In this paper we deal with the problem of path length optimization for nonholonomic robots modeled as Dubins' vehicles. We present an improved solution that computes paths through two-dimensional waypoints, that are shorter than those produced by one of the most used techniques in the literature. We initially present an optimization cost function that proves to be better suited for the nonholonomic constraints of the vehicles that are the focus of this work. We also propose an improvement for the Alternating Algorithm, used to determinate the orientation angles on the calculation of the Dubins' Path through the waypoints. Finally, we use a recently developed optimization meta-heuristic, called Continuous-GRASP (C-GRASP), to generate a path that is shorter than paths obtained with classical techniques. Our results show significant improvements on the search for optimal paths for the case of nonholonomic vehicles. Our methodology was thoroughly evaluated and validated in simulation, and the results have shown a decrease on path's length of 28% on average, compared with the classic technique found in literature. In some cases a reduction of approximately 50% was obtained.

A Wireless Sensor Network consists of several sensor nodes deployed in an environment having as primary goal to collect data. However, due to limited sensor communication range, oftentimes it is necessary to use a mobile node that will visit other nodes to gather up their collected data. This work addresses the problem of planning efficient paths for data collection by a mobile node modeled as a nonholonomic vehicle with curvature constraints. We propose an efficient algorithm to identify areas of intersection among the nodes RF footprints which will guide the identification of a smaller set of waypoints through which the vehicle needs to traverse in order to collect available data. Then, a metric similar to the classical Traveling Salesman Problem is used to determine the best circuit that includes all these collecting points. In order to reduce the total path length for the mobile node, a meta-heuristic is used. The classical Dubins' path technique is employed to generate a feasible tour for the vehicle and a new heuristic is used to generate the required orientation at the collecting points. The methodology was validated in a simulated environment. Our methodology outperforms the classical Alternating Algorithm and the best performing stateof-the-art algorithm.

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In this paper we present an improved color-based planar¯ducial marker system. Our framework provides precise and robust full 3D pose estimation of markers with superior accuracy when compared with many¯ducial systems in the literature, while color information encoding enables using over 65 000 distinct markers. Unlike most color-based¯ducial frameworks, which requires prior classi¯cation training and color calibration, ours can perform reliably under illumination changes, requiring but a rough white balance adjustment. Our methodology provides good detection performance even under poor illumination conditions which typically compromise other marker identi¯cation techniques, thus avoiding the evaluation of otherwise falsely iden-ti¯ed markers. Several experiments are presented and carefully analyzed, in order to validate our system and demonstrate the signi¯cant improvement in estimation accuracy of both position and orientation over traditional techniques.

Wireless Sensor Networks (WSNs) are commonly employed for environmental and wildlife monitoring. In these scenarios, mobile robots with specialized sensing, processing and actuation abilities may be employed to investigate relevant events in place. However, driving the robot to the event place is not a trivial task in the typical case where sensor nodes do not have positioning sensors such as GPS. In this work we propose a novel navigation algorithm for the mobile robot based solely on the Received Signal Strength Indication (RSSI) of communication packets. The proposed algorithm builds on a probabilistic signal propagation model recovered from real signal decay data, unlike alternative solutions found in literature. Simulations show a superior performance in comparison to similar related work.