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This paper presents an event-triggered model predictive control (MPC) strategy with learning terminal cost for robotic manipulators containing model uncertainty and input constraints. In the proposed MPC structure, an adaptive predictive model for the robotic system is established by radial basis function neural networks (RBFNNs) firstly. Then, a terminal cost adjusted by the global learning mechanism is constructed. Both global steady-state optimization and transient fast convergence are achieved by adding the learning terminal cost to the MPC scheme. After that, a triggering condition of the MPC solving is developed based on the predictive model’s weights and the predictive tracking error. Besides, the condition to avoid Zeno behavior is obtained. The recursive feasibility of the proposed MPC strategy is verified, and the ultimately uniformly boundedness (UUB) of all variables is proved according to the Lyapunov theorem. Finally, experiments based on an xMate7 Pro robot are conducted to demonstrate the effectiveness of the presented method. Note to Practitioners—The tracking control of robotic manipulators is a common and important problem in industrial applications, such as grasping, loading, unloading, et al. There exist some limitations in existing control methods. For example, general control strategies such as adaptive control, sliding mode control neglect the balance between control costs and expected performance; existing optimal control approaches rarely consider global steady-state optimization and transient fast convergence under the influence of model uncertainty. This paper is motivated by these limitations of robotic control design and inspired by model predictive control and optimal control theory. It develops a novel strategy for tracking control of robotic manipulators, which includes three following items: (1) adopt an approximation model constructed by neural networks as the predictive model for estimating the robotic system; (2) introduce a critic network into the terminal cost of the objective function of MPC for learning global optimal solution; (3) establish an event-triggered mechanism for solving of the optimization problem. The proposed method is verified by experimental results. In our future work, the proposed control strategy will be applied to more industrial processes. [ABSTRACT FROM AUTHOR]

Owing to its potential superiorities in terms of flexibility, compliance, and robustness, the musculoskeletal robotic system has become a promising direction for next-generation robots. However, motion learning and generalization of musculoskeletal systems are still challenging problems. In this article, a muscle-synergies-based neuromuscular control is proposed. First, a new computational model of time-varying muscle synergies is constructed, which utilizes both phasic and tonic muscle synergies to characterize the basic features of muscle excitations more sufficiently. Second, a novel neuromuscular control method is proposed for realizing the motion learning and generalization of musculoskeletal systems. Therein, a radial basis function (RBF) neural network is designed to modulate muscle synergies according to different movement targets. Muscle excitations are computed with the combination of modulated muscle synergies. Covariance matrix adaptation evolutionary strategy is applied to realize the synchronous optimization of muscle synergies and the RBF neural network. In the experiment, a sophisticated musculoskeletal system learns to perform center-out reaching tasks through trial-and-error learning on a few targets. With the muscle synergies and neural modulation learned from a few targets, the musculoskeletal system can also reach many unexperienced targets. The proposed method not only improves the speed and accuracy of motion learning but also enhances motion generalization. This article also promotes the development of the musculoskeletal robotic system and the fusion of neuroscience and robotics. [ABSTRACT FROM AUTHOR]

Development of a flexible low-cost robotic system to grasp various 3D workpieces is of practical important. Traditional approaches on 3D grasping usually need to compute the effective contact positions based on sufficient conditions, such as “force-closure” or “form-closure,” to prevent all the motions of the grasped objects. Compared with the previous work motivated by high-precision applications, caging provides a way to manipulate an object without needing to immobilize it. However, most caging conditions consider only 2D motions of the object in grasping. In some cases, other motions in 3D space, e.g., the pitch and roll rotations of the objects, would also possibly change a caging configuration to an uncaging configuration. This paper aims to discuss caging with frictionless contact, by taking into consideration of all the motions of the object. We first discuss the relationship between the gripper configuration and the state of the grasped object in grasping, where all the motions of the object are taken into account. We then establish the sufficient conditions to find a set of 3D caging configurations in terms of the width of the object's projection and the gap of the gripper, such that we can utilize the projection to find the feasible placements of the pins to determine the caging configuration. Furthermore, we discuss how to find the caging configuration that is capable of leading to a form-closure based on attractive region formed in the configuration space. [ABSTRACT FROM PUBLISHER]

In this paper we propose the graduated nonconvexity and concavity procedure (GNCCP) as a general optimization framework to approximately solve the combinatorial optimization problems defined on the set of partial permutation matrices. GNCCP comprises two sub-procedures, graduated nonconvexity which realizes a convex relaxation and graduated concavity which realizes a concave relaxation. It is proved that GNCCP realizes exactly a type of convex-concave relaxation procedure (CCRP), but with a much simpler formulation without needing convex or concave relaxation in an explicit way. Actually, GNCCP involves only the gradient of the objective function and is therefore very easy to use in practical applications. Two typical related NP-hard problems, partial graph matching and quadratic assignment problem (QAP), are employed to demonstrate its simplicity and state-of-the-art performance. [ABSTRACT FROM PUBLISHER]

In general, localization is a very important step in the manufacturing process, which can be considered as a prior process of assembly, machining, transportation, etc. Localization can be achieved with or without sensors. Compared with localization with sensors, localization without sensors can be more reliable, cheaper, and can have lower requirements on the environment. For example, localization without sensors can be achieved in a dark environment. However, it is more restrictive to the condition of the system. Localization of 2-D objects without sensors has been deeply investigated. Some theoretical results on 3-D-object localization without sensors have been given. On the other hand, some work has been carried out that tries to find methods to reduce uncertainties in the 3-D orientation of a polyhedron (i.e., the orientation of the polyhedron in a 3-D space). However, to the best of our knowledge, no practical and effective methods have been proposed, so far, to localize a polyhedron from any initial 3-D orientation to a unique 3-D orientation without sensors. This paper aims to find conditions and strategies for 3-D objects to be rotated from an unknown initial stable state to a unique stable state without sensors. The main contributions of this paper are given as follows: 1) It is discovered and proved that there are two classes of 3-D objects that can be rotated into a unique state from an arbitrary initial state without sensory feedback. 2) For these two classes of objects, the practical strategies are presented, and one example for each class is given to show the validity of the strategy. 3) Based on the above results, the robotic system and localization operations are illustrated, and some experimental results are given. [ABSTRACT FROM PUBLISHER]

Several adaptive visual tracking algorithms have been recently proposed to capture the varying appearance of target. However, adaptability may also result in the problem of gradual drift, especially when the target appearance changes drastically. This paper gives some theoretical principles for online learning of target model, and then presents a novel adaptive tracking algorithm which is able to effectively cope with drastic variations in target appearance and resist gradual drift. Once target is localized in each frame, the patches sampled from target observation are first classified into foreground and background using an effective classifier. Then the adaptive, pure and time-continuous target model is extracted online through two processes: absorption process and rejection process, through which only the reliable features with high separability are absorbed in the new target model, while the “dangerous” features which may cause interfusion of background patterns are rejected. To minimize the influence of background and keep the temporal continuity of target model, two collaborative models dominant model and continuous model are designed. The proposed learning and generation mechanisms of target model are finally embedded in an adaptive tracking system. Experimental results demonstrate the robust performance of the proposed algorithm under challenging conditions. [ABSTRACT FROM AUTHOR]

In this paper, a novel methodology called a reference model approach to stability analysis of neural networks is proposed. The core of the new approach is to study a neural network model with reference to other related models, so that different modeling approaches can be combinatively used and powerfully cross-fertilized. Focused on two representative neural network modeling approaches (the neuron state modeling approach and the local field modeling approach), we establish a rigorous theoretical basis on the feasibility and efficiency of the reference model approach. The new approach has been used to develop a series of new, generic stability theories for various neural network models. These results have been applied to several typical neural network systems including the Hopfield-type neural networks, the recurrent back-propagation neural networks, the BSB-type neural networks, the bound-constraints optimization neural networks, and the cellular neural networks. The results obtained unify, sharpen or generalize most of the existing stability assertions, and illustrate the feasibility and power of the new method. [ABSTRACT FROM AUTHOR]

This article proposes a novel decision-making framework that bridges a gap between model-based (MB) and model-free (MF) control processes through only adjusting the planning horizon. Specifically, the output policy is obtained by solving a model predictive control problem with a locally optimal state value as terminal constraints. When the planning horizon decreases to zero, the MB control will transform into the MF control smoothly. Meanwhile, inspired by the neural mechanism of emotion modulation on decision-making, we build a biologically plausible computational model of emotion processing. This model can generate an uncertainty-related emotional response on the basis of the state prediction error and reward prediction error, and then dynamically modulates the planning horizon in the tasks. The simulation results demonstrate that the proposed decision-making framework can produce better policies than traditional methods. Emotion modulation can shift the MB and MF control well to improve the learning efficiency and the speed of decision-making. [ABSTRACT FROM AUTHOR]

Feature correspondence lays the foundation for many computer vision and image processing tasks, which can be well formulated and solved by graph matching. Because of the high complexity, approximate methods are necessary for graph matching, and the continuous relaxation provides an efficient approximate scheme. But there are still many problems to be settled, such as the highly nonconvex objective function, the ignorance of the combinatorial nature of graph matching in the optimization process, and few attention to the outlier problem. Focusing on these problems, this paper introduces a continuation method directly targeting at the combinatorial optimization problem associated with graph matching. Specifically, first a regularization function incorporating the original objective function and the discrete constraints is proposed. Then a continuation method based on Gaussian smoothing is applied to it, in which the closed forms of relevant functions with respect to the outlier distribution are deduced. Experiments on both synthetic data and real world images validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The path following algorithm was proposed recently to approximately solve the matching problems on undirected graph models and exhibited a state-of-the-art performance on matching accuracy. In this paper, we extend the path following algorithm to the matching problems on directed graph models by proposing a concave relaxation for the problem. Based on the concave and convex relaxations, a series of objective functions are constructed, and the Frank-Wolfe algorithm is then utilized to minimize them. Several experiments on synthetic and real data witness the validity of the extended path following algorithm. [ABSTRACT FROM PUBLISHER]

Classification-based pedestrian detection systems (PDSs) are currently a hot research topic in the field of intelligent transportation. A PDS detects pedestrians in real time on moving vehicles. A practical PDS demands not only high detection accuracy but also high detection speed. However, most of the existing classification-based approaches mainly seek for high detection accuracy, while the detection speed is not purposely optimized for practical application. At the same time, the performance, particularly the speed, is primarily tuned based on experiments without theoretical foundations, leading to a long training procedure. This paper starts with measuring and optimizing detection speed, and then a practical classification-based pedestrian detection solution with high detection speed and training speed is described. First, an extended classification/detection speed metric, named feature-per-object (fpo), is proposed to measure the detection speed independently from execution. Then, an fpo minimization model with accuracy constraints is formulated based on a tree classifier ensemble, where the minimum fpo can guarantee the highest detection speed. Finally, the minimization problem is solved efficiently by using nonlinear fitting based on radial basis function neural networks. In addition, the optimal solution is directly used to instruct classifier training; thus, the training speed could be accelerated greatly. Therefore, a rapid and accurate classification-based detection technique is proposed for the PDS. Experimental results on urban traffic videos show that the proposed method has a high detection speed with an acceptable detection rate and a false-alarm rate for onboard detection; moreover, the training procedure is also very fast. [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]

Policy search in reinforcement learning (RL) is a practical approach to interact directly with environments in parameter spaces, that often deal with dilemmas of local optima and real-time sample collection. A promising algorithm, known as guided policy search (GPS), is capable of handling the challenge of training samples using trajectory-centric methods. It can also provide asymptotic local convergence guarantees. However, in its current form, the GPS algorithm cannot operate in sequential multitask learning scenarios. This is due to its batch-style training requirement, where all training samples are collectively provided at the start of the learning process. The algorithm’s adaptation is thus hindered for real-time applications, where training samples or tasks can arrive randomly. In this paper, the GPS approach is reformulated, by adapting a recently proposed, lifelong-learning method, and elastic weight consolidation. Specifically, Fisher information is incorporated to impart knowledge from previously learned tasks. The proposed algorithm, termed sequential multitask learning-GPS, is able to operate in sequential multitask learning settings and ensuring continuous policy learning, without catastrophic forgetting. Pendulum and robotic manipulation experiments demonstrate the new algorithms efficacy to learn control policies for handling sequentially arriving training samples, delivering comparable performance to the traditional, and batch-based GPS algorithm. In conclusion, the proposed algorithm is posited as a new benchmark for the real-time RL and robotics research community. [ABSTRACT FROM AUTHOR]

A precise robotic assembly for large-scale objects is an important application in manufacture. In this paper, we design a new assembly system for large-scale objects based on automatic guidance and alignment with two cameras and three laser distance sensors. The corresponding strategy consists of three stages. First, the object is guided from a remote position to a close one based on our proposed link-line model-based polygon detection and Computer Aided Design (CAD) - View method. Second, the object is aligned to the installation framework based on the proposed visual and laser alignment method. Finally, the object is automatically inserted into the installation framework. An accurate easily applying and close-loop hand-eye calibration method is also proposed. The proposed assembly system has high assembly precision and efficiency. In our test for a 710 \text mm \times 625 mm optical module, the maximum 6-DOF localization error of guidance and alignment is 0.53 mm in translation and 0.10° in orientation. The mechanical constraint-based controlling accuracy, i.e., the final assembly accuracy, is up to 0.2 mm in translation. [ABSTRACT FROM PUBLISHER]