This paper addresses the problem of numerically finding an optimal path for a robot with non-holonomic constraints. A car like robot, whose turning radius is lower bounded i s considered as an example, where the arc length and the change in steering angle are optimized. The car like robot is kinematically constrained and i s modelled as a 2 D object translating and rotating in the horizontal pl...

This project proposes an obstacle avoiding path planning algorithm based on cell decomposition method for a car-like robot. Dijkstra’s algorithm is applied in order to find the shortest path. Using cell decomposition, the free space of the robot is exactly partitioned into cells. Then, the connectivity graph is created followed by calculating the shortest path by Dijkstra’s algorithm. This proj...

This paper shows how to compute the nonholonomic distance between a point-wise car-like robot and polygonal obstacles. Geometric constructions to compute the shortest paths from a configuration (given orientation and position in the plane of the robot) to a position (i.e., a configuration with unspecified final orientation) are first presented. The geometric structure of the reachable set (set ...

Trajectory tracking is an important behaviour for mobile robots. This paper addresses the nonlinear trajectory tracking control problem for a nonholonomic car-like mobile robot with a constrained state. Compared with the existing works on the nonholonomic car-like mobile robots, the state constraint is emphasized in this work. The controllability of the system under the state constraint is chec...

This paper presents the usage of hybrid systems to develop an adaptive recent horizon controller for a nonholonomic car-like robot. The system is modeled by a non-deterministic hybrid system in which transitions represent the discrete control actions, mode invariants the constraints and the transition relation encodes sequencing requirements. The control algorithm examines at runtime the possib...

The problem investigated in this paper is that of driving a car-like robot along a race track and the use of reinforcement learning to find a good control function. The reinforcement learner uses a case-based function approximator to extend the reinforcement learning paradigm to handle continuous states. The learned controller performs similar to the best control functions in both simulation an...

This paper deals with path planning for car-like robot. 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 proole is not continuous: to follow them precisely, a vehicle must stop and reorient its directing wheels at each curvature discontinuity (transition segment{ ci...

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 disco...

In this work, we propose a new approach for motion planning for nonholonomic car-like robots which is based on a Customizable PRM (C-PRM). A major advantage of our approach is that it enables the same roadmap to be efficiently utilized for car-like robots with different turning radii, which need not be known before query time. Our C-PRM-based approach first builds a so-called control roadmap wh...