Real-time collision-free motion planning of a mobile robot using a Neural Dynamics-based approach

A neural dynamics based approach is proposed for real-time motion planning with obstacle avoidance of a mobile robot in a nonstationary environment. The dynamics of each neuron in the topologically organized neural network is characterized by a shunting equation or an additive equation. The real-time collision-free robot motion is planned through the dynamic neural activity landscape of the neural network without any learning procedures and without any local collision-checking procedures at each step of the robot movement. Therefore the model algorithm is computationally simple. There are only local connections among neurons. The computational complexity linearly depends on the neural network size. The stability of the proposed neural network system is proved by qualitative analysis and a Lyapunov stability theory. The effectiveness and efficiency of the proposed approach are demonstrated through simulation studies.     

Autonomous mobile robot dynamic motion planning using hybrid fuzzy potential field

A new fuzzy-based potential field method is presented in this paper for autonomous mobile robot motion planning with dynamic environments including static or moving target and obstacles. Two fuzzy Mamdani and TSK models have been used to develop the total attractive and repulsive forces acting on the mobile robot. The attractive and repulsive forces were estimated using four inputs representing the relative position and velocity between the target and the robot in the x and y directions, in one hand, and between the obstacle and the robot, on the other hand. The proposed fuzzy potential field motion planning was investigated based on several conducted MATLAB simulation scenarios for robot motion planning within realistic dynamic environments. As it was noticed from these simulations that the proposed approach was able to provide the robot with collision-free path to softly land on the moving target and solve the local minimum problem within any stationary or dynamic environment compared to other potential field-based approaches.     

Real-time localization of an UAV using Kalman filter and a Wireless Sensor Network

A real-time localization algorithm is presented in this paper. The algorithm presented here uses an Extended Kalman Filter and is based on time difference of arrivals (TDOA) measurements of radio signal. The position and velocity of an Unmanned Aerial Vehicle (UAV) are successfully estimated in closed-loop in real-time in both hover and path following flights. Relatively small position errors obtained from the experiments, proves a good performance of the proposed algorithm.     

Intelligent mobile manipulator navigation using adaptive neuro-fuzzy systems

The work presented in this paper deals with the problem of autonomous and intelligent navigation of mobile manipulator, where the unavailability of a complete mathematical model of robot systems and uncertainties of sensor data make the used of approximate reasoning to the design of autonomous motion control very attractive.A modular fuzzy navigation method in changing and dynamic unstructured environments has been developed. For a manipulator arm, we apply the robust adaptive fuzzy reactive motion planning developed in [J.B. Mbede, X. Huang, M. Wang, Robust neuro-fuzzy sensor-based motion control among dynamic obstacles for robot manipulators, IEEE Transactions on Fuzzy Systems 11 (2) (2003) 249-261]. But for the vehicle platform, we combine the advantages of probabilistic roadmap as global planner and fuzzy reactive based on idea of elastic band. This fuzzy local planner based on a computational efficient processing scheme maintains a permanent flexible path between two nodes in network generated by a probabilistic roadmap approach. In order to consider the compatibility of stabilization, mobilization and manipulation, we add the input of system stability in vehicle fuzzy navigation so that the mobile manipulator can avoid stably unknown and/or dynamic obstacles. The purpose of an integration of robust controller and modified Elman neural network (MENN) is to deal with uncertainties, which can be translated in the output membership functions of fuzzy systems.     

Observer Kalman filter identification of an autonomous underwater vehicle

This paper deals with the identification of linear discrete-time multivariable models of an autonomous underwater vehicle (AUV). The observer Kalman filter identification (OKID) method is applied with the main objective of evaluating its effectiveness to the experimental identification of the dynamic behaviour of an AUV. After presenting the mathematical background of the OKID algorithm, the proposed method is first validated on the basis of simulated data of both the linearized and nonlinear yaw dynamics of an AUV. Subsequently, the identification algorithm is applied to a set of experimental data. Results suggest that the method can be an efficient tool for the experimental identification of AUV dynamics.     

Trajectory tracking for an autonomous airship using fuzzy adaptive sliding mode control

We present a novel control approach for trajectory tracking of an autonomous airship.First,the dynamics model and the trajectory control problem of an airship are formulated.Second,the sliding mode control law is designed to track a time-varying reference trajectory.To achieve better control performance,fuzzy adaptive sliding mode control is proposed in which the control gains are tuned according to fuzzy rules,and an adaptation law is used to guarantee that the control gains can compensate for model uncertainties of the airship.The stability of the closed-loop control system is proven via the Lyapunov theorem.Finally,simulation results illustrate the effectiveness and robustness of the proposed control scheme.     

A learning fuzzy algorithm for motion planning of mobile robots

A sensor-based fuzzy algorithm is proposed to navigate a mobile robot in a 2-dimensional unknown environment filled with stationary polygonal obstacles. When the robot is at the starting point, vertices of the obstacles that are visible from the robot are scanned by the sensors and the one with the highest priority is chosen. Here, priority is an output fuzzy variable whose value is determined by fuzzy rules. The robot is then navigated from the starting point to the chosen vertex along the line segment connecting these two points. Taking the chosen vertex as the new starting point, the next navigation decision is made. The navigation process will be repeated until the goal point is reached.In implementation of fuzzy rules, the ranges of fuzzy variables are parameters to be determined. In order to evaluate the effect of different range parameters on the navigation algorithm, the total traveling distance of the robot is defined as the performance index first. Then a learning mechanism, which is similar to the simulated annealing method in the neural network theory, is presented to find the optimal range parameters which minimize the performance index. Several simulation examples are included for illustration.     

Compliant motion using a mobile manipulator: an operational space formulation approach to aircraft canopy polishing

The operational space formulation provides a framework for the analysis and control of robotic systems with respect to interactions with their environments. In this paper, we discuss its implementation on a mobile manipulator programmed to polish an aircraft canopy with a curved surface of unknown geometry. The polishing task requires the robot to apply a specified normal force on the canopy surface while simultaneously performing a compliant motion keeping the surface of the grinding tool tangentially in contact with the workpiece. A human operator controls the mobile base via a joystick to guide the polishing tool to desired areas on the canopy surface, effectively increasing the mobile manipulator's reachable workspace. The results demonstrate the efficacy of compliant motion and force regulation based on the operational space formulation for robots performing tasks in unknown environments with robustness towards base motion disturbances. The mobile manipulator consists of a PUMA 560 arm mounted on top of a Nomad XR4000 mobile base. Implementation issues are discussed and experimental results are shown.     

Door-opening behavior of an autonomous mobile manipulator by sequence of action primitives

A goal of this research is to accomplish a long distance navigation task by an autonomous mobile manipulator, including a behavior of “Passing through a doorway.” In our approach to this problem, we apply the concept of action primitives to the mobile manipulator control system. Action primitives are defined as unit elements of a complex behavior (such as door opening behavior), which control a robot according to a sequence of planned motion primitives. An important feature of the concept is that each action primitive is designed to include an error adjustment mechanism to cope with the accumulated position error of the mobile base. In this article, we report on the design and implementation of action primitives for a door opening task, and show experimental results for “Passing through a doorway” by an autonomous mobile manipulator using sequences of action primitives. © 1996 John Wiley & Sons, Inc.     

Task scheduling and motion planning for an industrial manipulator

In many robotic industrial applications, a manipulator should move among obstacles and reach a set of task-points in order to perform a pre-defined task. It is quite important as well as very complicated to determine the time-optimum sequence of the task-points visited by the end-effector's tip only once assuring that the manipulator's motion through the successive task-points is collision-free.     

Construction of self-learning fuzzy controllers using autonomous adaptive control methodology

A method of constructing adaptive fuzzy controllers using autonomous adaptive control methodology is considered. Knowledge in the system is represented in the form of fuzzy production rules. New rules are automatically generated by clustering empirical data obtained using substractive method in the course of system operation. The system is adapted with application of a special quantity calculated for each rule, the “adequacy degree”, which specifies the weight of the rule in the course of control. The method developed can be used for constructing applied control systems of dynamic objects. This opportunity is shown experimentally using the problems of balancing an inverted pendulum and stabilizing the angular motion of a spacecraft.     

Control of an omnidirectional spherical mobile robot using an adaptive Mamdani-type fuzzy control strategy

Neural Computing and Applications - In this study, a supervisory control system with an adaptive Mamdani-type fuzzy controller (AMFC) is used to control an omnidirectional spherical mobile robot...     

Online tuning fuzzy PID controller using robust extended Kalman filter

Fuzzy PID controllers have been developed and applied to many fields for over a period of 30 years. However, there is no systematic method to design membership functions (MFs) for inputs and outputs of a fuzzy system. Then optimizing the MFs is considered as a system identification problem for a nonlinear dynamic system which makes control challenges. This paper presents a novel online method using a robust extended Kalman filter to optimize a Mamdani fuzzy PID controller. The robust extended Kalman filter (REKF) is used to adjust the controller parameters automatically during the operation process of any system applying the controller to minimize the control error. The fuzzy PID controller is tuned about the shape of MFs and rules to adapt with the working conditions and the control performance is improved significantly. The proposed method in this research is verified by its application to the force control problem of an electro-hydraulic actuator. Simulations and experimental results show that proposed method is effective for the online optimization of the fuzzy PID controller.     

A layered goal-oriented fuzzy motion planning strategy for mobile robot navigation.

Most conventional motion planning algorithms that are based on the model of the environment cannot perform well when dealing with the navigation problem for real-world mobile robots where the environment is unknown and can change dynamically. In this paper, a layered goal-oriented motion planning strategy using fuzzy logic is developed for a mobile robot navigating in an unknown environment. The information about the global goal and the long-range sensory data are used by the first layer of the planner to produce an intermediate goal, referred to as the way-point, that gives a favorable direction in terms of seeking the goal within the detected area. The second layer of the planner takes this way-point as a subgoal and, using short-range sensory data, guides the robot to reach the subgoal while avoiding collisions. The resulting path, connecting an initial point to a goal position, is similar to the path produced by the visibility graph motion planning method, but in this approach there is no assumption about the environment. Due to its simplicity and capability for real-time implementation, fuzzy logic has been used for the proposed motion planning strategy. The resulting navigation system is implemented on a real mobile robot, Koala, and tested in various environments. Experimental results are presented which demonstrate the effectiveness of the proposed fuzzy navigation system.     

Path planning strategy for autonomous mobile robot navigation using Petri-GA optimisation

In this paper, a novel knowledge based genetic algorithm (GA) for path planning of multiple robots for multiple targets seeking behaviour in presence of obstacles is proposed. GA technique has been incorporated in Petri-Net model to make an integrated navigational controller. The proposed algorithm is based upon an iterative non-linear search, which utilises matches between observed geometry of the environment and a priori map of position locations, to estimate a suitable heading angle, there by correcting the position and orientation of the robots to find targets. This knowledge based GA is capable of finding an optimal or near optimal robot path in complex environments. The Petri-GA model can handle inter robot collision avoidance more effectively than the stand alone GA. The resulting navigation algorithm has been implemented on real mobile robots and tested in various environments to validate the developed control scheme.     

Design and motion planning of an autonomous climbing robot with claws

This paper presents the design of a novel robot capable of climbing on vertical and rough surfaces, such as stucco walls. Termed CLIBO (claw inspired robot), the robot can remain in position for a long period of time. Such a capability offers important civilian and military advantages such as surveillance, observation, search and rescue and even for entertainment and games. The robot’s kinematics and motion, is a combination between mimicking a technique commonly used in rock climbing using four limbs to climb and a method used by cats to climb on trees with their claws. It uses four legs, each with four-degrees-of-freedom (4-DOF) and specially designed claws attached to each leg that enable it to maneuver itself up the wall and to move in any direction. At the tip of each leg is a gripping device made of 12 fishing hooks and aligned in such a way that each hook can move independently on the wall’s surface. This design has the advantage of not requiring a tail-like structure that would press against the surface to balance its weight. A locomotion algorithm was developed to provide the robot with an autonomous capability for climbing along the pre-designed route. The algorithm takes into account the kinematics of the robot and the contact forces applied on the foot pads. In addition, the design provides the robot with the ability to review its gripping strength in order to achieve and maintain a high degree of reliability in its attachment to the wall. An experimental robot was built to validate the model and its motion algorithm. Experiments demonstrate the high reliability of the special gripping device and the efficiency of the motion planning algorithm.     

破拆机器人机械臂自主运动控制系统软件设计

针对现有破拆机器人手工定位时间长、定位不精确的问题,研发了一个基于激光定位的破拆机器人机械臂自主运动的控制系统软件。设计了运动学模块、闭环反馈调节模块、液压装置控制模块、手眼标定模块和运动控制模块。实际使用效果表明,该软件系统能完成控制系统设计指标,提高工作效率。     

Sampling-based motion planning of manipulator with goal-oriented sampling

Intelligent Service Robotics - A sampling-based planning algorithm is one of the most powerful tools for collision avoidance in the motion planning of manipulators. However, this algorithm takes a...     

Fault-tolerant motion planning and control of redundant manipulator

One important issue in the motion planning of a kinematic redundant manipulator is fault tolerance. In general, if the motion planner is fault tolerant, the manipulator can achieve the required path of the end-effector even when its joint fails. In this situation, the contribution of the faulty joint to the end-effector is required to be compensated by the healthy joints to maintain the prescribed end-effector trajectory. To achieve this, this paper proposes a fault-tolerant motion planning scheme by adding a simple fault-tolerant equality constraint for the faulty joint. Such a scheme is then unified into a quadratic program (QP), which incorporates joint-physical constraints such as joint limits and joint-velocity limits. In addition, a numerical computing solver based on linear variational inequalities (LVI) is presented for the real-time QP solving. Simulative studies and experimental results based on a six degrees-of-freedom (DOF) redundant robot manipulator with variable joint-velocity limits substantiate the effectiveness of the proposed fault-tolerant scheme and its solution.