Hans Dulimarta

Climbing the walls [robots]

Presents two underactuated kinematic designs for miniature climbing robots. They use suction. The underactuation is to save weight.

Design, implementation, and evaluation of an under-actuated miniature biped climbing robot

The design, implementation, and evaluation of a miniature biped robot for urban reconnaissance are presented. Design specifications for mobility, space requirement weight, sensing, and control are defined. A revolute hip joint is selected based on its enhanced mobility and capability to function in reasonably confined spaces. Small size dictates minimal weight, which is achieved by an under actuated joint structure, providing steering at only one foot, minimizing sensors, and structural optimization. The smart robotic foot supports the robot on a variety of smooth surfaces and provides feedback when a firm grip is established. Adaptable control strategies and dithering are implemented in lieu of minimal sensors and uncertainty created by backlash, gravity, and compliance in the suction feet. The robot is evaluated while performing tasks on surfaces with a variety of inclinations.

Modeling and control of an under-actuated miniature crawler robot

This paper presents the modeling and control of our second generation prototype miniature crawler robot which was targeted to applications in constrained environments. The mechanical design and the drive mechanism of the robot are first discussed A kinematic model is then derived and the motion planning is analyzed. A description of the Texas Instrument DSP-based embedded controller is presented. Finally, experimental results are presented for evaluation of the robot performance.

DSP solution for wall-climber micro-robot control using TMS320LF2407 chip

This paper describes the controller design for a wall-climber micro-robot using a DSP chip. Because of its high-speed performance, its support for multi-motor control and its low power consumption, the TMS320LF2407 DSP from Texas Instruments (TI) demonstrates itself as an ideal candidate of the single chip controller for the wall-climber micro-robot.

Motion planning of a bipedal miniature crawling robot in hybrid configuration space

This paper describes the motion planning of a bipedal crawling robot with an under-actuated mechanism and multiple locomotion modes. A hybrid configuration space is proposed to incorporate the continuous configuration space with discrete motion status space imposed by the kinematic constraints. Under the hybrid configuration space framework, a motion planning method is developed which consists of a global planner and a local planner to generate a collision-free path and a feasible motion sequence to travel along the path. A cost function is defined based on the motion status information to guide the search for an optimal path. Simulation and experimental results have verified the theoretical development.

8 Reasons Why You Should Use Mobile Platforms in Your CS Courses

Mobile computing represents yet another significant paradigm shift in computing. On a very fundamental level, it is changing the way computing integrates into our daily lives, and also impacts what we should be teaching future computer scientists in our classrooms. In this paper establish our rationale for including mobile technology content in our CS courses. We then summarize our experiences to-date and provide a set of practical guidelines to help others incorporate mobile into their CS courses.

Modular agents for robot navigation

Building an expandable control strategy for a mobile robot system that is capable of performing a given task or tasks is an important issue. For instance, new hardware and peripherals keep emerging as better and faster technologies become available. In this paper, we describe a system that is designed for controlling a mobile robot system with multiple sensors or peripherals. We propose a control scheme that employs at least as many server modules as there are hardware or peripheral components. Thus, a server is dedicated to controlling one hardware component and responds to all requests designated to it. Since hardware components are independent of each other, the server modules can run concurrently and new peripherals can be easily added without modifying the existing system. A typical configuration of a system consists of several task specific modules, each carrying a subgoal as part of a common goal that the robot has to achieve. Information sharing among these modules is accomplished via communication with a central data server. All modules can send query or update requests of the information used by the entire system. The control system is designed to enable all the modules to run concurrently on different machines. Preliminary results on an indoor navigation task are encouraging.

Implementation of a monophonic note tracking algorithm on Android

Pitch tracking algorithms have been proposed in many digital speech processing literature. Among the practical use of pitch tracking are: improved recognition, improved speech synthesis, and semantic disambiguation. A similar problem to pitch tracking when applied to music input signals, is note tracking, i.e. detecting all the notes in the perceived music. The general problem of music recognition seems to be beyond the techniques that have been accomplished by the advances in digital speech processing. A “real music” signal is composed of multiple sound from several instruments, and digitally separating the mix into individual channels/tracks is a hard problem to solve. The algorithm described in this paper assumes that the input signal is produced by a single source and further it focuses on monophonic sound, as opposed to polyphonic sound where two or more notes are played at the same time. The algorithm described below has been implemented on an Android device using proper building blocks (Activity and Service) that comply with the Android design guidelines to achieve the best performance. In addition to the standard Android libraries from the latest Android SDK, the application also relies on a third-party library for digital signal processing routines. The Android implementation of the algorithm has been tested using input sources from human voice and musical instruments. The paper also shows the experimental results of handling these input sources.