Modular Robot Manipulators Research Articles

An approach for automatic generation of the URDF file of modular robots from modules designed using SolidWorks

Modular robot manipulators promise to enhance current automation practice by providing higher flexibility and quick recovery in case of failures with respect to fixed-structure robots. The configuration of the modular robot control for each new assembly, to account for the new system kinematics and dynamics, can be time consuming and requires robot modelling expertise. We propose an approach for automatically generating the Unified Robot Description Format (URDF) file of modular robot manipulators, starting from the kinematic and dynamic descriptions expressed following the URDF of the single modules they can be composed of. The approach has been implemented and numerically verified by exploiting off-the-shelf software tools from Robot Operating System (ROS) libraries.

Compensator-critic structure-based neuro-optimal control of modular robot manipulators with uncertain environmental contacts using non-zero-sum games

In this paper, a novel compensator-critic structure-based neuro-optimal control approach is presented for modular robot manipulators (MRMs) in contact with uncertain environments. Based on joint torque feedback (JTF) technique, the dynamic model of the manipulator systems is described as an integration of joint subsystems associated with the effect of interconnected dynamic coupling (IDC). A local dynamic information-based robust compensator is designed to engage the model uncertainty compensation, and then, the optimal tracking control problem of an MRM system is transformed into an n-player non-zero-sum game issue of multiple joint subsystems. By taking advantage of the adaptive dynamic programming (ADP) algorithm, a cost function approximator, which is constructed by using only critic neural networks (NNs), is developed for solving the Hamilton–Jacobi (HJ) equation, thus facilitating the feasible derivation of the neuro-optimal control policy. The tracking error of the closed-loop robotic system is proved to be uniformly ultimately bounded (UUB) on the basis of the Lyapunov theory. Finally, experiment results illustrated the advantage and effectiveness of the developed control method.

Compensator-critic structure-based event-triggered decentralized tracking control of modular robot manipulators: theory and experimental verification

This paper presents a novel compensator-critic structure-based event-triggered decentralized tracking control of modular robot manipulators (MRMs). On the basis of subsystem dynamics under joint torque feedback (JTF) technique, the proposed tracking error fusion function, which includes position error and velocity error, is utilized to construct performance index function. By analyzing the dynamic uncertainties, a local dynamic information-based robust controller is designed to engage the model uncertainty compensation. Based on adaptive dynamic programming (ADP) algorithm and the event-triggered mechanism, the decentralized tracking control is obtained by solving the event-triggered Hamilton–Jacobi–Bellman equation (HJBE) with the critic neural network (NN). The tracking error of the closed-loop manipulators system is proved to be ultimately uniformly bounded (UUB) using the Lyapunov stability theorem. Finally, experimental results illustrate the effectiveness of the developed control method.

Zero-sum game-based neuro-optimal control of modular robot manipulators with uncertain disturbance using critic only policy iteration

In this paper, a zero-sum differential game strategy-based neuro-optimal control method is presented via critic only policy iteration-adaptive dynamic programming (COPI-ADP) approach to address optimal trajectory tracking control problem of modular robot manipulators (MRMs) with uncertain disturbance. The dynamic model of modular robot manipulator systems is formulated as an integration of joint subsystems and unknown robotic model uncertainties are identified by the developed linear extension state observer. Then, the optimal control issue of the modular robot manipulator systems with uncertain disturbance is transformed into a two-player zero-sum differential game one. Based on adaptive dynamic programming and policy iteration algorithms, the Hamilton-Jacobi-Issacs (HJI) equation is approximately solved using only critic neural network and thus facilitating the feasible derivation of the approximated optimal control policy. The trajectory of tracking errors of modular robot manipulator system is guaranteed to be uniform ultimate bounded by using the Lyapunov theory. Finally, experiments are provided to demonstrate the advantage and effectiveness of the developed control method.

A UGV-based modular robotic manipulator for soil sampling and terramechanics investigations

Unmanned vehicles are a natural choice for accessing challenging or hazardous terrains, for instance oil sands tailings ponds, and performing tasks such as soil sampling and terramechanics investigations. In previously published work, an unmanned ground vehicle (UGV) named RTC-I was designed and built for this task by part of our group. The present article covers the design choices and technical details of a custom-built robotic manipulator, a soil sampler, and an instrumented wheel deployed onboard a second-generation UGV named RTC-II. The robotic manipulator is designed to provide the reach, payload capacity, ruggedness, and self-locking operation required for field operations. The soil sampler employs a curved scoop to minimize deformation of the collected sample. The instrumented wheel permits independent control of the normal load and the slip ratio during terramechanics investigations. Each of the three designs is deployed and successfully tested in field experiments. Measurements collected by the soil sampler and instrumented wheel will be used in future work dealing with sampler force modeling and real-time terrain parameter estimation, respectively.

Decentralized robust zero-sum neuro-optimal control for modular robot manipulators in contact with uncertain environments: theory and experimental verification

This paper presents a decentralized robust zero-sum optimal control approach for modular robot manipulators (MRMs) in contact with uncertain environments based on the adaptive dynamic programming (ADP) algorithm. The dynamic model of MRMs is formulated via joint torque feedback technique that is deployed for each joint module to design the model compensation controller. An uncertainty decomposition-based robust control is developed to compensate the model uncertainties, and then, the robust optimal control problem of the MRM system is transformed into a two-player zero-sum optimal control one. According to the ADP algorithm, the Hamilton–Jacobi–Isaacs equation can be solved by establishing action and critic neural networks, thus making the derivation of the approximate optimal control policy feasible. Based on the Lyapunov theory, the closed-loop robotic system is proved to be asymptotic stable under the developed decentralized control method. Finally, experiments are conducted to verify the effectiveness and advantages of the proposed method.

Decentralized robust optimal control for modular robot manipulators via critic-identifier structure-based adaptive dynamic programming

This paper presents a decentralized robust optimal control method for modular robot manipulators (MRMs) via a critic-identifier structure-based adaptive dynamic programming (ADP) scheme. The robust control problem of MRMs is transformed into an optimal compensation control issue, which consists of model-based compensation control, identifier-based learning control and ADP-based optimal control. The dynamic model of MRMs is deployed for each joint module where the local dynamic information is utilized to design the model compensation controller. A neural network (NN) identifier is established to approximate the interconnected dynamic coupling. Based on ADP and local online policy iteration algorithm, the Hamiltonian–Jacobi–Bellman equation is solved by constructing a critic NN, and then the approximate optimal control policy derivation is possible. The closed-loop robotic system is asymptotic stable by the implementation of a set of developed decentralized control policies. Simulations are presented to demonstrate the effectiveness of the proposed method.

On-the-Fly Control Design of Modular Robot Manipulators

We consider the problem of automatically designing model-based controllers of modular robot manipulators in a systematic way. Modular robots are especially useful in flexible manufacturing, where one wishes to quickly assemble robots from a set of modules for temporary tasks. The evaluation of all possible dynamical models is typically impractical, undermining the implementation of model-based control laws. Contrary to most other work that approached this challenge by designing decentralized controllers, we generate on-the-fly model-based centralized controllers after a new robot has been assembled. In this brief, we extend the applicability of our previous work on this subject by considering both link and joint modules, assembly dependent friction effects, and we finally present experiments that validate the overall approach.

Networked Embedded Control of Modular Robot Manipulators Using VDC

In this paper, a networked embedded control of modular robot manipulators without using joint torque sensing is presented. The proposed solution uses an effective control and communication mechanism based on the virtual decomposition control (VDC) approach with embedded FPGA (Field Programmable Gate Array) implementation. A hierarchical master-slaves control structure is used, supported by a high speed communication data bus. The master computer handles only kinematics computation and the dynamics-based computations are all performed by individual embedded FPGA module controllers. The virtual stability of each module is ensured, resulting in the L2/L∞ stability of the entire robot. Experimental results achieved on a three-module robot manipulator using harmonic drives are presented.

Precision Control of Modular Robot Manipulators: The VDC Approach With Embedded FPGA

A systematic solution to precision control of modular robot manipulators without using joint torque sensing is presented in this paper for the first time. Using the virtual decomposition control (VDC) approach with embedded field programmable gate array (FPGA) logic devices, the proposed solution solves a long-standing problem of lacking control precision fundamentally associated with the modular robot manipulators. As a result, this solution allows modular robot manipulators to possess not only their traditional advantages (such as reconfigurability, flexibility, versatility, and ease of use) but precision control capability as well. A hierarchical master-slave control structure is used, which is supported by a high-speed communication system modified from SpaceWire (IEEE 1355), transferring a limited amount of data between the master and slave nodes at a rate of 1000 Hz. In each module, the FPGA logic implementation uses multiple sampling periods of 163.8 μs, 1.28 μs, and 20 ns. A gravity counterbalance spring provides a design option for the purpose of energy saving. Experimental results demonstrate unprecedented control precision, which is attributed to the use of both the VDC approach and embedded FPGA implementation. The ratio of the maximum position tracking error to the maximum velocity reaches 0.00012 s-more than an order of magnitude better than available technologies in control of robots with harmonic drives. The solution presented in this paper is also applicable to integrated robot manipulators using embedded FPGA controllers.

Virtual Decomposition Control for Modular Robot Manipulators

AbstractVirtual decomposition control (VDC) is one of the most efficient approaches toward precision control of complex robots. The VDC approach uses subsystem (such as links and joints) dynamics with parametric uncertainty to construct model-based feedforward compensation terms and guarantees the stability of the entire complex robot with mathematical certainty, leading to precise motion/force tracking control with the control bandwidth being independent of the feedback control gains. The VDC approach can be naturally applied to modular robot manipulators to overcome their long-standing problem of lacking control precision in coordinated motion. In this paper, with respect to a typical module comprised of two links and one joint, the control equations of VDC are given and the virtual stability, a necessary and sufficient condition to ensure the stability of the entire robot, is proven. The experiment result yields unprecedented motion tracking precision for a one-module robot using a harmonic drive with significant friction. The ratio of the maximum position tracking error to the maximum velocity reaches 0.00024 (s).