Generation of high-quality drive paths is a significant issue for automated wheeled vehicles. To achieve this aim for a truck and trailer vehicle, the paper proposes the use of a parameterized curve primitive, the${\mbi \eta}^4$-spline. Using this spline, generation and shaping of smooth feasible paths is made possible as well as the transfer between arbitrary dynamic configurations of the articulated vehicle. The${\mbi \eta}^4$-spline is a ninth-order polynomial curve that can interpolate given Cartesian points with associated arbitrary unit tangent vector, curvature, and first and second derivatives of curvature. It depends on a set of eight (eta) parameters that can be freely chosen to modify the path shape without changing the interpolations conditions at the path endpoints. Completeness, minimality, and symmetry of the${\mbi \eta}^4$-spline are established. An example on a parking maneuver of the articulated vehicle is presented and the pertinent optimal path planning is also discussed.

The paper considers a minimum-time feedforward motion control problem for an open container carrying a liquid. The proposed solution is a time-continuous acceleration planning that avoids liquid spilling and satisfies amplitude constraints on jerk, acceleration, and velocity of the container moving on a linear guide of an automation line. This solution is based on linear programming and can provide rest-to-rest liquid motion planning or, alternatively, a rest-to-disequilibrium planning with bounded post-motion liquid oscillations. Experimental results on a test bench prototype show the effectiveness of the presented approach.

The article consider the Cartesian trajectory tracking of wheeled mobile robots to be performed by a hybrid control scheme with feedforward inverse control and a state feedback that is only updated periodically and relies on a recursive convex replanning of the reference trajectory. This approach applied to the standard unicycle model is shown to maintain its efficacy also in presence of noise or unmodeled robot dynamics. Explicit, sufficient conditions are provided to ensure global boundedness of the tracking error. Experimental results are presented using Lego Mindstorm mobile robots.

The paper proposes a linear programming approach to the feedforward minimum-time control of flexible joints. Taking into account both input and output constraints, the optimal bang-bang control is computed by discretizing a continuous-time joint model and by solving a sequence of linear programming feasibility problems. The resulting joint motion is a smooth rest-to-rest motion without oscillations. Experimental results illustrate the proposed open-loop technique.

The paper deals with the generation of smooth paths for the inversion-based motion control of wheeled mobile robots. A new path primitive, called$ { \mmb{\eta } }^{3}$-spline, is proposed. It is a seventh order polynomial spline which permits the interpolation of an arbitrary sequence of points with associated arbitrary tangent directions, curvatures, and curvature derivatives, so that an overall$\mmb G^{3}$-path is planned. A$\mmb G^{3}$-path or path with third order geometric continuity has continuous tangent vector, curvature, and curvature derivative along the arc length. Adopting this planning scheme and a dynamic path inversion technique, the robot's command velocities are continuous with continuous accelerations. The new primitive depends on a vector ($ { \mmb{\eta } }$) of six parameters that can be used to finely shape the path. The$ { \mmb{\eta } }^{3}$-spline can generate or approximate, in a unified framework, a variety of curve primitives such as circular arcs, clothoids, spirals, etc. The paper includes theoretical results, path planning examples, and a note on general$ { \mmb{\eta } }^{k}$-splines.

A new motion-generation approach is proposed for wheeled mobile robots described by the unicycle kinematic model. This approach permits the generation of smooth continuous-acceleration controls using a dynamic path-inversion procedure that exploits the concept of G/sup 3/ -paths, i.e., Cartesian paths with third-order geometric continuity (both the curvature function and its derivative, with respect to the arc length, are continuous). The exposed steering method is well suited to be adopted for the robot's iterative steering within a supervisory control architecture for sensor-based autonomous navigation. A worked example illustrates the approach.

This paper presents a new motion planning primitive to be used for the iterative steering of vision-based autonomous vehicles. This primitive is a parameterized quintic spline, denoted as /spl eta/-spline, that allows interpolating an arbitrary sequence of points with overall second-order geometric (G/sup 2/-) continuity. Issues such as completeness, minimality, regularity, symmetry, and flexibility of these G/sup 2/-splines are addressed in the exposition. The development of the new primitive is tightly connected to the inversion control of nonholonomic car-like vehicles. The paper also exposes a supervisory strategy for iterative steering that integrates feedback vision data processing with the feedforward inversion control.

Presents the methods for sensing obstacles and vehicles implemented on the University of Parma experimental vehicle (ARGO). The ARGO project is briefly described along with its main objectives; the prototype vehicle and its functionalities are presented. The perception of the environment is performed through the processing of images acquired from the vehicle. Details about the stereo vision-based detection of generic obstacles are given, along with a measurement of the performance of the method; then a new approach for leading vehicles detection is described, relying on symmetry detection in monocular images. The paper concludes with a description of the current implementation of the control system, based on a gain scheduled controller, which allows the vehicle to follow the road or other vehicles.

We present an approach for motion planning of servosystems endowed with elastic transmission. The open-loop control strategy proposed is based on noncausal system inversion and guarantees the absence of vibration both during and at the end of the point-to-point motion. The idea is to calculate the command input of the system after having determined a suitable parametrized motion law of the load. It can be demonstrated that, with an opportune choice of the motion law, the input function has a continuous derivative of a predefined order. Moreover, the time of the motion can be optimized with the purpose to minimize it, taking into account constraints on the actuators. The whole technique is explained in the paper and simulation results are given in order to show its feasibility.

A deterministic global optimization approach is proposed for the optimal trajectory planning of D-joint industrial robots. After mapping Cartesian knots into joint knots, by inverse kinematics, they are interpolated with spline functions under constraints on joint accelerations and jerks. An interval algorithm, a procedure based on interval analysis techniques, is presented to determine an optimal piecewise trajectory which globally minimizes the total travelling time. With arbitrarily sharp precision, this procedure solves the nonlinearly constrained optimization problem which is obtained using the branch-and-bound principle where the branching is performed by uniform subdivision and the bounding via interval inclusion functions. The interval algorithm, implemented in C++ with the PROFIL/BIAS libraries, has been applied to the optimal four-spline planning of a 6-joint arm, and computational results are included.