Search

In this paper, the design and implementation of a novel passive safe joint is reported. This safe joint is designed for applications, such as an air hockey playing robot playing against a human, in which the force applied by the linkage should be kept below a certain threshold. The proposed mechanism benefits from the compliance of two ramps controlled by the nonlinear behavior of a spring. The passivity of the design, the small footprint, the low cost of production, and the ability to rotate 360 degrees are the main advantages of this design. The mechanism has been designed, evaluated, implemented and tested showing its effectiveness. Finally it has been incorporated in an air hockey robot showing its capability in action.

In this paper we propose a centralized algorithm for stable operation of a multi-robot dome inspection, repair, and maintenance based on potential field method. The multi-robot system consists of two robots, a leader robot and a supporter robot, connected to each other by two strings. The follower robot sends its information to the leader robot for processing and receiving commands. The leader robot uses the friction model, the slope of the dome detected by the two robots' tilt sensors and the mass of the robots to calculate the stability ranges and determines the proper action to make the whole system stable. The leader robot moves toward its target pose while adjusting the tension of its string using potential field method to combine the attractive and repulsive potentials. The follower robot executes the commands sent by the leader robot to create the most stable configuration for the whole system. It is shown that the algorithm can guarantee stable operation all over a typical dome. The algorithm has been implemented and shown its effectiveness in simulation. Furthermore, the effect of each parameter has been discussed and it is shown that the multi-robot system is capable of completely covering a typical dome using the proposed potential field algorithm.

The hybrid localization using Angle Of Arrival (AOA) and Di erential Received Strength Signal Indicator (DRSSI) of an RF source with unknown power and Non- Line-Of-Sight (NLOS) condition has been proven to be advantageous compared to using each method separately. In this paper, the initial hybrid method, which was implemented using particle lters due to the multi-modal/non-Gaussian nature of localization in NLOS condition, has been replaced by a multi-step Gaussian ltering approach which provides similar accuracy with better performance. This has been done using DRSSI input in the rst step of the ltering to determine the linearization point, and then using AOA and DRSSI inputs together in the second step of the ltering to improve the localization accuracy. The proposed method has been implemented using Extended Kalman lter and Unscented Kalman lter. The simulation results show that the accuracy of the multi-step Gaussian ltering is comparable to the particle ltering approach with much lower computational load that is important for online localization of several RF sources. Furthermore, the e ects of uncertainty on the propagation parameters have been studied to show that the robustness of the multi-step Gaussian ltering to the uncertainties is comparable to the particle lter approach.

In this work, we propose an algorithm which enables an air hockey playing robot to quickly adapt itself to new environments. There are a number of parameters in each environment which affect the puck's motion (e.g. coefficients of restitution). The robot should be able to estimate these parameters accurately enough in order to predict the position of the puck and to reach its goals. Our algorithm does this task well without need of a high speed camera and in a noisy environment. We implemented this algorithm on an air hockey simulator. It successfully tunes the parameters with the average number of 17.12 shots and the average error of 1.39% in its shot planning after the calibration process is done.

This paper presents a path planning approach of several UAVs for Radio Frequency (RF) source localization in None Line Of Sight (NLOS) propagation condition using the Received Signal Strength Indication (RSSI). The paths are planned such that the lower bound of standard deviation of localization error, which is equivalent to the Cramer-Rao Lower Bound (CRLB) of the estimation, minimized at any time. Due to the complexity of Jacobin calculation to perform global CRLB optimization, the local values of CRLBs in the current waypoint and next probable waypoints are used in the steepest decent approach to determine the best path. Furthermore, the complexity is reduced by discretizing the space for UAVs to make the computation feasible. The effect of NLOS propagation on the RSSI measurements is simulated by a log-normal distribution and the last estimation of radio source location is used to calculate the local CRLBs. The proposed approach has been simulated and compared with the basic bio-inspired approach of going toward the sensed direction of the source. The result shows better performance than the basic approach.

Imitation is different from other similar social learning techniques such as mimicking. True imitation is goal-directed and involves abstraction. This paper presents a hierarchical concept learning scheme, which uses imitation to learn new actions based on perceived observations. Presented demonstrations are composed of a spatio-temporal signal and a feature vector. In this paper a probabilistic model is provided in order to represent these hybrid demonstrations. These models have been used in an autonomous concept learning algorithm which uses effects of actions as a measure of functional similarity. Also a functional space is defined to determine concepts similarity. An algorithm for high-level concept formation in this space is provided. The proposed methods are used and evaluated in an air hockey simulation environment. Simulation results show efficiency of the proposed methods in recognition and reproduction of air hockey basic shots and their ability for hierarchical concept learning.

In this work, an adhesion system for wall climbing robots using vortex cup has been designed and numerically evaluated. A small scale model of the system has been designed and the flow patterns including pressure and velocity fields are computed using CFD analysis. The results are verified using mesh independency and validated through comparison with the available experimental data and show to have high correlation together. Furthermore, the effect of different parameters such as the number of nozzles, diffuser length, cup height and cup radius on the adhesion force has been studied. Finally, a new cup has been designed base on the optimum data obtained from the numerical results.