A modified magnetic field (MMF) has been used for the generation of robot motion plans of a linked manipulator such as a robot arm, in a complex workspace while veering around enclosing obstacle walls. The MMF method ensures global convergence and collision constraint satisfaction while allowing the application of this method as an online plan generation and control technique in both Cartesian and configuration spaces. Methods for using the MMF to perform both the planning and dynamic control of a robot arm around obstacles and singular configurations are developed in this paper and results shown for a specific workspace configuration.

This paper introduces a new feedback algorithm for steering a point robot through an obstacle field. The key innovation is the use of a circulatory field to rotate the robot path around the obstacles instead of the common potential field which repels the robot. This idea is motivated by a charged particle in a magnetic field generated by a current flowing around the obstacle. In constrast, the potential field approach is associated with a repulsive static electric field generated by charges of the same polarity as the robot, on the obstacle. The circulatory field does not generate any spurious local minimum as it does not change the total energy of the system. By combining with an attractive potential field associated with the desired destination, this method achieves global convergence while avoiding collisions with obstacles.

This paper introduces and analyzes a method based on vector-quaternion pairs for accomplishing task-based control. The optimal control paradigm has been extended into quaternion space with a complete definition of the control law required to control a plant in such a space. A complete set of derivations of the quaternion algebra used to accomplish the optimal control have also been developed. The advantage of this approach is that it decouples the position and orientation states, thereby reducing the complexity of the control algorithms. The intended application of this research is task-level control of the five-fingered Anthrobot mounted on a PUMA 760.

There are numerous applications in which robots are needed to assist rather than replace human operators. These applications typically involve either work in a dangerous environment or performing tasks that are physically impossible for humans to accomplish by themselves. Robots used in this mode do not need to be autonomous because they can essentially be controlled by the operator. Nonetheless, it is difficult for an operator to flawlessly control a robot with a large number of degrees of freedom. Therefore, an intelligent control system is needed to correct and improve the operator's input to the robot. In this paper the authors discuss a central component in this control system known as the variably-autonomous trajectory generator (VATG). The VATG combines input from the operator with input from one or more autonomous control modules (CM) to generate the robot trajectory. The amount of impact the autonomous CMs will have on the overall trajectory will depend on a number of factors such as the task being performed and the sensing modalities available, hence the authors refer to this type of control as being variably-autonomous.>