Mobile robots and vehicles with active articulated elements are well suited to drive on irregular and rough terrain because they are able to adjust their center of mass and decrease the risk of tip over or other accidents. The articulated mechanism, however, is usually constrained by the actuator's bandwidth, making it difficult for the vehicle to compensate for abrupt changes in terrain profile. In this paper we propose a terrain model-based control method to improve stability when traversing terrains with varying slopes, depressions, and rises. A model predictive control approach takes into account actuator bandwidth to anticipate interactions with the terrain and control the articulated elements to prevent tipping over. The method is applied to an autonomous orchard platform where workers stand while conducting production tasks on the trees, adjusting the platform height and increasing its stability. The feasibility is illustrated via numerical simulations performed with the MD Adams/Car software and Matlab, with both artificial and natural terrain data. Challenging yet realistic terrain profiles are considered to demonstrate the control effectiveness in preventing the platform from tipping over and therefore improving the workers' and the vehicle's safety.

This paper considers the active control problem of reconfigurable mobile robots on irregular terrain. A kinematic control strategy to improve robot mobility (stability and traction) is proposed. The proposed control is validated through numerical simulations and experimental tests using an amphibious wheel-legged robot, named Environmental Hybrid Robot, recently developed by Petrobras S.A. (Brazilian Petroleum Company) for environmental monitoring in the Amazon rain forest.

This work considers a hybrid force and vision control system for robotic manipulators using a force sensor and a fixed uncalibrated camera. A method is proposed to combine direct force control and adaptive visual servoing to perform tasks on unknown smooth surfaces, in the presence of uncertainties in the camera-robot system parameters. The considered task involves the visual tracking of a moving target, while the end-effector tip exerts a controlled contact force on the surface. Simulation results are presented to illustrate the performance of the proposed scheme

In this work, a new 2D adaptive visual positioning/tracking controller is considered. The proposed scheme is developed for image-based eye-to-hand systems to perform position and tracking tasks on the 3D environment, when both camera calibration and robot parameters are uncertain. The symmetric-diagonal-upper (SDU) factorization method is adopted to solve the adaptive multivariable control problem. The positioning/tracking controller is first developed for the Cartesian robot case, and then extended to the general robotic system. Simulation results are also presented for the proposed strategy.

A control-theoretical solution is presented for the direct adaptive visual control of planar manipulators using a fixed camera, when both camera calibration and robot dynamics are uncertain. The proposed scheme is developed for image-based look-and-move visual systems to allow tracking of a moving target. In order to solve the multivariable parameter adaptive problem, the proposed immersion and invariance (I&I) method is used. The scheme is then combined with the adaptive controller for the manipulator, taking into account its nonlinear dynamics and leading to an overall stable adaptive visual system. Simulations and experimental results are also presented for the proposed strategy.

This paper considers the localization and navigation of a mobile robot. The control strategy is based on a nonlinear model predictive control technique that utilizes the Newton method. The robot localization is obtained using information from odometric and ultrasonic sensors through a Kalman filter. Simulation and experimental results illustrate the efficacy of the proposed method.

We present two globally convergent vision-based position controllers for a planar two-links manipulator in the so-called fixed-camera configuration, where the camera orientation and scale factor are considered unknown. This is a basic adaptive visual servoing problem whose solution was hampered by the nonlinear dependence of the system dynamics on the unknown parameters. The controller design techniques of immersion and invariance and nonlinear proportional integral (PI) are used to derive the smooth adaptive schemes that ensure global asymptotic regulation without overparameterization, projections, or persistency of excitation assumptions. In the case of tracking, we establish error bounds that are reduced, eventually to zero, as the speed of the reference trajectory decreases, and with improved prior knowledge on the camera scale factor, for the immersion and invariance controller, or increasing a tuning gain for the nonlinear PI. The efficacy of the approaches is shown through simulations.

We address, and solve, a basic adaptive visual servoing problem whose solution was hampered by the nonlinear dependence of the system dynamics on the unknown parameters. Namely, we present a globally convergent vision-based position controller for a planar two-links manipulator in the so-called fixed-camera configuration, where the camera orientation and scale factor are considered unknown. The controller design technique of immersion and invariance is used to derive a smooth adaptive scheme that ensures global asymptotic regulation without overparametrization, projections or persistency of excitation assumptions. In the case of tracking, we establish error bounds that depend on the speed of the reference trajectory, and can be reduced (eventually to zero) with improved prior knowledge on the camera scale factor. The efficacy of the approach is shown through simulations.

Describes the development and implementation of a dynamic positioning system for remotely operated underwater vehicles (ROVs) which is based on a mechanical passive arm (PA) for position measurement. The system, named DPSROV, is suited for tasks which require precise positioning within a limited range around a working site. The DPSROV was configured aiming at easy installation in most commercially available ROVs. The effectiveness of the DPSROV was tested on real ROVs. Two different control strategies were adopted: a conventional linear controller and a robust variable structure model-reference adaptive control. Experimental results are presented to illustrate the DPSROV performance.