Mobile robotics is a very attractive research field. There are two main reasons for this. The first is the high number of applications and sectors where these machines could be introduced in the future: transport, agriculture, military, aerospace, etc. The second reason is that mobile robots are excellent test-beds to develop and integrate the results of many other research areas: control, computer vision, electronics, mechanics, artificial intelligence, etc. The third IFAC Symposium on Intelligent Autonomous Vehicles was an opportunity to present some of the latest developments in the area. Authors presented not only new methods and technologies to solve the classical problems related to intelligent autonomous vehicles, which include path planning, localization, environment modeling, path following, etc., but also new approaches to their design, such as new architectures, new navigation procedures, self-learning systems, etc. In addition, many different applications and vehicles were considered: from autonomous ocean vehicles to planetary rovers. This special issue of Robotica comprises some revised and updated selected papers of the Symposium. The variety of selected papers includes surveys, applications and design and development aspects of intelligent autonomous vehicles.

This survey paper is focused on the navigation of mobile robots. The most important associated problems and the solutions given by different researchers are discussed. Several aspects are not yet satisfactorily resolved and some promising research approaches are addressed.

In this paper a multirobot system consisting of small size ball-shaped mobile underwater robots is introduced. Robots form a cooperative society operating together for a common goal. This is made possible by inter-member communication and control architecture allowing cooperation. The test environment is a closed aquatic process containing tanks, pipes, and a jet pump. The task considered is cleaning of biologically contaminated spots in the process. Detailed hardware structure of a robot-member as well as the control architecture are introduced. Behaviour of the cooperative system is demonstrated in a test environment where contamination caused by biological algae growth is emulated by infrared panels behaving like a living biomass.

The Jet Propulsion Laboratory (JPL) is embarking on a series of exploration missions to Mars. Long-range rovers with several science instruments on board will traverse many kilometers on the surface. This paper describes some of the research underway at JPL as they pertain to navigation of these long-range rovers. A brief description is provided of the Sojourner rover which recently completed a short-range, near-lander Mars mission. A research rover called Rocky-7 under development at JPL implements a number of new technologies for long-range Mars traverses. The kinematic configuration of this vehicle is described as is tile sensor set. The paper then focuses on state estimation techniques used on-board Rocky-7. A brief review of rover-related state-estimation work is presented and an Extended Kalman Filter framework is introduced. This includes process models for translation, small-angle/quaternion based rotations, gyro bias and wheel contact as well as observation models for a gyro, sun-sensor, and accelerometer. A novel slip constraint technique is developed to allow incorporation into the filter of the highly non-linear kinematics resulting from the rover's rocker-bogey suspension mechanism and wheel-to-ground contact point variations. Simulation results are discussed to allow detailed comparisons with ground-truth data and experimental results from Rocky-7 are presented.

Draper Small Autonomous Aerial Vehicle (DSAAV), MITy and SMART micro-rovers, Companion mini-rover, and Vorticity Control Unmanned Undersea Vehicle (VCUUV) are highlighted. DSAAV demonstrated autonomy with GPS/INS integration and vision processing. The micro- and mini-rovers investigated ground based autonomy with extensive mapping and planning integration. VCUUV is a flexible-hull UUV which propels and maneuvers like a tuna.

In future real-time systems such as those required for intelligent autonomous vehicle control, we need flexibility in choosing the set of services to support under varying environmental conditions and system states. It is not feasible to make an optimal choice of services at run-time, so we propose a method of ranking the services pre-run-time, based on the ‘utility' of each service. This paper focuses on the problem of calculating a ‘value' for the utility of each service alternative. We show how to derive values systematically and rationally, using Measurement Theory and Decision Analysis. The approach relies on engineering judgement and data input by a domain expert. In the context of autonomous vehicles, we believe that such knowledge would be available, making ‘value-based scheduling' a feasible approach.

This paper describes a new vision-based playback method of a wheeled mobile robot. The path of the robot is divided into straight line parts and curved line parts. The curved line parts were approximated by a Bézier curve to decrease the volume of data. In order to track the curved line parts with good accuracy, tracking errors were detected by referring to two pre-recorded images on one part of the curve. Then, the errors were corrected by adjusting the control points of the Bézier curve. Outdoor experiments demonstrated good repeatability by using the proposed method.

Often inspection and maintenance work involve a large number of highly dangerous manual operations, especially within industrial fields such as shipbuilding and construction. This paper deals with the autonomous climbing robot which uses the “caterpillar” concept to climb in complex 3D metallic-based structures. During its motion the robot generates in real-time the path and grasp planning in order to ensure stable self-support to avoid the environment obstacles, and to optimise the robot consumption during the inspection. The control and monitoring of the robot is achieved through an advanced Graphical User Interface to allow an effective and user friendly operation of the robot. The experiments confirm its advantages in executing the inspection operations.

Machine vision-based sensing enables automatic hover stabilization of helicopters. The evaluation of image data, which is produced by a camera looking straight to the ground, results in a drift free autonomous on-board position measurement system. No additional information about the appearance of the scenery seen by the camera (e.g. landmarks) is needed. The technique being applied is a combination of the 4D-approach with two dimensional template tracking of a priori unknown features.

In this paper neural network (NN) control techniques for non-model based PD controlled robot manipulators are proposed. The main difference between the proposed technique and the existing feedback error learning (FEL) technique is that compensation of robot dynamics uncertainties is done outside the control loop by modifying the desired input trajectory. By using different NN training signals, two NN control schemes are developed. One is comparable to that in the FEL technique and another has to deal with the Jacobian of the PD controlled robot dynamic system. Performances of both controllers for various trajectories with different PD controller gains are examined and compared with that of the FEL controller. It is shown that the new control technique performed better and robust to PD controller gain variations.

Regarding assistance to disabled people for object manipulation and carrying, the paper focuses on the localisation for mobile robot autonomy. In order to respect strong low-cost constraints, the perception system of the mobile robot uses sensors of low metrological quality, ultrasonic ring and odometry. That poses new problems for localisation, in particular. Among different localisation techniques, we present only off-line localisation. With poor perception means, it is necessary to introduce a priori knowledge on sensors and environment models. To solve the localisation problem, the ultrasonic image is segmented applying the Hough transform, well-adapted to ultrasonic sensor characteristics. The segments are then matched with the room, modelled and assumed to be rectangular. Several positions are found. A first sort, based on a cost function, reduces the possibilities. The remaining ambiguities are removed by a neural network which plays the part of a classifier detecting the door in the environment. Improvements of the method are proposed to take into account obstacles and non-rectangular room. Experimental results show that the localisation operates even with one obstacle.

This paper presents a solution to the global adaptive partial state feedback control problem for rigid-link, flexible-joint (RLFJ) robots. The proposed tracking controller adapts for parametric uncertainty throughout the entire mechanical system while only requiring link and actuator position measurements. A nonlinear filter is employed to eliminate the need for link velocity measurements while a set of linear filters is utilized to eliminate the need for actuator velocity measurements. A backstepping control strategy is utilized to illustrate global asymptotic link position tracking. An output feedback controller that adapts for parametric uncertainty in the link dynamics of the robot manipulator is presented as an extension. Experimental results are provided as verification of the proposed controller.