Target position and avoidance margin effects on path planning in obstacle avoidance

Abstract

This study examined how people choose their path to a target, and the visual information they use for path planning. Participants avoided stepping outside an avoidance margin between a stationary obstacle and the edge of a walkway as they walked to a bookcase and picked up a target from different locations on a shelf. We provided an integrated explanation for path selection by combining avoidance margin, deviation angle, and distance to the obstacle. We found that the combination of right and left avoidance margins accounted for 26%, deviation angle accounted for 39%, and distance to the obstacle accounted for 35% of the variability in decisions about the direction taken to circumvent an obstacle on the way to a target. Gaze analysis findings showed that participants directed their gaze to minimize the uncertainty involved in successful task performance and that gaze sequence changed with obstacle location. In some cases, participants chose to circumvent the obstacle on a side for which the gaze time was shorter, and the path was longer than for the opposite side. Our results of a path selection judgment test showed that the threshold for participants abandoning their preferred side for circumventing the obstacle was a target location of 15 cm to the left of the bookcase shelf center.

Waist rotation angle as an indicator of probable human collision avoidance direction for autonomous mobile robots

“…Similarly, it has been revealed that a signal from the body part, such as gaze, becomes a cue to predict the avoidance direction [33][34][35]. For example, the gaze direction of the person who approaches head-on affects a moving trajectory [34].…”

“…Similarly, studies have revealed that a signal from a body part, such as a gaze, serves as an indicator to predict the avoidance direction (Joshi et al, 2021; Nummenmaa et al, 2009; Saeedpour-Parizi et al, 2021). For example, the gazing direction of a person who approaches head-on affects his/her trajectory (Nummenmaa et al, 2009).…”

Research on Path Planning of UAV Swarm for Decapitation Strike

“…[ , ] a a a  (7) (3) Speed constraint When performing tasks, the UAV needs to reach the target point quickly on the one hand, and needs to fly at a lower speed when avoiding the threat area accurately on the other hand. Therefore, it is necessary to specify the speed range of UAVs:…”

“…In order to ensure the UAV swarm to avoid the attack range and reach the mission area safely, flight path planning is quite necessary. The path planning of UAV swarm is a multi-objective optimization problem [7]. At present, researchers mainly use intelligent algorithms such as artificial potential field method(APF) [8] [9], A* algorithm [11]- [13], particle swarm optimization algorithm(PSO) [14] [15], wolf swarm optimization algorithm [16] and so on.…”