This paper deals with vehicle platooning, where a convoy aims at following closely and safely its leader's path without collision nor lateral deviation. In this paper, we propose a platooning algorithm based on a near-to-near decentralized approach. Each vehicle estimates and memorizes on-line the path of its predecessor as a set of points. After choosing a suitable position to aim for, the follower estimates on-line the predecessor's path curvature around the selected target. Then, based on a heuristic search, it computes an angular velocity using the estimated curvature. The optimization criteria used in this work allows the robot to follow its predecessor's path without oscillation while reducing the lateral and angular errors.

This paper considers the platooning problem: we aim to steer a train of vehicles along an unknown path generated by the first vehicle, which is human driven. Among existing approaches, we study a decentralised local approach to avoid robustness issues due to communication failure which are common to centralised approaches. However, decentralised control rises up the problem of lateral deviation which is accumulated along the platoon.

This paper deals with the platooning problem that can be defined as the automatic following of a manned driven vehicle by a convoy of automatic ones. Different approaches have been proposed so far. Some require the localisation of each vehicle and a communication infrastructure, others called near-to-near approach only needs vehicle on-board sensors. However, to our knowledge, they do not provide any proof of non collision. We propose a novel near-to-near longitudinal platooning building a collision-free platooning whatever the number of vehicles. The model is derived from the study of the most dangerous interaction between two vehicles, i.e. considering the maximum acceptable acceleration when the previous vehicles brakes at maximum capacity. Collision avoidance of this model is proved. Finally, we show that this model can be combined to existing ones, keeping this collision-free property while allowing more various behaviors.

This paper presents Continuous Curvature (CC) Steer, a steering method for car-like vehicles, i.e., an algorithm planning paths in the absence of obstacles. CC Steer is the first to compute paths with: 1) continuous curvature; 2) upper-bounded curvature; and 3) upper-bounded curvature derivative. CC Steer also verifies a topological property that ensures that when it is used within a general motion-planning scheme, it yields a complete collision-free path planner. The coupling of CC Steer with a general planning scheme yields a path planner that computes collision-free paths verifying the properties mentioned above. Accordingly, a car-like vehicle can follow such paths without ever having to stop in order to reorient its front wheels. Besides, such paths can be followed with a nominal speed which is proportional to the curvature derivative limit. The paths computed by CC Steer are made up of line segments, circular arcs, and clothoid arcs. They are not optimal in length. However, it is shown that they converge toward the optimal "Reeds and Shepp" paths when the curvature derivative upper bound tends to infinity. The capabilities of CC Steer to serve as an efficient steering method within two general planning schemes are also demonstrated.

The paper presents an approach to detect and localize contact between deformable polyhedra, which can be convex or concave depending on the time step. Usual contact detection algorithms, defined for convex polyhedra, cannot be used efficiently, as they would imply completing the convex decomposition of the considered polyhedra at each time step, as it can change due to the deformability of these polyhedra. As the computation of this convex decomposition is very expensive (in complexity and computation time), we propose an algorithm to detect and localize the contact in linear time w.r.t. the number of vertices. This algorithm returns the direction of this contact and the value of the maximum intersection distance between the convex hulls of the two considered polyhedra. Experimental results taken from a dynamic simulation application are presented with their computation time, to complete the complexity analysis.

Deals with trajectory planning for non-holonomic mobile robots. We call trajectory the association of a path, which is the geometrical restriction of the motion, and of a time schedule given by a time table, a velocity or an acceleration profile. The robots considered are non-holonomic, as their motion's direction is limited by the robot's position. However, two types of robot are considered: car-like vehicles have a lower bounded turning radius, while manoeuvrable robots do not. The article shows that optimal paths for car-like vehicles correspond to optimal trajectories for manoeuvrable robots, and presents how these paths can thus be followed by manoeuvrable robots faster than paths made of line segments, along which these robots have to stop at each direction's discontinuity. Experimental results obtained using such paths for manoeuvrable robots are then provided.

Deals with path planning for car-like robots. Usual planners compute paths made of circular arcs tangentially connected by line segments, as these paths are locally optimal. The drawback of these paths is that their curvature profile is not continuous: to follow them precisely, a vehicle must stop and reorient its directing wheels at each curvature discontinuity (transition segment-circle). To remove this limitation, a new path planning problem is proposed: two curvature constraints are added to the classical kinematic constraints taken into account. Thus, the curvature must remain continuous, and its derivative is bounded (as the car-like robot can reorient its directing wheels with a limited speed only). For this problem, the existence of solutions and the characterization of those of optimal length are shown. A method solving the forward-only problem (i.e. the problem for a car moving only forward) is then presented, and this method is compared to the classical one w.r.t. the complexity and computation time, the length of the generated paths and the quality of the tracking.

This paper presents a set of paths, called bi-elementary paths. These paths are smooth and feasible for a car-like robot (i.e. their tangent direction is continuous and they respect a minimum turning radius constraint), and they can be followed by a real vehicle without stopping (i.e. they have a continuous curvature profile)-which is not the case of Dubins' curves. These paths are composed of arcs of a clothoid (a clothoid is a curve whose curvature is a linear function of its arc length), and are used to define a simplified, i.e. non-complete, planner. This simplified planner is, in turn, used in two global planning schemes, namely the Ariadne's Clew algorithm and probabilistic path planning. This paper proves an important property of the bi-elementary paths, from which the completeness of the two global planners is deduced.

We consider path planning for a car-like vehicle. Previous solutions to this problem computed paths made up of circular arcs connected by tangential line segments. Such paths have a discontinuous curvature profile. Accordingly a vehicle following such a path has to stop at each curvature discontinuity in order to re-orientate its front wheels. To remove this limitation, we add a continuous-curvature constraint to the problem at hand. In addition, we introduce a constraint on the curvature derivative, so as to reflect the fact that a car-like vehicle can only re-orientate its front wheels with a finite velocity. We propose an efficient solution to the problem at hand that relies upon the definition of a set of paths with continuous curvature and maximum curvature derivative. These paths contain at most eight pieces, each piece being either a line segment, a circular arc of maximum curvature, or a clothoid arc. They are called simple continuous curvature paths. They are used to design a local path planner. The experimental results are presented.

This paper presents a continuous-curvature path planner (CCPP) for a car-like robot. Previous collision-free path planners for car-like robots compute paths made up of straight segments connected with tangential circular arcs. The curvature of this type of path is discontinuous so much so that if a car-like robot were to actually follow such a path, it would have to stop at each curvature discontinuity so as to reorient its front wheels. CCPP is one of the first planner to compute collision-free paths with continuous curvature profiles. These paths are made up of clothoid arcs, i.e. curves whose curvature is a linear function of their arc length. CCPP uses a general planning technique called the Ariadne's Clew algorithm. It is based upon two complementary functions: SEARCH and EXPLORE. EXPLORE builds an approximation of the region of the configuration space reachable from a start configuration by incrementally placing a set of reachable landmarks in the configuration space. SEARCH checks the existence of a solution path between a landmark newly placed and the goal configuration.

This paper addresses motion planning for a car-like robot moving in a changing planar workspace, i.e. with moving obstacles. First, this motion planning problem is formulated in the state-time space framework. The state-time space of a robot is its state space plus the time dimension. In this framework part of the constraints at hand are translated into static forbidden regions of state-time space, and a trajectory maps to a state-time curve which must respect the remaining constraints. Then an approximate solution to the problem is presented.>