Novel Method of Assessing Practical Intelligence Acquired in Mechatronics Laboratory Classes

Practical intelligence is often referred to as the ability of a person to solve practical challenges in a given domain. The lack of practical intelligence may be due to the way in which explicit knowledge is valued and subsequently assessed in engineering education, namely via examinations, tests, laboratory reports, and tutorial exercises. The lack of effective assessments on practical intelligence indicates implicit devaluation, which can significantly impair engineering students' ability to acquire practical intelligence. To solve this problem, the authors propose a new method of assessment for measuring practical intelligence acquired by engineering students after performing engineering laboratory classes. The novices-experts approach is used in designing the assessment instruments, based on the behaviors' of novices/experts observed and novices/experts representative work-related situations. The practical intelligence can be measured by calculating the difference between participants' and the experts' ratings; the closer the novices to experts, the higher the practical intelligence acquired.

Distributed Robots Path/Tasks Planning on Fetch Scheduling

A highly concurrent task-planner for distributed multi-robot systems in dynamical industrial feed-lines is presented in this chapter. The system deals with two main issues: a) a path-planning model and b) a robotic-tasks scheduler. A set of kinematic control laws based on directional derivatives model the dynamical robots interaction. Distributed wheeled mobile robots perform the execution of autonomous tasks concurrently and synchronized just in time. A planner model for distributed tasks to autonomously reconfigure and synchronize online change priority missions by the robotic primitives—sense, plan, and act—are proposed. The robotic tasks concern carry-and-fetch to different goals, and dispatching materials. Numerical simulation of mathematical formulation and real experiments illustrate the parallel computing capability and the distributed robot's behavior. Results depict robots dealing with highly concurrent tasks and dynamical events through a parallel scheme.

A Framework for RF-Visual SLAM for Cooperative Multi-Agent System

This chapter provides a framework for radio frequency visual simultaneous localization and mapping problems for a team of agents consisting of three blimps and beacons. In a cooperative system, each agent must establish reliable data sharing during a mission. Under these conditions, a framework was proposed which allows each agent to share the local information using peer-to-peer networking schemes. The RF-vSLAM algorithm seeks to acquire a map during navigation, simultaneously localizing itself using the map and received signal strength indicator information to predict the distance between agents. In this chapter, the authors address the problem of detection features using SIFT algorithms. The authors have considered the sea surface as the working environment. In this research, the framework consisted of two types of agents, where beacon representing the static agent and blimp representing the homogeneous mobile agent. The communication exchange between these two types of agents is an environmentally friendly monitoring system that preserves natural value of the selected area.

Modeling and Visual Navigation of Autonomous Surface Vessels

In this chapter, the authors address main issues of Navigation, Guidance, and Control (NGC) and vision system of Autonomous Surface Vessels (ASV). These issues compose research problems and related research findings in recent years. Related research results are reviewed first; then the hardware and subsystem of ASVs is introduced. For the typical rudder-propeller, three degrees of freedom horizontal underactuated model is presented. Visual ASV is applied more and more in complex and unknown environment with increasing demand of obstacles avoidance. Two examples of visual applications are demonstrated. One is riverbank identification using color segmentation and Hough Transform; the other is bridge detection using optical flow.

Tracking Control of a Nonholonomic Mobile Robot Using Neural Network

The goal of this chapter is to enable a nonholonomic mobile robot to track a specified trajectory with minimum tracking error. Towards that end, an adaptive P controller is designed whose gain parameters are tuned by using two feed-forward neural networks. Back-propagation algorithm is chosen for online learning process and posture-tracking errors are considered as error values for adjusting weights of neural networks. The tracking performance of the controller is illustrated for different trajectories with computer simulation using Matlab/Simulink. In addition, open-loop response of an experimental mobile robot is investigated for these different trajectories. Finally, the performance of the proposed controller is compared to a standard PID controller. The simulation results show that “adaptive P controller using neural networks” has superior tracking performance at adapting large disturbances for the mobile robot.

Mobile Worm-Like Robots for Pipe Inspection

This chapter describes various designs of multilink mobile robots intended to move inside the confined space of pipelines. The mathematical model that describes robot dynamics and controlled motion, which allows simulating different regimes of robot motion and determining design parameters of the device and its control system, is presented. The chapter contains the results of numerical simulations for different types of worm-like mobile robots. The experimental studies of the in-pipe robots prototypes and their analyses are presented in this chapter.

MEMS Microrobot with Pulse-Type Hardware Neural Networks Integrated Circuit

Hexapod locomotive Micro-Electro Mechanical Systems (MEMS) microrobot with Pulse-type Hardware Neural Networks (P-HNN) locomotion controlling system is presented in this chapter. MEMS microrobot is less than 5 mm width, length, and height in size. MEMS microrobot is made from a silicon wafer fabricated by micro fabrication technology to realize the small size mechanical components. The mechanical components of MEMS microrobot consists of body frames, legs, link mechanisms, and small size actuators. In addition, MEMS microrobot has a biologically inspired locomotion controlling system, which is the small size electrical components realized by P-HNN. P-HNN generates the driving pulses for actuators of the MEMS microrobot using pulse waveform such as biological neural networks. The MEMS microrobot emulates the locomotion method and the neural networks of an insect with small size actuator, link mechanisms, and P-HNN. As a result, MEMS microrobot performs hexapod locomotion using the driving pulses generated by P-HNN.

Mechatronic Design of Mobile Robots for Stable Obstacle Crossing at Low and High Speeds

This chapter presents recent mechatronics developments to create original terrestrial mobile robots capable of crossing obstacles and maintaining their stability on irregular grounds. Obstacle crossing is both considered at low and high speeds. The developed robots use wheeled propulsion, efficient on smooth grounds, and improve performance on irregular grounds with additional mobilities, bringing them closer to legged locomotion (hybrid locomotion). Two sections are dedicated to low speed obstacle crossing. Section two presents an original mobile robot combining four actuated wheels with an articulated frame to improve obstacle climbing. Section three extends this work to a new concept of modular poly-robot for agile transport of long payloads. The last two sections deal with high-speed motion. Section four describes new suspensions with four mobilities that maintain pitch stability of vehicles crossing obstacles at high speed. After the shock, section five demonstrates stable pitch control during ballistic phase by accelerating-braking the wheels in flight.

Kinodynamic Motion Planning for an X4-Flyer

This chapter describes kinodynamic motion planning and its application. Kinodynamics is the discipline that tries to solve kinematic constraints and dynamical constraints simultaneously. By using kinodynamic motion planning, control inputs can be generated in a much simpler way, compared to the conventional motion planning that solves kinematics and dynamics separately. After briefly overviewing the kinodynamic motion planning, its application to a flying robot is described in detail.

Dynamic Modeling and Control Techniques for a Quadrotor

This chapter presents the detailed dynamic model of a Vertical Take-Off and Landing (VTOL) type Unmanned Aerial Vehicle (UAV) known as the quadrotor. The mathematical model is derived based on Newton Euler formalism. This is followed by the development of a simulation environment on which the developed model is verified. Four control algorithms are developed to control the quadrotor's degrees of freedom: a linear PID controller, Gain Scheduling-based PID controller, nonlinear Sliding Mode, and Backstepping controllers. The performances of these controllers are compared through the developed simulation environment in terms of their dynamic performance, stability, and the effect of possible disturbances.