Feedrate planning for machining with industrial six-axis robots

The authors want to thank Stäubli for providing the necessary information of the controller, Dynalog for its contribution to the experimental validations and X. Helle for its material contributions.Nowadays, the adaptation of industrial robots to carry out high-speed machining operations is strongly required by the manufacturing industry. This new technology machining process demands the improvement of the overall performances of robots to achieve an accuracy level close to that realized by machine-tools. This paper presents a method of trajectory planning adapted for continuous machining by robot. The methodology used is based on a parametric interpolation of the geometry in the operational space. FIR filters properties are exploited to generate the tool feedrate with limited jerk. This planning method is validated experimentally on an industrial robot

Adaptive driver modelling in ADAS to improve user acceptance: A study using naturalistic data

Accurate understanding of driver behaviour is crucial for future Advanced Driver Assistance Systems (ADAS) and autonomous driving. For user acceptance it is important that ADAS respect individual driving styles and adapt accordingly. Using data collected during a naturalistic driving study carried out at the University of Southampton, we assess existing models of driver acceleration and speed choice during car following and when cornering. We observe that existing models of driver behaviour that specify a preferred inter-vehicle spacing in car-following situations appear to be too prescriptive, with a wide range of acceptable spacings visible in the naturalistic data. Bounds on lateral acceleration during cornering from the literature are visible in the data, but appear to be influenced by the minimum cornering radii specified in design codes for UK roadway geometry. This analysis of existing driver models is used to suggest a small set of parameters that are sufficient to characterise driver behaviour in car-following and curve driving, which may be estimated in real-time by an ADAS to adapt to changing driver behaviour. Finally, we discuss applications to adaptive ADAS with the objectives of improving road safety and promoting eco-driving, and suggest directions for future researc

Mixed kinematic and dynamic sideslip angle observer for accurate control of fast off-road mobile robots

Automation in outdoor applications (farming, surveillance, military activities, etc.) requires highly accurate control of mobile robots, at high speed, although they are moving on low-grip terrain. To meet such expectations, advanced control laws accounting for natural ground specificities (mainly sliding effects) must be derived. In previous work, adaptive and predictive control algorithms, based on an extended kinematic representation, have been proposed. Satisfactory experimental results have been reported (accurate to within ±10 cm, whatever the grip conditions), but at limited velocity (below 3 m·s-1). Nevertheless, simulations reveal that control accuracy is decreased when vehicle speed is increased (up to 10 m·s-1). In particular, oscillations are observed at curvature transition. This drawback is due to delays in sideslip angle estimation, unavoidable at high speed because only an extended kinematic representation was used. In this paper, a mixed backstepping kinematic and dynamic observer is designed to improve observation of these variables: the slow-varying data are still estimated from a kinematic representation, which is then injected into a dynamic observer to supply reactive and reliable sliding variable (namely sideslip angle) estimation, without increasing the noise level. The algorithm is evaluated via advanced simulations (coupling Adams and MatLab software) investigating high-speed capabilities. Actual experiments at lower speed (experimental platform maximum velocity) demonstrate the benefits of the proposed approach

High-speed autonomous navigation system for heavy vehicles

This paper presents techniques for GPS based autonomous navigation of heavy vehicles at high speed. The control system has two main functions: vehicle position estimation and generation of the steering commands for the vehicle to follow a given path autonomously. Position estimation is based on fusion of measurements from a carrier-phase differential GPS system and odometric sensors using fuzzy logic. A Takagi-Sugeno fuzzy controller is used for steering commands generation, to cope with different road geometry and vehicle velocity. The presented system has been implemented in a 13 tons truck, and fully tested in very demanding conditions, i.e. high velocity and large curvature variations in paved and unpaved roads

Force-Field Compensation in a Manual Tracking Task

This study addresses force/movement control in a dynamic “hybrid” task: the master sub-task is continuous manual tracking of a target moving along an eight-shaped Lissajous figure, with the tracking error as the primary performance index; the slave sub-task is compensation of a disturbing curl viscous field, compatibly with the primary performance index. The two sub-tasks are correlated because the lateral force the subject must exert on the eight-shape must be proportional to the longitudinal movement speed in order to perform a good tracking. The results confirm that visuo-manual tracking is characterized by an intermittent control mechanism, in agreement with previous work; the novel finding is that the overall control patterns are not altered by the presence of a large deviating force field, if compared with the undisturbed condition. It is also found that the control of interaction-forces is achieved by a combination of arm stiffness properties and direct force control, as suggested by the systematic lateral deviation of the trajectories from the nominal path and the comparison between perturbed trials and catch trials. The coordination of the two sub-tasks is quickly learnt after the activation of the deviating force field and is achieved by a combination of force and the stiffness components (about 80% vs. 20%), which is a function of the implicit accuracy of the tracking task