Singularities of a manipulator have been addressed repeatedly. However, the singularities and the degree(s) of freedom, as a matter of fact, are two different aspects of the mobility of a manipulator. Consequently, this paper dedicates to discussing the mobility properties through mobility space, which synchronously define the type, number and direction characteristics of the independent motions that the manipulator should execute. The mobility space of a manipulator can be obtained with reciprocal screws of the manipulator via singular value decomposition, which instantaneously depicts the singularity and mobility problems of the manipulators. Application example demonstrates that this methodology can investigate the all-sided mobility properties of parallel manipulators.

This paper presents a singularity analysis for a 3-DOF parallel manipulator with R-P-S (Revolute-Prismatic-Spherical) joint structure. All three types of singularities are investigated with most attention paid for direct kinematics singularities (DKS). The loci of inverse kinematics and combined singularities are identified using a new approach. The equation of DKS is defined first from the condition of existence of an instantaneous motion. The geometrical method is used to find the loci of trajectories corresponding to DKS-s. As a result of these investigations, an optimization procedure was proposed of a robot design in order to have an enlarged singularity free part of the working space. The construction of a singularity free path is discussed without changing the robot trajectory by selecting the appropriate inverse kinematics task solution.

In recent years, cooperation of heterogeneous multiagents (HMAs) has achieved formidable results and gained an increasing attention by researchers. This paper presents an entertainment robot system based on the HMAs' cooperation. This robot is used to distinguish the basic information of a person from the audience and entertain him in the exhibition hall. The main agents include the Arbitration System (AS), the Face Recognition System (FRS), the Position Cognition System (PCS), and the Behavior Planner (BP), which are carved up according to their respective functions. Each agent completes its own task independently and the robot finishes the whole mission through their cooperation. The hybrid control architecture and the data-fusion algorithm based on Bayesian Belief Network are also discussed in this paper.

Hyper-redundant manipulators have a very large number of redundant degrees of freedom. They have been recognized as a means to improve manipulator performance in complex and unstructured environments. However, the high degree of redundancy also poses new challenges when performing inverse kinematics calculations. Prior works have shown that the workspace density (generated by sampling the joint space and evaluating the frequency of occurrence of the resulting end-effector reference frames) is a valuable quantity for use in ${\cal O}(P)$ inverse kinematics algorithms. Here $P$ is the number of modules in a manipulator constructed of a serial cascade of modules. This paper develops a new “divide-and-conquer” method for inverse kinematics using the workspace density. This method does not involve a high-dimensional Jacobian matrix and offers high accuracy. And its computational complexity is only ${\cal O}({\rm log}_2\,P)$, which makes it ideal for very high degree-of-freedom systems. Numerical simulations are performed to demonstrate this new method on a planar example, and a detailed comparison with a breadth-first search method is conducted.