Automatic model building is a fundamental task in mobile robotics. We present a method for sensor-based exploration of unknown environments by non-holonomic mobile robots. This method proceeds by building a data structure called SRT (sensor-based random tree). The SRT represents a roadmap of the explored area with an associated safe region, and estimates the free space as perceived by the robot during the exploration. The original work presents two techniques: SRT-ball and SRT-star. In this paper, we propose an alternative strategy called SRT-radial that deals with non- holonomic constraints using two alternative planners named SRT-extensive and SRT-goal. We present experimental results to show the performance of the SRT-radial and both planners.

In this article, a recently developed approach for robot motion planning is extended and applied to non-holonomic mobile robots. This approach replace random sampling by deterministic one. We present several implementations of PRM-based planners: 1) Classical PRM with deterministic sampling and random sampling, 2) Deterministic and random Lazy-PRM, and 3) Lattice-based PRM. We have used several low-discrepancy sequences (Halton, Hammersley, Faure, and Sobol) and low-discrepancy lattices. Experimental results show that the deterministic variants of the PRM offer performance advantages in comparison to the original PRM.

This work deals with the sensor-based motion planning problem for car-like robots. Sensor-based versions of Lazy DRM and Lazy LRM are used to exploit the information obtained from sensors and to compute a feasible collision-free path. The algorithm tries to reach the goal, executing the local method in the known free region. If it succeeds, a path to the goal is found and the algorithm finishes. Otherwise, the algorithm executes more scans to extend its free space, an so on. We have performed some simulations that show the promise of our approach.

The aim of this article is to compare experimentally the use of quasi-random sampling techniques for nonholonomic path planning. The experiments are evaluated in the context of the probabilistic roadmap methods (PRM). Two quasi-random variants of PRM-based planners are proposed: (1) a classical PRM with quasi-random sampling, and (2) a quasi-random lazy-PRM. Both have been implemented for car-like robots, and are shown through experimental results to offer some performance advantages in comparison to their randomized counterparts.