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In this paper, we address depth perception in the peripersonal space within three virtual environments: poor environment (dark room), reduced cues environment (wireframe room), and rich cues environment (a lit textured room). Observers binocularly viewed virtual scenes through a head-mounted display and evaluated the egocentric distance to spheres using visually open-loop pointing tasks. We conducted two different experiments within all three virtual environments. The apparent size of the sphere was held constant in the first experiment and covaried with distance in the second one. The results of the first experiment revealed that observers more accurately estimated depth in the rich virtual environment compared to the visually poor and the wireframe environments. Specifically, observers' pointing errors were small in distances up to 55 cm, and increased with distance once the sphere was further than 55 cm. Individual differences were found in the second experiment. Our results suggest that the quality of virtual environments has an impact on distance estimation within reaching space. Also, manipulating the targets' size cue led to individual differences in depth judgments. Finally, our findings confirm the use of vergence as an absolute distance cue in virtual environments within the arm's reaching space.

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Safe and smooth mobile robot navigation through cluttered environment from the initial position to goal with optimal path is required to achieve intelligent autonomous ground vehicles. There are countless research contributions from researchers aiming at finding solution to autonomous mobile robot path planning problems. This paper presents an overview of nature-inspired, conventional, and hybrid path planning strategies employed by researchers over the years for mobile robot path planning problem. The main strengths and challenges of path planning methods employed by researchers were identified and discussed. Future directions for path planning research is given. The results of this paper can significantly enhance how effective path planning methods could be employed and implemented to achieve real-time intelligent autonomous ground vehicles.