Kwan S. Kwok

Bio: Kwan S. Kwok is an academic researcher from Sandia National Laboratories. The author has contributed to research in topics: Mobile robot & Robot. The author has an hindex of 6, co-authored 12 publications receiving 138 citations.

Analyzing the Multiple-target-multiple-agent Scenario Using Optimal Assignment Algorithms

Abstract: This work considers the problem of maximum utilization of a set of mobile robots with limited sensor-range capabilities and limited travel distances. The robots are initially in random positions. A set of robots properly guards or covers a region if every point within the region is within the effective sensor range of at least one vehicle. We wish to move the vehicles into surveillance positions so as to guard or cover a region, while minimizing the maximum distance traveled by any vehicle. This problem can be formulated as an assignment problem, in which we must optimally decide which robot to assign to which slot of a desired matrix of grid points. The cost function is the maximum distance traveled by any robot. Assignment problems can be solved very efficiently. Solution times for one hundred robots took only seconds on a Silicon Graphics Crimson workstation. The initial positions of all the robots can be sampled by a central base station and their newly assigned positions communicated back to the robots. Alternatively, the robots can establish their own coordinate system with the origin fixed at one of the robots and orientation determined by the compass bearing of another robot relative to this robot. This paper presents example solutions to the multiple-target-multiple-agent scenario using a matching algorithm. Two separate cases with one hundred agents in each were analyzed using this method. We have found these mobile robot problems to be a very interesting application of optimal assignment algorithms, and we expect this to be a fruitful area for future research.

Rapid 3-D digitizing and tool path generation for complex shapes

Abstract: A system for automatic tool path generation was developed at Sandia National Laboratories for finish machining operations. The system consists of a commercially available 5-axis milling machine controlled by Sandia developed software. This system was used to remove overspray on cast turbine blades. A laser-based, structured-light sensor, mounted on a tool holder, is used to collect 3D data points around the surface of the turbine blade. Using the digitized model of the blade, a tool path is generated which will drive a 0.375" CBN grinding pin around the tip of the blade. A fuzzified digital filter was developed to properly eliminate false sensor readings caused by burrs, holes and overspray. The digital filter was found to successfully generate the correct tool path for a blade with intentionally scanned holes and defects. The fuzzified filter improved the computation efficiency by a factor of 25. For application to general parts, an adaptive scanning algorithm was developed and presented with simulation and experimental results. A right pyramid and an ellipsoid were scanned successfully with the adaptive algorithm in simulation studies. In actual experiments, a nose cone and a turbine blade were successfully scanned. A complex shaped turbine blade was successfully scanned and finish machined using these algorithms.

Decentralized Fuzzy Control of Multiple Nonholonomic Vehicles

Abstract: This work considers the problem of controlling multiple nonholonomic vehicles so that they converge to a scent source without colliding with each other. Since the control is to be implemented on a simple 8-bit microcontroller, fuzzy control rules are used to simplify a linear quadratic regulator control design. The inputs to the fuzzy controllers for each vehicle are the noisy direction to the source, the distance to the closest neighbor vehicle, and the direction to the closest vehicle. These directions are discretized into four values: forward, behind, left, and right; and the distance into three values: near, far, and gone. The values of the control at these discrete values are obtained based on the collision-avoidance repulsive forces and an attractive force towards the goal. A fuzzy inference system is used to obtain control values from a small number of discrete input values. Simulation results are provided which demonstrate that the fuzzy control law performs well compared to the exact controller. In fact, the fuzzy controller demonstrates improved robustness to noise.

Path planning for complex terrain navigation via dynamic programming

Abstract: This work considers the problem of planning optimal paths for a mobile robot traversing complex terrain. In addition to the existing obstacles, locations in the terrain where the slope is too steep for the mobile robot to navigate safely without tipping over become mathematically equivalent to extra obstacles. To solve the optimal path problem, we use a dynamic programming approach. The dynamic programming approach utilized does not suffer the difficulties associated with spurious local minima that the artificial potential field approaches do. In fact, a globally optimal solution is guaranteed to be found if a feasible solution exists. The method is demonstrated on several complex examples including very complex terrains.

Decentralized fuzzy control of multiple nonholonomic vehicles

Abstract: This work considers the problem of controlling multiple nonholonomic vehicles so that they converge to a scent source without colliding with each other. Since the control is to be implemented on a simple 8-bit microcontroller, fuzzy control rules are used to simplify a linear quadratic regulator control design. The inputs to the fuzzy controllers for each vehicle are the noisy direction to the source, the distance to the closest neighbor vehicle, and the direction to the closest vehicle. These directions are discretized into four values: forward, behind, left, and right; and the distance into three values: near, far, and gone. The values of the control at these discrete values are obtained based on the collision-avoidance repulsive forces and an attractive force towards the goal. A fuzzy inference system is used to obtain control values for inputs between the small number of discrete input values. Simulation results are provided which demonstrate that the fuzzy control law performs well compared to the exact controller. In fact, the fuzzy controller demonstrates improved robustness to noise.

Dynamic Task Assignment and Path Planning of Multi-AUV System Based on an Improved Self-Organizing Map and Velocity Synthesis Method in Three-Dimensional Underwater Workspace

Abstract: For a 3-D underwater workspace with a variable ocean current, an integrated multiple autonomous underwater vehicle (AUV) dynamic task assignment and path planning algorithm is proposed by combing the improved self-organizing map (SOM) neural network and a novel velocity synthesis approach. The goal is to control a team of AUVs to reach all appointed target locations for only one time on the premise of workload balance and energy sufficiency while guaranteeing the least total and individual consumption in the presence of the variable ocean current. First, the SOM neuron network is developed to assign a team of AUVs to achieve multiple target locations in 3-D ocean environment. The working process involves special definition of the initial neural weights of the SOM network, the rule to select the winner, the computation of the neighborhood function, and the method to update weights. Then, the velocity synthesis approach is applied to plan the shortest path for each AUV to visit the corresponding target in a dynamic environment subject to the ocean current being variable and targets being movable. Lastly, to demonstrate the effectiveness of the proposed approach, simulation results are given in this paper.

A Survey of Sensor Selection Schemes in Wireless Sensor Networks

Abstract: One of the main goals of sensor networks is to provide accurate information about a sensing field for an extended period of time. This requires collecting measurements from as many sensors as possible to have a better view of the sensor surroundings. However, due to energy limitations and to prolong the network lifetime, the number of active sensors should be kept to a minimum. To resolve this conflict of interest, sensor selection schemes are used. In this paper, we survey different schemes that are used to select sensors. Based on the purpose of selection, we classify the schemes into (1) coverage schemes, (2) target tracking and localization schemes, (3) single mission assignment schemes and (4) multiple missions assignment schemes. We also look at solutions to relevant problems from other areas and consider their applicability to sensor networks. Finally, we take a look at the open research problems in this field.

Mobile Robot Control and Navigation: A Global Overview

Abstract: The aim of this paper is to provide a global overview of mobile robot control and navigation methodologies developed over the last decades. Mobile robots have been a substantial contributor to the welfare of modern society over the years, including the industrial, service, medical, and socialization sectors. The paper starts with a list of books on autonomous mobile robots and an overview of survey papers that cover a wide range of decision, control and navigation areas. The organization of the material follows the structure of the author’s recent book on mobile robot control. Thus, the following aspects of wheeled mobile robots are considered: kinematic modeling, dynamic modeling, conventional control, affine model-based control, invariant manifold-based control, model reference adaptive control, sliding-mode control, fuzzy and neural control, vision-based control, path and motion planning, localization and mapping, and control and software architectures.

Bioinspired Neural Network for Real-Time Cooperative Hunting by Multirobots in Unknown Environments

Abstract: Multiple robot cooperation is a challenging and critical issue in robotics. To conduct the cooperative hunting by multirobots in unknown and dynamic environments, the robots not only need to take into account basic problems (such as searching, path planning, and collision avoidance), but also need to cooperate in order to pursue and catch the evaders efficiently. In this paper, a novel approach based on a bioinspired neural network is proposed for the real-time cooperative hunting by multirobots, where the locations of evaders and the environment are unknown and changing. The bioinspired neural network is used for cooperative pursuing by the multirobot team. Some other algorithms are used to enable the robots to catch the evaders efficiently, such as the dynamic alliance and formation construction algorithm. In the proposed approach, the pursuing alliances can dynamically change and the robot motion can be adjusted in real-time to pursue the evader cooperatively, to guarantee that all the evaders can be caught efficiently. The proposed approach can deal with various situations such as when some robots break down, the environment has different boundary shapes, or the obstacles are linked with different shapes. The simulation results show that the proposed approach is capable of guiding the robots to achieve the hunting of multiple evaders in real-time efficiently.

A Neural Network Approach to Dynamic Task Assignment of Multirobots

Abstract: In this paper, a neural network approach to task assignment, based on a self-organizing map (SOM), is proposed for a multirobot system in dynamic environments subject to uncertainties. It is capable of dynamically controlling a group of mobile robots to achieve multiple tasks at different locations, so that the desired number of robots will arrive at every target location from arbitrary initial locations. In the proposed approach, the robot motion planning is integrated with the task assignment, thus the robots start to move once the overall task is given. The robot navigation can be dynamically adjusted to guarantee that each target location has the desired number of robots, even under uncertainties such as when some robots break down. The proposed approach is capable of dealing with changing environments. The effectiveness and efficiency of the proposed approach are demonstrated by simulation studies