Task reconstruction method for real-time singularity avoidance for robotic manipulators

The kinematic (KS) and algorithmic singularities (AS) in controlling robotic manipulators have been investigated intensively because they are not predictable or difficult to avoid. The problem with handling these singularities is an unnecessary performance reduction in the non-singular region and the difficulty in performance tuning. In this paper, we propose a method of avoiding KS and AS by applying a task reconstruction approach while maximizing the task performance by calculating singularity measures. The proposed method is implemented by removing the component approaching the singularity calculated by using a singularity measure in real-time. The outstanding feature of the proposed task reconstruction (TR) method is that it is based on a local TR as opposed to the local joint reconstruction of many other approaches. This method has a dynamic task priority assignment feature which ensures system stability under singular regions due to the change of task priority. The TR method enables us to increase the task controller gain to improve the task performance, whereas this increase can destabilize the system for conventional algorithms in real experiments. In addition, the physical meaning of tuning parameters is very straightforward. Hence, we can maximize task performance even near the singular region while simultaneously obtaining the singularity-free motion. The advantage of the proposed method is experimentally tested by using a 7-d. o. f. spatial manipulator and the result shows that the new method improves the performance several times over the existing algorithms.     

Coordination of Multiple Control Schemes for Mobile Robot Navigation on the Basis of the Generalized Stochastic Petri-Nets

There have been two major streams of research for the motion control of mobile robots: model-based deliberate control and sensor-based reactive control. Since the two schemes have complementary advantages and disadvantages, each cannot completely replace the other. There are a variety of environmental conditions that affect the performance of navigation. The motivation of this study is that multiple motion control schemes are required to survive in dynamic real environments. In this paper, we exploit two discrete motion controllers for mobile robots. One is the deliberate trajectory tracking controller and the other is the reactive dynamic window approach. We propose the selective coordination of two controllers on the basis of the generalized stochastic Petri net (GSPN) framework. The major scope of this paper is to clarify the advantage of the proposed controller based on the coordination of multiple controllers from the results of quantitative performance comparison among motion controllers. For quantitative comparison, both simulations and experiments in dynamic environments were carried out. In addition, it is shown that navigation experiences are accumulated in the GSPN formalism. The performance of navigation service can be significantly improved owing to the automatically stored experiences.     

Fuzzy logic techniques for navigation of several mobile robots

In this paper, navigation techniques for several mobile robots as many as one thousand robots using fuzzy logic are investigated in a totally unknown environment. Fuzzy logic controllers (FLC) using different membership functions are developed and used to navigate mobile robots. First a fuzzy controller has been used with four types of input members, two types of output members and three parameters each. Next two types of fuzzy controllers have been developed having same input members and output members with five parameters each. Each robot has an array of ultrasonic sensors for measuring the distances of obstacles around it and an infrared sensor for detecting the bearing of the target. These techniques have been demonstrated in various exercises, which depicts that the robots are able to avoid obstacles as well as negotiate the dead ends and reach the targets efficiently. Amongst the techniques developed, FLC having Gaussian membership function is found to be most efficient for mobile robots navigation.     

Multiple tasks manipulation for a robotic manipulator

Robotic manipulators can execute multiple tasks precisely at the same time and, thus, the task-priority scheme plays an important role in implementing multiple tasks. Until now, several algorithms for task-priority have been used in solving the inverse kinematics for redundant manipulators. In this paper, through the comparative study of existing algorithms, we will propose a new method for task-priority manipulation in terms of two important criteria—algorithmic singularity and task error. This manipulation scheme will be applied to a planar three-link manipulator to demonstrate its effectiveness.     

Analysis of local-camera-based shepherding navigation

This study investigates group navigation with the aid of strong interaction between two kinds of agents: A shepherd drives a sheep group with a large population to a given goal position. Even though numerous studies have been performed on the realization of shepherd-like navigation, they are based on the condition that all sheep positions are given. This study examines the navigation of a sheep group using a local-camera-based approach, i.e. a shepherd perceives sheep using the shepherd's vision. Before testing local-camera-based navigation, we design a shepherd controller referred to as a farthest-agent targeting controller, in which the shepherd selects the sheep farthest from the goal. We demonstrate the validity of the proposed controller using statistical analysis and comparison with previous conventional controllers. After examining the effectiveness of this controller, we show that the controller works appropriately even if the shepherd cannot know all sheep positions. In addition, we show the robustness of the proposed controller for the positional errors of the sheep flock or for agent-lost cases to apply it to real-world situations.     

Sailboat navigation control system based on spiking neural networks

In this paper, we presented the development of a navigation control system for a sailboat based on spiking neural networks (SNN). Our inspiration for this choice of network lies in their potential to achieve fast and low-energy computing on specialized hardware. To train our system, we use the modulated spike time-dependent plasticity reinforcement learning rule and a simulation environment based on the BindsNET library and USVSim simulator. Our objective was to develop a spiking neural network-based control systems that can learn policies allowing sailboats to navigate between two points by following a straight line or performing tacking and gybing strategies, depending on the sailing scenario conditions. We presented the mathematical definition of the problem, the operation scheme of the simulation environment, the spiking neural network controllers, and the control strategy used. As a result, we obtained 425 SNN-based controllers that completed the proposed navigation task, indicating that the simulation environment and the implemented control strategy work effectively. Finally, we compare the behavior of our best controller with other algorithms and present some possible strategies to improve its performance.     

Evolution of Fuzzy Controllers for Robotic Vehicles: The Role of Fitness Function Selection

An important issue not addressed in the literature, is related to the selection of the fitness function parameters which are used in the evolution process of fuzzy logic controllers for mobile robot navigation. The majority of the fitness functions used for controllers evolution are empirically selected and (most of times) task specified. This results to controllers which heavily depend on fitness function selection. In this paper we compare three major different types of fitness functions and how they affect the navigation performance of a fuzzy logic controlled real robot. Genetic algorithms are employed to evolve the membership functions of these controllers. Further, an efficiency measure is introduced for the systematic analysis and benchmarking of overall performance. This measure takes into account important performance results of the robot during experimentation, such as the final distance from target, the time needed to reach its final position, the time of sensor activation, the mean linear velocity e.t.c. In order to examine the validity of our approach a low cost mobile robot has been developed, which is used as a testbed.     

Harmony search-based hybrid stable adaptive fuzzy tracking controllers for vision-based mobile robot navigation

In this paper the harmony search (HS) algorithm and Lyapunov theory are hybridized together to design a stable adaptive fuzzy tracking control strategy for vision-based navigation of autonomous mobile robots. The proposed variant of HS algorithm, with complete dynamic harmony memory (named here as DyHS algorithm), is utilized to design two self-adaptive fuzzy controllers, for $x$ -direction and $y$ -direction movements of a mobile robot. These fuzzy controllers are optimized, both in their structures and free parameters, such that they can guarantee desired stability and simultaneously they can provide satisfactory tracking performance for the vision-based navigation of mobile robots. In addition, the concurrent and preferential combinations of global-search capability, utilizing DyHS algorithm, and Lyapunov theory-based local search method, are employed simultaneously to provide a high degree of automation in the controller design process. The proposed schemes have been implemented in both simulation and real-life experiments. The results demonstrate the usefulness of the proposed design strategy and shows overall comparable performances, when compared with two other competing stochastic optimization algorithms, namely, genetic algorithm and particle swarm optimization.     

Fuzzy-Logic Based Navigation of Underwater Vehicles

A fuzzy logic based general purpose modular control architecture is presented for underwater vehicle autonomous navigation, control and collision avoidance. Three levels of fuzzy controllers comprising the sensor fusion module, the collision avoidance module and the motion control module are derived and implemented. No assumption is made on the specific underwater vehicle type, on the amount of a priori knowledge of the 3-D undersea environment or on static and dynamic obstacle size and velocity. The derived controllers account for vehicle position accuracy and vertical stability in the presence of ocean currents and constraints imposed by the roll motion. The main advantage of the proposed navigation control architecture is its simplicity, modularity, expandability and applicability to any type of autonomous or semi-autonomous underwater vehicles. Extensive simulation studies are performed on the NPS Phoenix vehicle whose dynamics have been modified to account for roll stability.     

Inevitable collision states — a step towards safer robots?

An inevitable collision state for a robotic system can be defined as a state for which, no matter what the future trajectory followed by the system is, a collision with an obstacle eventually occurs. An inevitable collision state takes into account the dynamics of both the system and the obstacles, fixed or moving. The main contribution of this paper is to lay down and explore this novel concept (and the companion concept of inevitable collision obstacle). Formal definitions of the inevitable collision states and obstacles are given. Properties fundamental for their characterization are established. This concept is very general, and can be useful both for navigation and motion planning purposes (for its own safety, a robotic system should never find itself in an inevitable collision state). To illustrate the interest of this concept, it is applied to a problem of safe motion planning for a robotic system subject to sensing constraints in a partially known environment (i.e. that may contain unexpected obstacles). In safe motion planning, the issue is to compute motions for which it is guaranteed that, no matter what happens at execution time, the robotic system never finds itself in a situation where there is no way for it to avoid collision with an unexpected obstacle.     

Design of an endoscopic solo surgery simulator for quantitative evaluation of the human–machine interface in robotic camera positioning systems

An endoscopic solo surgery simulator was designed to quantitatively evaluate the human-machine interface in robotic camera positioning systems. Our simulator can assess not only the quantitative efficiency of laparoscopic cameraworks but also the influence of cameraworks upon the accuracy of surgical actions. Two human-machine interfaces (a face motion navigation system and a voice activated system) were developed and compared. As a result, the face control interface was more efficient in cameraworks than the voice control, even under a stress to control the instruments. However, it was also found that the face motion may have an adverse influence on precise surgical actions.     

New tuning conditions for a class of nonlinear PID global regulators of robot manipulators

Several nonlinear proportional-integral-derivative (PID) controllers for robot manipulators that ensure global asymptotic stability have been proposed in the literature. However, the tuning criteria obtained are expressed in terms of conditions so restrictive that they have avoided, until now, carrying out experimental tests with such controllers. Tuning criteria of some PID controllers for robot manipulators with conditions more relaxed than those presented previously in the literature have been proposed in two recent works by the authors. This was achieved by setting the tuning conditions individually for each joint instead of general conditions for the whole robot. In this paper we extend these results to a class of nonlinear PID global regulators for robot manipulators. The obtained tuning criteria are given in terms of conditions so relaxed that they have allowed to carry out, for the first time, experimental essays with these controllers. Such experiments are presented in this paper using a two-degrees-of-freedom robot manipulator.     

Stability Analysis of a Vision-Based UAV Controller

The stability analysis of a vision-based control strategy for a quad rotorcraft UAV is addressed. In the present application, the imaging sensing system provides the required states for performing autonomous navigation missions, however, it introduces latencies and time-delays from the time of capture to the time when measurements are available. To overcome this issue, a hierarchical controller is designed considering a time-scale separation between fast and slow dynamics. The dynamics of the fast-time system are stabilized using classical proportional derivative controllers. Additionally, delay frequency and time domain techniques are explored to design a controller for the slow-time system. Simulations and experimental results consisting on a vision-based road following task are presented.     

New results on doubly coprime fractional representations of generalized dynamical systems

This note first points out that the main results by Wang and Balas regarding the doubly coprime fractional representations for generalized dynamical systems have severe limitation in their applications, that is, the doubly coprime factorization obtained by Wang and Balas cannot characterize the parameterization of all properly stabilizing controllers when a system is singular, therefore, truly generalized. To remedy those results, two new doubly coprime factorizations have been developed that will parameterize all properly stabilizing controllers for single-input or single-output cases. In addition, the new results can characterize the parameterization of all corresponding causal properly stabilizing controllers. Finally, the extension to the multiple-input-multiple-output case is presented.     

Design of a PID optimized neural networks and PD fuzzy logic controllers for a two‐wheeled mobile robot

In this paper, two intelligent techniques for a two‐wheeled differential mobile robot are designed and presented: A smart PID optimized neural networks based controller (SNNPIDC) and a PD fuzzy logic controller (PDFLC). Basically, mobile robots are required to work and navigate under exigent circumstances where the environment is hostile, full of disturbances such as holes and stones. The robot navigation leads to an autonomous decision making to overcome an obstacle and/or to stop the engine to protect it. In fact, the actuators that drive the robot should in no way be damaged and should stop to change direction in case of insurmountable disturbances. In this context, two controllers are implemented and a comparative study is carried out to demonstrate the effectiveness of the proposed approaches. For the first one, neural networks are used to optimize the parameters of a PID controller and for the second a fuzzy inference system type Mamdani based controller is adopted. The goal is to implement control algorithms for safe robot navigation while avoiding damage to the motors. In these two control cases, the smart robot has to quickly perform tasks and adapt to changing environment conditions while ensuring stability and accuracy and must be autonomous with regards to decision making. Simulations results aren't done in real environments, but are obtained with the Matlab/Simulink environment in which holes and stones are modeled by different load torques and are applied as disturbances on the mobile robot environment. These simulation results and the robot performances are satisfactory and are compared to a PID controller in which parameters are tuned by the Ziegler–Nichols tuning method. The applied methods have proven to be highly robust.     

Design and implementation of a hybrid fuzzy logic controller for a quadrotor VTOL vehicle

Helicopters have generated considerable interest in both the control community due to their complex dynamics, and in military community because of their advantages over regular aerial vehicles. In this paper, we present the modeling and control of a four rotor vertical take-off and landing (VTOL) unmanned air vehicle known as quadrotor aircraft. This model has been generated using Newton-Euler equations. In order to control the helicopter, classical PD (proportional derivative) and Hybrid Fuzzy PD controllers have been designed. Although fuzzy control of various dynamical systems has been presented in literature, application of this technology to quadrotor helicopter control is quite new. A quadrotor helicopter has nonlinear characteristics where classical control methods are not adequate especially when there are time delays, disturbances and nonlinear vehicle dynamics. On the other hand, Fuzzy control is nonlinear and it is thus suitable for nonlinear system control. Matlab Simulink has been used to test, analyze and compare the performance of the controllers in simulations. For the evaluation of the autonomous flight controllers, some experiments were also performed. For this purpose, an experimental test stand has been designed and manufactured. This study showed that although, both of the classical PD and the Fuzzy PD controllers can control the system properly, the Fuzzy PD controllers performed slightly better than the classical PD controllers, and have benefits such as better disturbance rejection, ease of building the controllers.     

Two degree-of-freedom design for a send-on-delta sampling PI control strategy

A complete event-based two-degree-of-freedom PI controller is presented. The architecture of the control system is based on two decoupled PI controllers, one for the set-point following and one for the load disturbance rejection task. The distinctive feature of the proposed approach is that the two controllers have the same parameters and the reference tracking performance is improved by suitably modifying the reference signal applied to the set-point following controller. Examples of the technique are given. In particular, the control strategy has been applied to a distributed solar collector field.     

Bioreactor profile control by a nonlinear auto regressive moving average neuro and two degree of freedom PID controllers

This paper presents the use of nonlinear auto regressive moving average (NARMA) neuro controller for temperature control and two degree of freedom PID (2DOF-PID) for pH and dissolved oxygen (DO) of a biochemical reactor in comparison with the industry standard anti-windup PID (AWU-PID) controllers. The process model of yeast fermentation described in terms of temperature, pH and dissolved oxygen has been used in this study. Nonlinear auto regressive moving average (NARMA) neuro controller used for temperature control has been trained by Levenberg–Marquardt training algorithm. The 2DOF-PID controllers used for pH and dissolved oxygen have been tuned by MATLAB's auto tune feature along with manual tuning. Random training data with input varying from 0 to 100 l/h have been obtained by using NARMA graphical interface. The data samples used for training, validation and testing are 20,000, 10,000 and 10,000 respectively. Random profiles have been used for simulation. The NARMA neuro controller and the 2DOF-PID controllers have shown improvement in rise time, residual error and overshoot. The proposed controllers have been implemented on TMS320 Digital Signal Processing board using code composure studio. Arduino Mega board has been used for input/output interface.     

Design of robust active queue management controllers for a class of TCP communication networks

This paper describes the design of active queue management (AQM) controllers for a class of TCP communication networks. In TCP/IP networks, the packet-dropping probability function is considered as a control input. Therefore, a TCP AQM controller was modeled as a time-delayed system with a saturated input. The objective of the work described here was to design robust controllers capable of achieving the desired queue size and guaranteeing asymptotic stability of the operating point. To achieve this aim, we have proposed two control strategies, namely a static state feedback controller and an observer-based controller. By applying the Lyapunov-Krasovskii functional approach and the linear matrix inequality technique, control laws and delay-independent stability criteria for the AQM controllers were derived. The performance of the two control schemes was evaluated in various network scenarios via a series of numerical simulations. The simulation results confirm that the proposed schemes outperform other AQM schemes.