Morteza Azad

Balancing control algorithm for a 3D under-actuated robot

This paper presents an angular momentum based controller to control the balancing motion of a spatial underactuated robot with three degrees of under-actuation. The control algorithm is based on the idea of decoupling the robots motion instantaneously into bending and swivelling motions. This property of the robot is obtained by using a constant velocity joint as the 2-DoF active joint of the robot. Simulation results show the performance of the controller during some interesting motions of the robot such as straightening, crouching and reorienting motions. The last two motions, which are the results of decoupling the robots motion, are demonstrated here for the first time.

Angular momentum based balance controller for an under-actuated planar robot

In this paper, a new control algorithm based on angular momentum is presented for balancing an under-actuated planar robot. The controller is able to stabilize the robot in any unstable balanced configuration in which the robot is controllable, and also it is able to follow a class of arbitrary trajectories without losing balance. Simulation results show the good performance of the controller in balancing and trajectory tracking motions of the robot. The simulations also show that the proposed controller is robust to significant imperfections in the system, such as errors in the controller’s dynamic model of the robot and imperfections in the sensors and actuators. The new controller is compared with three existing balance controllers and is shown to equal or outperform them.

Kinematics-based estimation of contact constraints using only proprioception

Robots are increasingly being required to perform tasks which involve contacts with the environment. This paper addresses the problem of estimating environmental constraints on the robots motion. We present a method which estimates such constraints, by computing the null space of a set of velocity vectors which differ from commanded velocities during contacts. We further extend this method to handle unilateral constraints, for example when the robot touches a rigid surface. Unlike previous work, our method is based on kinematics analysis, using only proprioceptive joint encoders, thus there is no need for either expensive force-torque sensors or tactile sensors at the contact points or any use of vision. We first show results of experiments with a simulated robot in a variety of situations, and we analyse the effect of various levels of observation noise on the resulting contact estimates. Finally we evaluate the performance of our method on two sets of experiments using a KUKA LWR IV manipulator, tasked with exploring and estimating the constraints caused by a horizontal surface and an inclined surface.

Model estimation and control of compliant contact normal force

This paper proposes a method to realize desired contact normal forces between humanoids and their compliant environment. By using contact models, desired contact forces are converted to desired deformations of compliant surfaces. To achieve desired forces, deformations are controlled by controlling the contact point positions. Parameters of contact models are assumed to be known or estimated using the approach described in this paper. The proposed methods for estimating the contact parameters and controlling the contact normal force are implemented on a LWR KUKA IV arm. To verify both methods, experiments are performed with the KUKA arm while its end-effector is in contact with two different soft objects.

Effects of hand contact on the stability of a planar humanoid with a momentum based controller

This paper studies the effects of hand contact force on the stability of a planar humanoid robot while translational perturbations are applied to its foot. A momentum based controller is used to control the robots motion during the perturbations. Simulation results show that the displacements of the center of pressure (CoP) of the foot decrease substantially when there is a supportive contact between the hand and the environment. The simulation results of the CoP displacements and handle forces also conform with the results of the experiments on human subjects with different positions of the hand contact. This conformity shows that the momentum based controller adequately models human behaviour in contact with the environment during a balancing motion.

The CoDyCo Project Achievements and Beyond: Toward Human Aware Whole-Body Controllers for Physical Human Robot Interaction

The success of robots in real-world environments is largely dependent on their ability to interact with both humans and said environment. The FP7 EU project CoDyCo focused on the latter of these two challenges by exploiting both rigid and compliant contacts dynamics in the robot control problem. Regarding the former, to properly manage interaction dynamics on the robot control side, an estimation of the human behaviors and intentions is necessary. In this letter, we present the building blocks of such a human-in-the-loop controller, and validate them in both simulation and on the iCub humanoid robot using a human–robot interaction scenario. In this scenario, a human assists the robot in standing up from being seated on a bench.

Uncertainty averse pushing with model predictive path integral control

Planning robust robot manipulation requires good forward models that enable robust plans to be found. This work shows how to achieve this using a forward model learned from robot data to plan push manipulations. We explore learning methods (Gaussian Process Regression, and an Ensemble of Mixture Density Networks) that give estimates of the uncertainty in their predictions. These learned models are utilised by a model predictive path integral (MPPI) controller to plan how to push the box to a goal location. The planner avoids regions of high predictive uncertainty in the forward model. This includes both inherent uncertainty in dynamics, and meta uncertainty due to limited data. Thus, pushing tasks are completed in a robust fashion with respect to estimated uncertainty in the forward model and without the need of differentiable cost functions. We demonstrate the method on a real robot, and show that learning can outperform physics simulation. Using simulation, we also show the ability to plan uncertainty averse paths.

Knowledge-based particle swarm optimization for PID controller tuning

A proportional-integral-derivative (PID) controller is a control loop feedback mechanism widely employed in industrial control systems. The parameters tuning is a sticking point, having a great effect on the control performance of a PID system. There is no perfect rule for designing controllers, and finding an initial good guess for the parameters of a well-performing controller is difficult. In this paper, we develop a knowledge-based particle swarm optimization by incorporating the dynamic response information of PID into the optimizer. Prior knowledge not only empowers the particle swarm optimization algorithm to quickly identify the promising regions, but also helps the proposed algorithm to increase the solution precision in the limited running time. To benchmark the performance of the proposed algorithm, an electric pump drive and an automatic voltage regulator system are selected from industrial applications. The simulation results indicate that the proposed algorithm with a newly proposed performance index has a significant performance on both test cases and outperforms other algorithms in terms of overshoot, steady state error, and settling time.

Balance control strategy for legged robots with compliant contacts

This paper proposes a momentum-based balancing controller for robots which have non-rigid contacts with their environments. This controller regulates both linear momentum and angular momentum about the center of mass of the robot by controlling the contact forces. Compliant contact models are used to determine the contact forces at the contact points. Simulation results show the performance of the controller on a four-link planar robot standing on various compliant surfaces while unknown external forces in different directions are acting on the center of mass of the robot.