C.S.G. Lee

Neural-network-based fuzzy logic control and decision system

A general neural-network (connectionist) model for fuzzy logic control and decision systems is proposed. This connectionist model, in the form of feedforward multilayer net, combines the idea of fuzzy logic controller and neural-network structure and learning abilities into an integrated neural-network-based fuzzy logic control and decision system. A fuzzy logic control decision network is constructed automatically by learning the training examples itself. By combining both unsupervised (self-organized) and supervised learning schemes, the learning speed converges much faster than the original backpropagation learning algorithm. The connectionist structure avoids the rule-matching time of the inference engine in the traditional fuzzy logic system. Two examples are presented to illustrate the performance and applicability of the proposed model. >

An Approximation Theory of Optimal Control for Trainable Manipulators

A theoretical procedure is developed for comparing the performance of arbitrarily selected admissible controls among themselves and with the optimal solution of a nonlinear optimal control problem. A recursive algorithm is proposed for sequential improvement of the control law which converges to the optimal. It is based on the monotonicity between the changes of the Hamiltonian and the value functions proposed by Rekasius, and may provide a procedure for selecting effective controls for nonlinear systems. The approach has been applied to the approximately optimal control of a trainable manipulator with seven degrees of freedom, where the controller is used for motion coordination and optimal execution of object-handling tasks.

Weighted selection of image features for resolved rate visual feedback control

The authors develop methodologies for the automatic selection of image features to be used to visually control the relative position and orientation (pose) between the end-effector of an eye-in-hand robot and a workpiece. A resolved motion rate control scheme is used to update the robots pose based on the position of three features in the cameras image. The selection of these three features depends on a blend of image recognition and control criteria. The image recognition criteria include feature robustness, completeness, cost of feature extraction, and feature uniqueness. The control criteria include system observability, controllability, and sensitivity. A weighted criteria function is used to select the combination of image features that provides the best control of the end-effector of a general six-degrees-of-freedom manipulator. Both computer simulations and laboratory experiments on a PUMA robot arm were conducted to verify the performance of the feature-selection criteria. >

Collision-Free Motion Planning of Two Robots

An approach to collision-free motion planning of two moving robots in a common workspace is presented. Each robot is represented by a sphere containing the wrist and the manipulator hand. The results from a strictly straight line trajectory planning method are utilized for planning a path avoiding potential collisions. Due to the distinct nature of the potential collisions between the two moving robots, a new classification of path requirement situations is presented and utilized for planning a collision-free path. Notions of collision map and time scheduling are developed and applied for realizing a collision-free motion planning. A procedure is developed for the time scheduling of the straight line trajectory. An example is shown for the time scheduling of the trajectory, which shows the significance of the proposed approach in collision-free motion planning of the two moving robots.

An adaptive control strategy for mechanical manipulators

This paper focuses on the study of an adaptive perturbation control which tracks a desired time-based trajectory as close as possible for all times over a wide range of manipulator motion and payloads. The proposed adaptive control is based on the linearized perturbation equations in the vicinity of a nominal trajectory. The controlled system is characterized by feedforward and feedback components which can be computed separately and simultaneously. The feedforward component computes the nominal torques from the Newton-Euler equations of motion to compensate all the interaction forces among the various joints. The feedback component consisting of recursive least-square identification and an optimal adaptive self-tuning control algorithm for the linearized system computes the perturbation torques which reduce the position and velocity errors of the manipulator along the nominal trajectory. A computer simulation study was conducted to evaluate the performance of the proposed adaptive control.

Self-adaptive recurrent neuro-fuzzy control for an autonomous underwater vehicle

This paper presents the utilization of a self-adaptive recurrent neuro-fuzzy control as a feedforward controller and a proportional-plus-derivative (PD) control as a feedback controller for controlling an autonomous underwater vehicle (AUV) in an unstructured environment. Without a priori knowledge, the recurrent neuro-fuzzy system is first trained to model the inverse dynamics of the AUV and then utilized as a feedforward controller to compute the nominal torque of the AUV along a desired trajectory. The PD feedback controller computes the error torque to minimize the system error along the desired trajectory. This error torque also provides an error signal for online updating the parameters in the recurrent neuro-fuzzy control to adapt in a changing environment. A systematic self-adaptive learning algorithm, consisting of a mapping-constrained agglomerative clustering algorithm for the structure learning and a recursive recurrent learning algorithm for the parameter learning, has been developed to construct the recurrent neuro-fuzzy system to model the inverse dynamics of an AUV with fast learning convergence. Computer simulations of the proposed recurrent neuro-fuzzy control scheme and its performance comparison with some existing controllers have been conducted to validate the effectiveness of the proposed approach.

A multiprocessor-based controller for the control of mechanical manipulators

A cost-effective architecture for the control of mechanical manipulators based on a functional decomposition of the equations of motion of a manipulator are described. The Lagrange-Euler and the Newton-Euler formulations were considered for this decomposition. The functional decomposition separates the inertial, Coriolis and centrifugal, and gravity terms of the Lagrange-Euler equations of motion. The recursive nature of the Newton-Euler equations of motion lend themselves to being decomposed to the terms used to generate the recursive forward and backward equations. Architectures tuned to the functional flow of the two algorithms were examined. An architecture which meets our design criterion is proposed. The proposed controller architecture can best be described as a macro level pipeline, with parallelism within elements of the pipeline. The pipeline is designed to take maximum benefit of the serial nature of the Newton-Euler equations of motion.

A framework of knowledge-based assembly planning

A framework of knowledge-based assembly planning for the automatic generation of assembly plans from the CAD model of an assembly is presented. The knowledge about assembly structure, precedence constraints, and resource constraints is represented using predicate calculus, and it forms the static knowledge database. Production rules are used to generate assembly plans. A graph search mechanism is used to search for the optimal assembly plan. To test the proposed approach, a prototype system has been developed. It can read in the CAD data of a product and the resource information of an assembly cell and automatically generate an optimal assembly plan based on the selection criteria specified by the user of the system.<<ETX>>

Precedence knowledge in feature mating operation assembly planning

The authors discuss the representation and acquisition of the precedence knowledge of an assembly, which plays an important role in the generation of assembly sequences and the planning of assembly. An efficient and complete symbolic representation has been developed to express the precedence knowledge clearly and precisely. This symbolic representation makes it possible to perform reasoning and manipulation of the precedence knowledge. Furthermore, the representation is complete in the sense that it can represent the assembly precedence knowledge as well as the disassembly precedence knowledge and these two forms of knowledge can be transformed from one to another. A geometric mating graph is developed to include all the necessary geometric and topological information for the precedence knowledge acquisition. Two algorithms are developed to obtain the precedence knowledge from the geometric mating graph systematically. The disassembly precedence knowledge thus obtained is equivalent to the assembly precedence knowledge and can be used to generate all the possible sets of assembly sequences.<<ETX>>

Efficient scheduling algorithms for robot inverse dynamics computation on a multiprocessor system

The problem of scheduling the robot inverse dynamics computation consisting of m computational modules to be executed on a multiprocessor system consisting of p identical homogeneous processors to achieve a minimum-schedule length is examined. This scheduling problem is known to be NP-complete. To achieve the minimum computation time, the Newton-Euler equations of motion are expressed in the homogeneous linear recurrence form that results in achieving maximum parallelism. To speed up the searching for a solution, a heuristic search algorithm called dynamical highest-level-first/most-immediate-successors-first (DHLF/MISF) is proposed to find a fast but suboptimal schedule. For an optimal schedule the minimum-schedule-length problem can be solved by a state-space search method, the A* algorithm coupled with an efficient heuristic function derived from the Fernandez and Bussell bound. >