Abstract-This paper presents a sonar-based localization approach for an autonomous underwater vehicle, in structured and unstructured environments. The system is based on a particle filter approach to represent the vehicle state and it uses a mechanically scanned profiling sonar, acquiring range profiles. A modification to the standard particle filter algorithm is proposed, in order to explore the state space in a more effective way and to reduce computational complexity. The proposed system was validated both in simulation and in trials involving a real vehicle, showing a high robustness and real-time capabilities.

International audienceA nonlinear inertial-aided image-based visual servo control approach for the stabilisation of (almost) fully-actuated autonomous underwater vehicles (AUVs) is proposed. It makes use of the homography matrix between two images of a planar scene as feedback information while the system dynamics are exploited in a cascade manner in control design: an outer-loop control defines a reference setpoint based on the homography matrix and an inner-loop control ensures the stabilisation of the setpoint by assigning the thrust and torque controls. Unlike conventional solutions that only consider the system kinematics, the proposed control scheme is novel in considering the full system dynamics (incorporating all degrees of freedom, nonlinearities and couplings as well as interactions with the surrounding fluid) and in not requiring information of the relative depth and normal vector of the observed scene. Augmented with integral corrections , the proposed controller is robust with respect to model uncertainties and disturbances. The almost global asymptotic stability of the closed-loop system is demonstrated, which is the largest domain of attraction one can achieve by means of continuous feedback control. Simulation results illustrating these properties on a realistic AUV model subjected to a sea current are presented and finally experimental results on a real AUV are reported

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A nonlinear image-based visual servo control approach for pipeline following of fully-actuated Autonomous Underwater Vehicles (AUV) is proposed. It makes use of the binormalized Plücker coordinates of the pipeline borders detected in the image plane as feedback information while the system dynamics are exploited in a cascade manner in the control design. Unlike conventional solutions that consider only the system kinematics, the proposed control scheme accounts for the full system dynamics in order to obtain an enlarged provable stability domain. Control robustness with respect to model uncertainties and external disturbances is reinforced using integral corrections. Robustness and efficiency of the proposed approach are illustrated via both realistic simulations and experimental results on a real AUV.

Localisation, i.e. estimation of one’s position in a given environment is a crucial element of many mobile systems, manned and unmanned. Due to the high demand for autonomous exploration, patrolling and inspection services and a rapid improvement of batteries, sensors and machine learning algorithms, the quality of localisation becomes even more important for smart robotic systems. The underwater domain is a very challenging environment due to the water blocking most of the signals over short distances. Recent results in localisation techniques for underwater vehicles are summarised in two principal categories: passive techniques, which strive to provide the best estimation of the vehicle’s position (global or local) given the past and current information from sensors, and active techniques, which additionally produce guidance output that is expected to minimise the uncertainty of estimated position.