Dan S. Necsulescu

Extended Kalman filter-based sensor fusion for operational space control of a robot arm

Accurate measurements of positions, velocities, and accelerations in both joint and operational space are required for achieving accurate operational space motion control of robots. Servomotors used for joint actuation are normally equipped with position sensors and optionally with velocity sensors for interlink motion measurements. Further improvements in measurement accuracy can be obtained by equipping the robot arm with accelerometers for absolute acceleration measurement. In this paper, an extended Kalman filter is used for multisensor fusion. The real-time control algorithm was previously based on the assumption of a jerk represented as a processed white noise with the zero mean. In reality, the accelerations are varying in time during the arm motion, and the zero mean assumption is not valid, particularly during fast accelerating periods. In this paper, a model predictive control approach is used for predetermining next-time-step jerk such that the remaining term can be modeled as Gaussian white noise. Experimental results illustrate the effectiveness of the proposed sensor fusion approach.

Experimental study of the dynamic based feedback linearization of an autonomous wheeled ground vehicle

Abstract In this paper a combined open/closed-loop method for point stabilization of autonomous wheeled vehicles using feedback linearization technique is presented. For point stabilization of the vehicle in planar motion (the vehicle has 2 degrees of freedom), both position (in two directions) and orientation must be stabilized (approach their desired values). Here, we propose to apply feedback linearization and exponentially stabilize the position of the center of mass of the vehicle in curvilinear coordinates (closed-loop part) and the vehicle’s orientation along a path that connects the initial and final positions with the corresponding desired orientations (open-loop part with bounded error). The vehicle used for illustration in this paper has one front (steering and driving) and two rear (idle) wheels and also a computer, two dc motors, two batteries and two measurement systems is an example of an autonomous ground vehicle. The dynamic model of this vehicle is presented in the state-space form with steering and driving torques as inputs. The results of the simulation and the experimental study of the proposed controller on this three-wheel vehicle as a prototype show that when the arc length coordinate of the position of the center of mass of the vehicle reaches its desired value, the vehicle stops at the desired position with the desired orientation with small acceptable errors proving the validity of the proposed method.

Study of the internal dynamics of an autonomous mobile robot

Abstract The requirement of ideal rolling without sideways slipping for wheels imposes nonholonomic (non-integrable) constraints on the motion of the wheels and consequently on the motion of wheeled mobile robots. From the control point of view, the dynamics of nonholonomic systems can be divided in two parts: external and internal dynamics. The dimension of the external dynamics of nonholonomic systems depends on the number of inputs to the system and the dimension of the internal dynamics depends on the number of independent nonholonomic constraints. For different motion control problems of nonholonomic systems, a smooth (model based) state feedback control law only deals with the system external dynamics; therefore, the system internal dynamics must be examined separately and its stability has to be analyzed and proved. In this paper, the internal dynamics of a three-wheel mobile robot with front wheel steering and driving is investigated. In particular, its internal dynamics stability is analyzed for two different situations, when the mobile robot is moving and when it is stationary.

Navigation of an autonomous mobile robot using EKF-SLAM and FastSLAM

This paper presents a navigation of an autonomous robot using simultaneous localization and mapping (SLAM) in outdoor environments. SLAM is a method in which localization and mapping are done simultaneously in an unknown environment without an access to a priori map. This paper introduces a probabilistic approach to a SLAM problem under Gaussian and non-Gaussian conditions and offers alternative solutions. First, an extended Kalman filter algorithm for the SLAM problem under Gaussian condition will be shown. Also, an alternative way of dealing with SLAM problem with assumption of non-Gaussian and called FastSLAM will be analyzed. FastSLAM is an algorithm that using Rao-Blackwellised method for particle filtering, estimates the path of robot while the landmarks positions which are mutually independent and with no correlation, can be estimated by EKF. This is done using a factorization that fits very well into SLAM problem based on a Bayesian network. In this paper, a real outdoor autonomous robot is presented and several experiments have been performed based on both methods. The experimental results are discussed and compared.

Haptic force control based on impedance/admittance control aided by visual feedback

This paper demonstrates a haptic device for interaction with a virtual environment. The force control is added by visual feedback that makes the system more responsive and accurate. There are two popular control methods widely used in haptic controller design. First, is impedance control when user motion input is measured, and then, the reaction force is fed back to the operator. The alternative method is admittance control, when forces exerted by user are measured and motion is fed back to the user. Both, impedance and admittance control are also basic ways for interacting with a virtual environment. In this paper, several experiments were performed to evaluate the suitability of force-impedance control for haptic interface development. The difference between conventional application of impedance control in robot motion control and its application in haptic interface development is investigated. Open loop impedance control methodology is implemented for static case and a general-purpose robot under open loop impedance control was developed as a haptic device, while a closed loop model based impedance control was used for haptic controller design in both static and dynamic case. The factors that could affect to the performance of a haptic interface are also investigated experimentally using parametric studies. Experimental results for 1 DOF rotational motion and 2 DOF planar translational motion systems are presented. The results show that the impedance control aided by visual feedback broaden the applicability of the haptic device and makes the system more responsive and accurate.

Autonomous navigation among large number of nearby landmarks using FastSLAM and EKF-SLAM - A comparative study

This paper compares two commonly used algorithms to solve Simultaneous Localization and Mapping (SLAM) problem in order to safely navigate an outdoor autonomous robot in an unknown location and without any access to a priori map. EKF-SLAM is considered as a classical method to solve SLAM problem. This method, however, suffers from two major issues; the quadratic computational complexity and single hypothesis data association. Large number of landmarks in the environment, especially, nearby landmarks, causes extensive error accumulation when the robot is traveling along a desired path. The multi-hypothesis data association property and the linear computational complexity are essential features in FastSLAM method. Those features make this method an alternative to overcome mentioned issues. The FastSLAM algorithm uses Rao-Blackwellised particle filtering to estimate the path of the robot and EKF-SLAM method to estimate locations of landmarks. In case of FastSLAM applications, however, observation noise needs to be reconsidered if the motion measurements are noisy while the range sensor is noiseless. This study suggests optimization of a specific situation of FastSLAM algorithm in case of noise discrepancy.

Nonlinear Control for UAV Formation Flying

Abstract Unmanned Aerial Vehicles (UAVs) became a technology that have attracted considerable interest in the commercial markets for the military and civilian uses, such as surveillance and reconnaissance, aerial surveys for natural sources, traffic monitoring, early forest fire detection etc. This paper deals with Nonlinear and Model Predictive Control (MPC) of Unmanned Aerial Vehicles (UAV) flying in formation. Although, UAVs present numerous advantages over the manned aircrafts, they face challenges in various aspects of control in autonomous mode, and even more, in formation flying. An advanced control system is demonstrated as a possible solution to improve and increase the level of the autonomous mode and flying capabilities of UAVs. This paper deals with advanced control system of Unmanned Aerial Vehicles (UAV) flying in formation.

Autonomous mobile robot model predictive control

This paper presents model predictive control of an autonomous vehicle. Simulation and experimental results have been shown and compared with input–output linearization method. The results obtained show that the MPC is an efficient method that allows for accurate control and navigation of an autonomous vehicle. Model based predictive control is tested in simulations for motion on an inclined plane. This is done to prepare future work regarding the avoidance of the violation of the smoothness condition for exact linearization, while at the same time by modifying the input commands the geometric path planning results are conserved. The approach is presented for the wheel-ground slippage and tip-over avoidance of the three-wheeled vehicle for inclined plane motion. A complete three-dimensional dynamic model using Newtonian dynamics is also presented. Simulation results using a three-wheeled vehicle built in our laboratory illustrate the performance of the proposed controller.

Low-speed motion control of a mechanical system

Position control of a mechanical system at low speed in the presence of dry friction leads to a slip-stick process and steady-state errors when state feedback is used. In this article, the power transfer between servomotors and the system is analyzed using a Hamiltonian formulation. The analysis shows that power dissipation at low speed cannot be achieved efficiently using a proportional derivative error feedback, with constant gains. A sliding mode approach is proposed and evaluated for achieving accurate positioning of a servomotor in the presence of significant dry friction. Simulation results show the performance of a constant-gain sliding mode controller and of a self-adjusting sliding mode controller.

Control of nonholonomic autonomous vehicles and their formations

This paper analyzes several control schemes for nonholonomic vehicles (mobile robots with differential rear wheels) in stabilization and leader — follower manoeuvres. Extensive simulation studies have been conducted to evaluate the performance of existing control schemes and to test new control schemes suggested in this paper. The results for nonholomic vehicles show that polar coordinates control is effective for a variety of initial conditions of the follower, both in stabilization and in tracking.