters and results. The performance of torque prediction in different terrains is analyzed with inner and outer tracks. Compared with existing methods, the results validate the effectiveness and superior performance of the proposed framework. [ABSTRACT FROM AUTHOR]

This article investigates the target docking problem for nonholonomic mobile robots using relative position and orientation measurements of the target. Considering the fact that target docking operations generally happen in unstructured environments where obstacles are left lying around, the whole docking process is divided into two phases, namely the approaching phase and the autonomous docking phase. In the approaching phase, the robot navigates in the operational environment until it reaches a switching region around the target, which triggers the autonomous docking phase. To account for the noise-corrupted and intermittent target observations, an extended Kalman filter-based relative pose estimation algorithm is designed to estimate the position and orientation of the target in the local reference frame of the robot. To achieve a perfect docking operation in the autonomous docking phase, which requires a zero-impact angle and a zero-lateral offset at the final engagement stage, a practical solution is proposed where motion planning and tracking control of the robot are incorporated into a unified scheme. Unlike most of the existing docking approaches in the literature which only account for static targets, our proposed strategy addresses the moving target docking problem as well. Real robot experiments have been performed to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This article presents an algorithm termed as multiobjective dynamic rapidly exploring random (MOD-RRT*), which is suitable for robot navigation in unknown dynamic environment. The algorithm is composed of a path generation procedure and a path replanning one. First, a modified RRT* is utilized to obtain an initial path, as well as generate a state tree structure as prior knowledge. Then, a shortcuting method is given to optimize the initial path. On this basis, another method is designed to replan the path if the current path is infeasible. The suggested approach can choose the best node among several candidates within a short time, where both path length and path smoothness are considered. Comparing with other static planning algorithms, the MOD-RRT* can generate a higher quality initial path. Simulations on the dynamic environment are conducted to clarify the efficient performance of our algorithm in avoiding unknown obstacles. Furthermore, real applicative experiment further proves the effectiveness of our approach in practical applications. [ABSTRACT FROM AUTHOR]

This article investigates the path-guided containment maneuvering of networked two-wheeled mobile robots with multiple virtual leaders moving along multiple parameterized paths. A two-level cooperative control architecture is proposed to achieve a containment formation. At the kinematic level, distributed guidance laws and path update laws are developed for mobile robots and virtual leaders, respectively. Containment maneuvering errors are involved in the path update laws design to enforce the coordination between mobile robots and virtual leaders. At the kinetic level, two extended state observers are utilized to estimate the unknown uncertainties existing in surge and yaw dynamics. Then, antidisturbance kinetic control laws are developed through the estimated uncertainties. By using the proposed controllers, the states of mobile robots not only converge to the convex hull spanned by multiple virtual leaders, but also keep a certain formation pattern specified by the convex combination of virtual leaders. The input-to-state stability of the closed-loop path-guided containment maneuvering system is analyzed through Lyapunov theory. The effectiveness of the presented method is verified via experiment on CSICET-MR01 mobile robots. [ABSTRACT FROM AUTHOR]

This article addresses the problem of leader–follower formation control of mobile robots using only onboard monocular cameras that subjected to visibility constraints. An adaptive image-based visual servoing control strategy was proposed following the prescribed performance control methodology. First, the leader–follower visual kinematics in the image plane and an error transformation with predefined performance specifications are presented. Then, an adaptive control law with online estimating the inverse height between the optical center of camera and the single feature point attached to leader is designed to ensure the global stability of the closed-loop system. Finally, the applicability and performance of the proposed control scheme are demonstrated by the numerical simulations and hardware experiments. Compared with other formation control schemes, our solution relies only on onboard visual sensors without communication, since it does not need the relative angle/distance between the robots, or the velocity of the leader. Moreover, it guarantees the prescribed transient and the steady-state performance besides the visibility constraints. [ABSTRACT FROM AUTHOR]

This article addresses the task of mobile robot localization for indoor navigation. In this article, we propose a novel indoor localization system based on separated sonar sensors, which can be deployed in large-scale indoor environments conveniently. In our approach, the separated sonar receivers deploy on the top ceiling, and the mobile robot equipped with the separated sonar transmitters navigates in the indoor environment. The distance measurements between the receivers and the transmitters can be obtained in real time from the control board of receivers with the infrared synchronization. The positions of the mobile robot can be computed without accumulative error. And the proposed localization method can achieve high precision in the indoor localization tasks at a very low cost. We also present a calibration method based on the simultaneous localization and mapping to initialize the positions of our system. To evaluate the feasibility and the dynamic accuracy of the proposed system, we construct our localization system in the Virtual Robot Experimentation Platform simulation platform and deploy this system in a real-world environment. Both the simulation and real-world experiments have demonstrated that our system can achieve centimeter-level accuracy. The localization accuracy of the proposed system is sufficient for robot indoor navigation. [ABSTRACT FROM AUTHOR]

In many applications, it is necessary for motion planning planners to get high-quality solutions in high-dimensional complex problems. In this article, we propose an anytime asymptotically optimal sampling-based algorithm, namely, informed anytime fast marching tree (IAFMT*), designed for solving motion planning problems. Employing a hybrid incremental search and a dynamic optimal search, the IAFMT* fast finds a feasible solution; if time permits, it can efficiently improve the solution toward the optimal solution. This article also presents the theoretical analysis of probabilistic completeness, asymptotic optimality, and computational complexity on the proposed algorithm. Its ability to converge to a high-quality solution with efficiency, stability, and self-adaptability has been tested by challenging simulations and a humanoid mobile robot. [ABSTRACT FROM AUTHOR]

In this article, an adaptive neural network control scheme is presented for an uncertain wheeled mobile robot (WMR) with velocity constraints and nonholonomic constraints. In practice, dynamic parameters of the system, which may change in some conditions, are hard to obtain precisely, and the velocity of the WMR should be constrained for safety. To deal with the uncertainty of the robot, adaptive neural networks are used to approximate unknown robotic dynamics, and the barrier Lyapunov function is used to guarantee the constraint on velocity. The tracking error of the closed-loop system is proven to converge to a small neighborhood of zero. Both simulation studies and practical experiments are provided to illustrate the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

This article investigates the fully distributed observer-based adaptive fault-tolerant synchronization problem (SP) of multiagent systems with event-triggered control mechanisms. First, a nonlinear and discontinuous adaptive observer-based event-triggered fault-tolerant controller is proposed for each agent to overcome the occurrence of unknown faults and unmeasurable full states of the controlled system. Besides, an adaptive triggering function consisting of state-dependent and time-dependent threshold is developed to adjust the parameter of triggering threshold online. Then, a modified nonlinear and continuous observer-based controller with adaptive ETC strategy is developed to overcome the chattering phenomenon from the discontinuous controller. It is proved that under two controllers, the SP not only can be achieved in a fully distributed way without continuous communication for both the controller updates and the triggering condition detecting but also exclusion of “Zeno behavior” can be realized. Finally, the effective algorithms can be verified by giving numerical simulations related to mobile robots. [ABSTRACT FROM AUTHOR]

Place recognition remains a challenging problem under various perceptual conditions, e.g., all weather, times of day, seasons, and viewpoint shifts. Different from most of the existing place recognition methods using pure vision, this article studies light detection and ranging (LiDAR) based approaches. Point clouds have some benefits for place recognition since they do not suffer from illumination changes. On the other hand, they are dramatically affected by structural changes from different viewpoints or across seasons. In this article, a novel LiDAR-based place recognition system is proposed to achieve long-term robust localization, even under severe seasonal changes and viewpoint shifts. To improve the efficiency, a compact cylindrical image model is designed to convert three-dimensional point clouds to two-dimensional images representing the prominent geometric relationships of scenes. The contexts (buildings, trees, road structures, etc.) of scenes are utilized for efficient place recognition. A sequence-based temporal consistency check is also introduced for postverification. Extensive real experiments on three datasets (Oxford RobotCar , NCLT , and DUT-AS) show that the proposed system outperforms both state-of-the-art visual and LiDAR-based methods, verifying its robust performance in challenging scenarios. [ABSTRACT FROM AUTHOR]

In this article, the global consensus problem of ring-networked nonholonomic systems with a saturated input is considered. Based on the Lyapunov method and appropriate related technologies, the stabilization of the nonholonomic systems is achieved toward consensus. Assuming that each agent has its own position information available in the global coordinates and can obtain the position information of its neighbors, global consensus is achieved asymptotically by using the designed distributed saturated controllers. A wireless local area network is set up with a local computer as the controller, having data transmitted through the wireless network so that the movement of the robots can be controlled. Consistent simulation and experimental results are presented to illustrate the practical design and theoretical analysis. [ABSTRACT FROM AUTHOR]

In this article, we propose an efficient algorithm for cornering trajectory planning avoiding slip based on dynamics for differential-wheeled mobile robots (DWMRs). In general, slip between tires and the ground increases odometry errors. Hence, to consider not only full dynamics, including actuators, but also slip avoidance, we formulate a complex planning problem, in which the following essential constraints are imposed: longitudinal and lateral force constraints to avoid slip and motor control input bounds for DWMR dynamics, including actuators. Given a single corner, the proposed trajectory is divided into three turning sections, which are composed of multiple intervals. In order to satisfy all constraints, the intervals are then split into several subintervals, which are with constant or time-varying control inputs based on the bang–bang principle or on force constraint bounds for optimal acceleration. We then find two local minimum trajectories by analyzing the time trend w.r.t. the turning radius for cornering. These are the low-velocity decrease trajectory and the shortest distance trajectory. The time-optimal solution is determined by the minimum of the local minima. Both simulations and experiments are conducted to verify the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This article presents a closed-loop position control of a mobile robot, which is capable of moving from its current position to a target point by manipulating its linear and angular velocities. The main objective of this article is to modify an existing control law based on the kinematic model to improve the response when the robot is backwards oriented and to reach the destination point in less time and with a shorter trajectory. Stability of the proposed control law is validated by Lyapunov Criterion. Some procedures are implemented to test this approach both in simulation with MATLAB, and experimentally with the Khepera IV robot. [ABSTRACT FROM AUTHOR]

This article presents a novel method to generate a trajectory for a two-wheeled mobile robot moving in an uncertain environment where only a few way-points are available to reach a nearby target state. The proposed method interpolates way-points by the combination of a straight line and a fifth-order Bézier curve along which the curvature varies continuously. In addition, a method to generate the associated timing law is proposed such that the robot travels along the curve in near minimum time under the maximum velocity and torque constraints. Since the method aims for an online application, heuristic techniques are applied to minimize the computational cost for finding the optimal solution. The time for computing a complete time-optimal trajectory for a moving robot to transit to a next way-point is only several milliseconds when tested in a personal computer. This result implies that a highly efficient motion control system for a two-wheeled mobile robot can be implemented with a low-cost hardware available today. [ABSTRACT FROM AUTHOR]

Generally, vision-based controls use various camera sensors and require camera calibration, while the control performance would degrade due to inaccuracy calibration. Therefore, in this paper, the proposed controller only makes use of the image information from an un-calibrated perspective camera mounted at the follower robot without relative position measurement or any communication among the robots. First, the nominal visual formation kinematic model is developed using the camera models. Then it is redescribed as a quadratic programming (QP) with the specified constraints. A neurodynamic optimization based on primal-dual neural network is utilized to ensure the QP being converged to the exact optimal values. Through two-time-scale neuro-dynamical optimization, the gain scheduling of the ancillary state feedback can be realized so that the state variables are constrained within an invariant designed tube. The experiment results provide the verification for the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

A vision-based target-following guider for mobile robot is presented in this paper. It consists of three parts: the visual tracking part; the target redetection part; and the visual servo part. In the visual tracking part, the target contour band is proposed for irregular sampling, which can improve the performance of the correlation filter-based methods. In the target redetection part, potential targets are searched by an offline trained detector. Then an online training module is used to determine the real target and achieve accurate positioning. The interaction matrix of point features is used in the visual servo part for motion control to maintain the relative pose between the target and the robot. The visual tracking part and the target redetection part are tested respectively on many videos, which proved to work well. To show the effectiveness of our method, some state-of-the-art methods are also tested. The target-following guider is evaluated on mobile robots. In our experiments, the robot can robustly follow the human target over a long distance, which strongly proved the validity of our target-following guider. [ABSTRACT FROM AUTHOR]

This paper presents a reduced-order extended state observer (ESO) based sliding mode control scheme for friction compensation of a three-wheeled omnidirectional mobile robot. Compared with previous works, the proposed control approach is attractive from an implementation point of view. It does not require any explicit friction model, with quite low computation cost. First, a dynamic model with unknown friction forces is given. Then, the controller is designed, consisting of two parts. One part of the control effort is to compensate the friction effects, which are estimated by a reduced-order ESO without using any explicit friction model. The inverse of inertia matrix is also avoided in the proposed reduced-order ESO. The other part of the control effort is designed based on a second-order sliding mode technique known as super-twisting algorithm, in presence of parameter uncertainties. In addition, stability analysis of the designed control system is presented. Extensive experiments are conducted to verify the effectiveness and robustness of the proposed control design in compensating different friction effects. [ABSTRACT FROM AUTHOR]

In this paper, we study the adaptive fuzzy formation control of multiple autonomous agents with prioritized missions. For a platoon of autonomous agents in an unknown environment containing multiple obstacles, formation control is investigated, where each agent is modeled by a second-order nonlinear system under unknown external disturbance in the Brunovsky form. We introduce the systematic procedure of null-space-based projection to convert the prioritized multimission control problem into a behavioral control problem. Then, we further develop a class of nonlinear-fast-terminal-sliding-mode-based adaptive control strategies that combine the fuzzy logic systems by jointly considering both kinematic and dynamic levels of the agents. The proposed controllers can guarantee each individual agent to achieve the predesigned desired pattern and drive the entire systems to achieve the prescribed missions. A simulation example with five agents demonstrates the effectiveness of the algorithm. Finally, the strategies are experimentally validated using a platoon of Pioneer 3AT and 3DX mobile robots. [ABSTRACT FROM AUTHOR]

Multiple mobile robots have gradually played a key role in many industrial systems, such as factory freight logistics system, patrol security in the factory environment, and multirobot collaborative service and work. As a key issue in industrial environment perception, the accurate robot localization can enhance their autonomous ability and is an important branch of robotic studies in artificial intelligence. In this paper, we propose a new method for cooperative autonomous localization among air-ground robots in a wide-ranging outdoor industrial environment. The aerial robot first maps an area of interest and achieves self-localization. Then the aerial robot transfers a simplified orthogonal perspective 2.5-D map to the ground robots for collaboration. Within the collaboration, the ground robot achieves pose estimation with respect to the unmanned aerial vehicle pose by instantaneously registering a single panorama with respect to the 2.5-D map. The 2.5-D map is used as the spatial association among air-ground robots. The ground robots estimate the orientation using automatically detected geometric information and generates the translation by aligning the 2.5-D map with a semantic segmentation of the panorama. Our method effectively overcomes the dramatic differences between the air-level view and the ground-level view. A set of experiments is performed in the outdoor industrial environment to demonstrate the applicability of our localization method. The proposed robotic collaborative localization outperforms most consumer sensor in accuracy and also has an outstanding running time. [ABSTRACT FROM AUTHOR]

In most visual servoing methods for wheeled mobile robots, the camera is supposed at the center of the robot, while it will bring much convenience for practical use when putting the camera a little away from the robot center. In this paper, to handle the challenge that is incurred by the uncalibrated translational camera-to-robot parameters, a novel visual servoing scheme is designed for wheeled mobile robots to track a given trajectory, which is defined by a sequence of images recorded previously. The trajectory tracking errors are defined with respect to both current and desired poses of the robot, and the open-loop error system is obtained after analyzing kinematic models of the visual robot system. Subsequently, to compensate both unknown extrinsic parameters and the visual depth, an adaptive visual servo tracking controller is elaborately designed, with disposing the nonholonomic motion constraint of the mobile robot. The tracking errors are strictly proven converging to zero asymptotically by employing Lyapunov techniques and Barbalat's lemma. Both simulation and comparative experiments are conducted to prove that the proposed strategy can drive the robot track the desired trajectory efficiently, even translational camera-to-robot parameters are unknown. [ABSTRACT FROM AUTHOR]

This paper presents an efficient double-layer ant colony optimization algorithm, called DL-ACO, for autonomous robot navigation. This DL-ACO consists of two ant colony algorithms that run independently and successively. First, a parallel elite ant colony optimization method is proposed to generate an initial collision-free path in a complex map, and then, we apply a path improvement algorithm called turning point optimization algorithm, in which the initial path is optimized in terms of length, smoothness, and safety. Besides, a piecewise B-spline path smoother is presented for easier tracking control of the mobile robot. Our method is tested by simulations and compared with other path planning algorithms. The results show that our method can generate better collision-free path efficiently and consistently, which demonstrates the effectiveness of the proposed algorithm. Furthermore, its performance is validated by experiments in indoor and outdoor environments. [ABSTRACT FROM AUTHOR]

In this paper, we propose a computationally efficient online local motion planning algorithm for mobile robots in unknown cluttered dynamic environments. The algorithm plans a trajectory incrementally up to the finite horizon in state-time space. Incremental planning method is capable of fast computation but has poor obstacle avoidance performance. To compensate for the drawbacks of incremental planning, a partial trajectory modification scheme is used that sets an interim goal and then plans a trajectory to pass through the interim goal. The interim goal is a temporary desired goal to prevent the robot from falling into the inevitable collision state. By using incremental planning and partial trajectory modification, it is possible to plan collision-free trajectory with small computation even in cluttered dynamic environment. To generate smooth trajectories around given waypoints and goal, we systematize bidirectional trajectory planning with three kinds of trajectories: 1) a forward trajectory from the current robot state; 2) a backward trajectory from the state of current target waypoint; 3) and a connecting trajectory between the forward and backward trajectories. A smooth trajectory is generated around the way point and goal by setting the state of the current target waypoint (or goal), while taking into account the positional relationship among the planned forward trajectory, the target waypoint and the next waypoint. Performances of the proposed algorithm are validated through extensive simulations and experiment with two types of mobile robots: 1) a holonomic mobile robot and 2) a differential drive mobile robot. [ABSTRACT FROM AUTHOR]

In this paper, an incremental centralized formation system is developed for controlling the multirobot formation with joining robots, and a nonlinear model predictive control (NMPC) method is implemented as the controller. The incremental updating method is used to update the system's state in real time, when there is a new robot joining during the formation process. Then, an NMPC approach is developed to reformulate the formation system into a convex nonlinear minimization problem, which can be further transformed into a quadratic programming (QP) with constraints. Then, a general projection neural network (GPNN) is implemented for solving this QP problem online to get the optimal inputs. In the end, two examples of incremental multirobot formation are demonstrated to verify the effectiveness of this method. [ABSTRACT FROM AUTHOR]

This paper revisits the circular formation control problem proposed in [G. S. Seyboth, J. Wu, J. Qin, C. Yu, and F. Allgower, “Collective circular motion of unicycle type vehicles with nonidentical constant velocities,” IEEE Transactions on Control of Network Systems, vol. 1, no. 2, pp. 167–176, Jun. 2014.] for multiple nonholonomic mobile robots with nonidentical constant forward speeds. Specifically, we study two types of circular formations: First, a circular motion with synchronized/balanced phase configuration; and second, a concentric circular formation with both circular orbits control and phase synchronization/balancing. Existing works on the above problems utilize a fixed-gain control input, which increases the workload of the engineers for selecting favorable algorithm parameters. To reduce this workload, we study the above problems from a new perspective by utilizing optimization methods, based on which two variable-gain control algorithms are proposed such that much greater flexibility on the selection of algorithm parameters is acquired. The global convergence properties of the proposed algorithms are analyzed under some mild assumptions. Both simulations and field experiments are presented to validate the effectiveness of the proposed formation algorithms. [ABSTRACT FROM AUTHOR]

This paper deals with the bearing angle error (BAE) estimation problem associated with a one-axis gimballed ultrasonic seeker, which can be used as a low-cost sensor for mobile robot applications. Under the assumptions that the target has an ultrasonic transmitter and the gimbal control loops work properly, the moving target tracking reduces to an estimation problem of a piecewise constant BAE using real sinusoids measured by a receiver array mounted on the gimbal. To effectively cope with the real-time implementation issue, our estimation problem is formulated using linear robust state estimation and is resolved using the constrained robust weighted least squares method. Through experimental results obtained from a hardware-in-the-loop simulation (HILS) approach, it is demonstrated that the proposed scheme provides more reliable and consistent estimation performance than existing approaches. [ABSTRACT FROM PUBLISHER]

This paper proposes a control strategy for the vision-based control problem of a nonholonomic constrained differential-drive mobile robots with bounded disturbance by using robust tube-based model predictive control (MPC) method. The proposed control strategy mainly consists of an ancillary state feedback controller and an MPC control for a nominal robotic system. First, the state-error kinematics of the nominal system is converted into a chained form system, and then its MPC optimization is computed to deal with a quadratic programming optimization problem by integrating a linear variable inequality-based primal-dual neural network (LVI-PDNN). Next, the gain scheduling of the ancillary state feedback is obtained via solving robust pole assignment using LVI-PDNN. An optimal state trajectory is thus generated for the nominal robotic system by the MPC without various uncertainties, the ancillary state feedback control forces the state variables to be constrained within an invariant designed tube. Finally, extensive experimental results on stabilization control of the nonholonomic mobile robot are provided to verify the effectiveness of the proposed robust tube-based MPC. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of cluster formation control for a networked multi-agent system (MAS) in the simultaneous presence of aperiodic sampling and communication delays. First, to fulfill multiple formation tasks, a group of agents are decomposed into $M$ distinct and nonoverlapping clusters. The agents in each cluster are then driven to achieve a desired formation, whereas the MAS as a whole accomplishes $ M $ cluster formations. Second, by a proper modeling of aperiodic sampling and communication delays, an aperiodic sampled-data cluster formation protocol (CFP) is delicately constructed such that the information exchanges among neighboring agents only occur intermittently at discrete instants of time. Third, a detailed theoretical analysis of cluster formability is carried out and a sufficient and necessary condition is provided such that the system is $M$-cluster formable. Furthermore, a discontinuous Lyapunov functional approach is developed to derive a design criterion on the existence of an admissible sampled-data CFP. Finally, numerical simulations on a team of nonholonomic mobile robots are given to illustrate the effectiveness of the obtained theoretical result. [ABSTRACT FROM AUTHOR]

Most existing control methods for wheeled mobile robot (WMR) are based on the primary assumption that the WMR's center of mass (CM) is known and fixed. This paper presents an adaptive tracking control scheme for an asymmetrically actuated WMR with slipping/skidding dynamics and uncertain/unknown mass center. First, we establish the WMR dynamic model with consideration of the fact that its CM is normally unknown or even shifting due to dynamic loading and/or load shifting. The resultant model also takes into account the impact of slipping/skidding uncertainties. Second, a structurally simple and computationally inexpensive controller is developed to deal with time-varying unknown control gain and parametric/nonparametric uncertainties of WMR, where the asymmetric and nonsmooth input saturation with no a prior knowledge of bounds of input saturation, together with output constraints and actuation/propulsion failures, is addressed. [ABSTRACT FROM PUBLISHER]

Heading information is critically important for autonomous mobile robots as it is necessary for scanning or sweeping predetermined areas for specific tasks. Fusing sensor data including angular rates, acceleration, and geomagnetic fields provide heading and attitude. However, the geomagnetic field is often interfered with by ferromagnetic objects or other magnetic sources, resulting in incorrect heading information. This paper describes an algorithm that detects and rejects magnetic disturbances contained in a geomagnetic field. This algorithm combined with an extended Kalman filter is implemented in a relatively low-cost, small-scale microprocessor and sensor module. The algorithm is detailed for parameters that detect magnetic disturbances. The algorithm is also evaluated outdoors by driving a mobile robot on a lawn with apparent ferromagnetic objects and on the flat roof of a ferroconcrete building that includes iron bars and electrical wires in or under the roof. The experimental results on a flat roof indicate that the algorithm improves the accuracy of the heading significantly by reducing the peak-to-peak error by 32.9% (or the rms error by 69.9%). [ABSTRACT FROM PUBLISHER]