Modular And Reconfigurable Robots Research Articles

Distributed Operation Health Monitoring for Modular and Reconfigurable Robot With Consideration of Actuation Limitation

A distributed operation health monitoring (OHM) method of modular and reconfigurable robot (MRR) is presented in this paper. The proposed method is shown to be able to monitor the health of each MRR joint module based on the deviation of the actuator output from what is commanded. Driven by the desire of avoiding the need of joint acceleration measurement, a novel health indicator that reflects the operation health of an MRR module is developed by filtering the commanded joint torque generated by the joint controller and comparing it with a filtered torque estimate derived from the dynamic model of MRR. The proposed approach can work effectively for MRR modules in any working mode, including stationary state. The proposed scheme has been evaluated experimentally, and the results demonstrate its efficacy.

Decentralized Trajectory Tracking Control for Modular and Reconfigurable Robots With Torque Sensor: Adaptive Terminal Sliding Control-Based Approach

The main technical challenge in decentralized control of modular and reconfigurable robots (MRRs) with torque sensor is related to the treatment of interconnection term and friction term. This paper proposed a modified adaptive sliding mode decentralized control strategy for trajectory tracking control of the MRRs. The radial basis function (RBF) neural network is used as an effective learning method to approximate the interconnection term and friction term, eliminating the effect of model uncertainty and reducing the controller gain. In addition, in order to provide faster convergence and higher precision control, the terminal sliding mode algorithm is introduced to the controller design. Based on the Lyapunov method, the stability of the MRRs is proved. Finally, experiments are performed to confirm the effectiveness of the method.

Torque sensorless decentralized neuro-optimal control for modular and reconfigurable robots with uncertain environments

A technical challenge of addressing the decentralized optimal control problem for modular and reconfigurable robots (MRRs) during environmental contacts is associated with optimal compensation of the uncertain contact force without using force/torque sensors. In this paper, a decentralized control approach is presented for torque sensorless MRRs in contact with uncertain environment via an adaptive dynamic programming (ADP)-based neuro-optimal compensation strategy. The dynamic model of the MRRs is formulated based on a novel joint torque estimation method, which is deployed for each joint model, and the joint dynamic information is utilized effectively to design the feedback controllers, thus making the decentralized optimal control problem of the environmental contacted MRR systems be formulated as an optimal compensation issue of model uncertainty. By using the ADP method, a local online policy iteration algorithm is employed to solve the Hamilton–Jacobi–Bellman (HJB) equation with a modified cost function, which is approximated by constructing a critic neural network, and then the approximate optimal control policy can be derived. The asymptotic stability of the closed-loop MRR system is proved by using the Lyapunov theory. At last, simulations and experiments are performed to verify the effectiveness of the proposed method.

Decentralized Integral Nested Sliding Mode Control for Time Varying Constrained Modular and Reconfigurable Robot

This paper presents a novel decentralized integral nested sliding mode control approach for a time varying constrained modular and reconfigurable robot (MRR) and addresses the problem of joint trajectory tracking control of the MRR with strongly coupled model uncertainty. First, the dynamic model of the time varying constrained MRR is described as a synthesis of interconnected subsystems. Second, a decentralized control scheme is proposed based on only local dynamic information of each module. An integral nested sliding surface is implemented to reduce the chattering effect of the controller. Model uncertainties, including the unmodeled subsystem dynamics, the friction modeling error, and the interconnected dynamic coupling, are compensated by a variable gain supertwisting algorithm (VGSTA) based controller. The stability of the close-loop system is proved using the Lyapunov theory. Finally, simulations are performed for a time varying constrained 2-DOF MRR to study the effectiveness of the proposed method.

Distributed fault detection for modular and reconfigurable robots with joint torque sensing: A prediction error based approach

A distributed fault detection scheme for modular and reconfigurable robots (MRRs) with joint torque sensing is proposed in this paper. With the proposed scheme, the joint torque command is filtered and compared with a filtered torque estimate derived from the nonlinear dynamic model of MRR with joint torque sensing. Common joint actuator faults are considered with fault detection being performed independently for each joint module. The proposed fault detection scheme for each module does not require motion states of any other module making it an ideal modular approach for fault detection of modular robots. Experimental results have confirmed the effectiveness of the proposed fault detection scheme.

Multiple Working Mode Control of Door-Opening With a Mobile Modular and Reconfigurable Robot

This paper addresses the problems of opening a door with a modular and reconfigurable robot (MRR) mounted on a wheeled mobile robot platform. The main concern of opening a door is how to prevent the occurrence of large internal forces that arise because of the positioning errors or imprecise modeling of the robot or its environment, specifically, the door parameters. Unlike previous methods that relied on compliance control, making the control design rather complicated, this paper presents a new concept that utilizes the multiple working modes of the MRR modules. The control design is significantly simplified by switching selected joints of the MRR to work in passive mode during door-opening operation. As a result, the occurrence of large internal forces is prevented. Different control schemes are used for control of the joint modules in different working modes. For the passive joint modules, a feedforward torque control approach is used to compensate the joint friction to ensure passive motion. For the active joint modules, a distributed control method based on torque sensing is used to facilitate the control of joint modules working under this mode. To enable autonomous door-opening, an online door parameter estimation algorithm is proposed on the basis of the least squares method, and a path planning algorithm is developed on the basis of Hermite cubic spline functions, with consideration of motion constraints of the mobile MRR. Simulation and experimental results are presented to show the effectiveness of the proposed approach.

Power Efficiency Estimation-Based Health Monitoring and Fault Detection of Modular and Reconfigurable Robot

The power efficiency degradation of machines often provides intrinsic indication of problems associated with their operation health conditions. Inspired by this observation, as presented in this paper, a simple yet effective power efficiency estimation-based health monitoring and fault detection technique is developed for a modular and reconfigurable robot (MRR) with a joint torque sensor. Power efficiency coefficients of each joint module are obtained using sensor measurements and used directly for health monitoring and fault detection. The proposed method has been experimentally tested on an MRR that has been developed in our laboratory for working in human environments, which critically demands for efficient health monitoring and fault detection capabilities. Experimental results have demonstrated the effectiveness of the proposed method.

Design, Analysis, and Control of a Spring-Assisted Modular and Reconfigurable Robot

Conventional robot manipulators produce poor payload to weight ratio and limited manipulation ability, as a significant portion of available actuation force is used to balance their own weight. The same cause also limits the robot's operation capability in terms of acceleration and manipulation force. Such problems become more severe for modular and reconfigurable robots (MRRs) when they are expanded by adding predesigned modules. Static balancing with counterweights and external springs can greatly improve a robot's payload and manipulation capabilities, but require sophisticated mechanisms and restrict the working envelope of the robot. In this paper, an innovative spring-assisted MRR design and control framework is presented, which is developed based on a synergetic integration of robot control with a brake and an embedded spring at each modular joint. The spring is inserted between the brake and the motor shaft through a decoupling bearing. By activating the brake, static balancing can be established at any desirable position of each module and any configuration of the robot, allowing reinforced delicate operation in a neighborhood of the balanced configuration such as door opening, as well as spring-assisted lift of heavy payload. A distributed control method has been proposed to facilitate control of the spring-assisted MRRs, which does not rely on a priori dynamic models, and can suppress uncertainties caused by reconfigurations, eliminating the need to readjust control parameters of the lower modules when new modules are added or removed. Prototype modules have been developed, and the experimental results have confirmed the effectiveness of the proposed design and control.

Design of Novel Interval Type-2 Fuzzy Controllers for Modular and Reconfigurable Robots: Theory and Experiments

Recently, there has been a growing interest in using modular and reconfigurable robots (MRRs) in flexible automation to reduce labor and increase throughput. Moreover, the significant facilitation of repair and maintenance of MRRs has attracted manufacturers to respond to the increasing desire for new products and methods of production in today's competitive market. As a result, design and control of such systems have become major topics for investigation in recent years. This paper presents a novel design methodology of interval type-2 Takagi-Sugeno-Kang fuzzy logic controllers (IT2 TSK FLCs) for MRR manipulators with uncertain dynamic parameters. We develop a mathematical framework for the design of IT2 TSK FLCs for tracking purposes that can be effectively used in real-time applications. To verify the effectiveness of the proposed controller, experiments are performed on an MRR with two degrees of freedom, which exhibits dynamic coupling behavior. Results show that the developed controller can outperform some well-known linear and nonlinear controllers for different configurations. Therefore, the proposed structure can be adopted for the position control of MRRs with unknown dynamic parameters in trajectory-tracking applications.

An adaptive niching genetic algorithm approach for generating multiple solutions of serial manipulator inverse kinematics with applications to modular robots

SUMMARYInverse kinematics (IK) is a nonlinear problem that may have multiple solutions. A modified genetic algorithm (GA) for solving the IK of a serial robotic manipulator is presented. The algorithm is capable of finding multiple solutions of the IK through niching methods. Despite the fact that the number and position of solutions in the search space depends on the position and orientation of the end-effector as well as the kinematic configuration (KC) of the robot, the number of GA parameters that must be set by a user are limited to a minimum through the use of an adaptive niching method. The only requirement of the algorithm is the forward kinematics (FK) equations which can be easily obtained from the Denavit–Hartenberg link parameters and joint variables of the robot. For identifying and processing the outputs of the proposed GA, a modified filtering and clustering phase is also added to the algorithm. For the postprocessing stage, a numerical IK solver is used to achieve convergence to the desired accuracy. The algorithm is validated on three KCs of a modular and reconfigurable robot (MRR).

Decentralized robust control of robot manipulators with harmonic drive transmission and application to modular and reconfigurable serial arms

SUMMARYIn this paper, we propose a decentralized robust control algorithm for modular and reconfigurable robots (MRRs) based on Lyapunov's stability analysis and backstepping techniques. In using decentralized control schemes with robot manipulators, each joint is considered as an independent subsystem, and the dynamical effects from the other links and joints are treated as disturbance. However, there exist many uncertainties due to unmodeled dynamics, varying payloads, harmonic drive (HD) compliance, HD complex gear meshing mechanisms, etc. Also, while the reconfigurability of MRRs is advantageous, modifying the configuration will result in changes to the robot dynamics parameters, thereby making it challenging to tune the control system. All of the above mentioned disturbances in addition to reconfigurability present a challenge in controlling MRRs. The proposed controller is well suited for MRR applications because of its simple structure that does not require the exact knowledge of the dynamic parameters of the configurations. Desired tracking performance can be achieved via tuning a limited set of parameters of the robust controller. If the numbers of degrees of freedom are held constant, these parameters are shown to be relatively independent of the configuration, and can be held constant between changes in configuration. This strategy is novel compared to existing MRR control methods. In order to validate the controller performance, experimental setup and results are also presented.