Neural Dynamics for Cooperative Motion Control of Omnidirectional Mobile Manipulators in the Presence of Noises: A Distributed Approach

This paper presents a distributed scheme with limited communications, aiming to achieve cooperative motion control for multiple omnidirectional mobile manipulators(MOMMs).The proposed scheme extends the existing single-agent motion control to cater to scenarios involving the cooperative operation of MOMMs. Specifically, squeeze-free cooperative load transportation is achieved for the end-effectors of MOMMs by incorporating cooperative repetitive motion planning(CRMP), while guiding each individual to desired poses. Then, the distributed scheme is formulated as a time-varying quadratic programming(QP) and solved online utilizing a noise-tolerant zeroing neural network(NTZNN). Theoretical analysis shows that the NTZNN model converges globally to the optimal solution of QP in the presence of noise. Finally, the effectiveness of the control design is demonstrated by numerical simulations and physical platform experiments.

Kinematic analysis of a humanoid robot CHP-1 and selection of motors in consideration of cooperative motion

A lower-part humanoid robot CHP-1 with 12 degree-of-freedom of motion has been developed for cooperative motion,such as pushing or lifting an object.The capability of the robot is mainly dependent on the performance of the motors,thus the motors need to be properly selected.For the purpose,the kinematics of the robot was analyzed,and a number of simulations for two kinds of cooperative motions were carried out.The torques required at each motor of the robot under external forces were obtained.Here,the external forces were also estimated through simulation and literature survey.On the basis of the torques found,the selection of motors was finally suggested,and the motors are to be installed to the humanoid robot.

A Comprehensive Review of Path Planning Algorithms for Autonomous Underwater Vehicles

The underwater path planning problem deals with finding an optimal or sub-optimal route between an origin point and a termination point in marine environments.The underwater environment is still considered as a great challenge for the path planning of autonomous underwater vehicles(AUVs)because of its hostile and dynamic nature.The major constraints for path planning are limited data transmission capability,power and sensing technology available for underwater operations.The sea environment is subjected to a large set of challenging factors classified as atmospheric,coastal and gravitational.Based on whether the impact of these factors can be approximated or not,the underwater environment can be characterized as predictable and unpredictable respectively.The classical path planning algorithms based on artificial intelligence assume that environmental conditions are known apriori to the path planner.But the current path planning algorithms involve continual interaction with the environment considering the environment as dynamic and its effect cannot be predicted.Path planning is necessary for many applications involving AUVs.These are based upon planning safety routes with minimum energy cost and computation overheads.This review is intended to summarize various path planning strategies for AUVs on the basis of characterization of underwater environments as predictable and unpredictable.The algorithms employed in path planning of single AUV and multiple AUVs are reviewed in the light of predictable and unpredictable environments.

Understanding enzyme catalysis by means of supramolecular artificial enzymes

Enzymes are biomacromolecules responsible for the abundant chemical biotransformations that sustain life. Recently, biochemists have discovered that multiple conformations and numerous parallel paths are involved during the processes catalyzed by enzymes. It is plausible that the entire macromolecular scaffold is involved in catalysis via cooperative motions that result in incredible catalytic efficiency. Moreover, some enzymes can very strongly bind the transition state with an association constant of up to 1024 M-1, suggesting that covalent bond formation is a possible process during the conversion of the transition state in enzyme catalysis, in addition to the concatenation of noncovalent interactions. Supramolecular chemistry provides fundamental knowledge about the relationships between the dynamic structures and functions of organized molecules. By tak-ing advantage of supramolecular concepts, numerous supramolecular enzyme mimics with complex and hierarchical structures have been designed and investigated. Through the study of supramolecular enzyme models, a great deal of information to aid our understanding of the mechanism of catalysis by natural enzymes has been acquired. With the development of supramolec-ular artificial enzymes, it is possible to replicate the features of natural enzymes with regards to their constitutional complexity and cooperative motions, and eventually decipher the conformation-based catalytic mystery of natural enzymes.