Optimal trajectory planning of manipulators: a review

Over the past decades, machine learning techniques, such as supervised learning, reinforcement learning, and unsupervised learning, have been increasingly used in the control engineering community. Various learning algorithms have been developed to achieve autonomous operation and intelligent decision making for many complex and challenging control problems. One of such problems is bipedal walking robot control. Although still in their early stages, learning techniques have demonstrated promising potential to build adaptive control systems for bipedal robots. This paper gives a review of recent advances on the state-of-the-art learning algorithms and their applications to bipedal robot control. The effects and limitations of different learning techniques are discussed through a representative selection of examples from the literature. Guidelines for future research on learning control of bipedal robots are provided in the end.

Machine Learning Algorithms in Bipedal Robot Control

Real time gait generation for autonomous humanoid robots: A case study for walking

pdf/integration-of-service-robots-in-the-smart-home-by-means-of-24ipfc0jyn.pdf

Integration of service robots in the smart home by means of UPnP: A surveillance robot case study

In this paper, the non-holonomic characteristic of a free-floating space robotic system is used to plan the path of the manipulator joints, by whose motion the base attitude and the manipulator joints attain the desired states. Here, we parameterize the joint trajectory using sinusoidal functions, whose arguments are high-order polynomials. Then, we define the cost function for optimization according to the constraint conditions and the accuracy of the space robot. Finally, genetic algorithms (GAs) are used to search for the solutions of the parameters. Compared with others, our approach has advantages as follows. (i) The motion of the manipulator and the disturbance on the base are practically constrained. (ii) The dynamic singularities cannot affect the algorithm since only the direct kinematic equations are utilized. (iii) The planned path is smooth and more applicable for the control of the manipulator. (iv) The convergence of the algorithm is not affected by the attitude singularity since the orienta...

Non-holonomic Path Planning of a Free-Floating Space Robotic System Using Genetic Algorithms

Selecting an appropriate gait can reduce consumed energy by a biped robot. In this paper, a Genetic Algorithm gait synthesis method is proposed, which generates the angle trajectories based on the minimum consumed energy and minimum torque change. The gait synthesis is considered for two cases: walking and going up-stairs. The proposed method can be applied for a wide range of step lengths and step times during walking; or step lengths, stair heights and step times for going up-stairs. The angle trajectories are generated without neglecting the stability of the biped robot. The angle trajectories can be generated for other tasks to be performed by the biped robot, like going down-stairs, overcoming obstacles, etc. In order to verify the effectiveness of the proposed method, the results for minimum consumed energy and minimum torque change are compared. A Radial Basis Function Neural Network is considered for the real-time application. Simulations are realized based upon the parameters of the 'Bonten-Maru ...

Application of Genetic Algorithms for biped robot gait synthesis optimization during walking and going up-stairs

Recently, many control architectures for robots have been proposed. However, in these architectures, it is difficult to add new functions to existing applications or add new applications. Moreover, developing a robot control system using many researchers makes it difficult to cooperate with each other. In order to deal with these problems, we propose a Humanoid Robot Control Architecture (HRCA) based on Common Object Request Broker Architecture (CORBA). The proposed HRCA is organized as a client/server control architecture. The HRCA is implemented as an integration of many humanoid robot control modules, which correspond to CORBA servers and clients. By applying these to “Bonten‐Maru I” a humanoid robot, which is under development in our laboratory, we describe the HRCA modules and the effectiveness of HRCA. We confirmed the effectiveness of HRCA from simulation and experimental results. By using the proposed HRCA, the control of the humanoid robot in a distributed environment such as a Local Area Network (LAN) is possible and thus various humanoid robots in the world can share their own modules with each other via the Internet.

A CORBA‐based approach for humanoid robot control

This paper describes an automated trimming system of large glass fiber reinforced plastic (GFRP) using an omni-directional wheeled mobile robot (WMR) and its path control method. In trimming GFRP parts, much glass fiber and plastic powder dust occur and it becomes bad visible in environment. It is necessary to correct dead-reckoning errors of the WMR in order to control its moving path. We have discussed an external correction method of the dead-reckoning errors for the WMR using ultrasonic sensor.

/pdf/an-autonomous-trimming-system-of-large-glass-fiber-2z7j8f00cb.pdf

An autonomous trimming system of large glass fiber reinforced plastics parts using an omni-directional mobile robot and its control

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Application of Genetic Algorithms for biped robot gait synthesis optimization during walking and going up-stairs

A CORBA‐based approach for humanoid robot control

An autonomous trimming system of large glass fiber reinforced plastics parts using an omni-directional mobile robot and its control