Wheeled Robot Development for Obstruction Avoidance Mechanism

Consolidating the current improvement status of clever robots, impediment aversion and programmed following are the focal point of robot travel issues. In light of the improvement status of homegrown clever control frameworks, current innovation, and so on, this paper utilizes Arduino as the centre control framework, joined with an infrared following module. Four modules, for example, ultrasonic impediment aversion module, engine drive module and force module have planned a decent control conspire, accordingly understanding the insightful following and hindrance evasion capacity of the wheeled robot. Keywords: Arduino, Wheel robot, Tracking, Obstacle Avoidance

On MBS Constraints and Projections

Constraints in multibody systems are usually treated by a Lagrange I - method resulting in equations of motion together with the constraint forces. Going from non-minimal coordinates to minimal ones opens the possibility to project the original equations directly to the minimal ones, thus eliminating the constraint forces. The necessary procedure is described, a general example of combined machine-process dynamics discussed and a specific example given. For a n-link robot tracking a path the equations of motion are projected onto this path resulting in quadratic form linear differential equations. They define the space of allowed motion, which is generated by a polygon-system.

One-Step Predictive H2 FIR Tracking under Persistent Disturbances and Data Errors

Information loss often occurs in industrial processes under unspecified impacts and data errors. Therefore robust predictors are required to assure the performance. We design a one-step H2 optimal finite impulse response (H2-OFIR) predictor under persistent disturbances, measurement errors, and initial errors by minimizing the squared weighted Frobenius norms for each error. The H2-OFIR predictive tracker is tested by simulations assuming Gauss-Markov disturbances and data errors. It is shown that the H2-OFIR predictor has a better robustness than the Kalman and unbiased FIR predictor. An experimental verification is provided based on the moving robot tracking problem