Chasin’ choppers: using unpredictable trajectories to test theories of object interception

Abstract

Three theories of the informational basis for object interception strategies were tested in an experiment where participants pursued toy helicopters. Helicopters were used as targets because their unpredictable trajectories have different effects on the optical variables that have been proposed as the basis of object interception, providing a basis for determining the variables that best explain this behavior. Participants pursued helicopters while the positions of both pursuer and helicopter were continuously monitored. Using models to predict the observed optical trajectories of the helicopter and ground positions of the pursuer, optical acceleration was eliminated as a basis of object interception. A model based on control of optical velocity (COV) provided the best match to pursuer ground movements, while one based on segments of linear optical trajectories (SLOT) provided the best match to the observed optical trajectories. We describe suggestions for further research to distinguish the COV and SLOT models.

Optical angular constancy is maintained as a navigational control strategy when pursuing robots moving along complex pathways

“…Results support that the strategy of maintaining a LOT relative to the background best matches the fielder behavior. This is consistent with past findings of fielders intercepting ballistic airborne baseballs (Aboufadel, 1996;Marken, 2001;McBeath et al, 1995a;Shaffer & McBeath, 2002;Wang et al, 2015), more complex moving airborne targets such as Frisbees and toy helicopters (Shaffer et al, 2004;Shaffer et al, 2008;Shaffer, Marken et al, 2013), as well as ballistic ground balls (Sugar, McBeath, & Wang, 2006), but in the case of groundbased trajectories, fielders maintain a flipped version of the same optical angular control heuristics. Thus, despite the vast differences in forces applied on airborne versus ground-based targets and resultant differences in target behavior, in both cases fielders selected running paths that maintained constancy of optical speed and angle of the target.…”

“…So a fundamental advantage of the real-world test used in the current study is that it allows participants a natural, full-range, ecologically valid environment. Other research findings examining navigational strategy in real-world environments with somewhat complex moving targets confirmed findings consistent with usage of the LOT control mechanism (Shaffer, Dolgov, Maynor, & Reed, 2013;Shaffer, Marken et al, 2013).…”

“…For balls headed within the sagittal plane, directly toward the fielder, the lateral optical movement is eliminated and the optical trajectory converges and simplifies down to just vertical optical constancy specified by OAC. The LOT control strategy has also been confirmed with mobile robots intercepting balls (Sugar & McBeath, 2001;Sugar, McBeath, Suluh et al, 2006), with moving backgrounds (Wang, McBeath, & Sugar, 2015), and a segmented version of the LOT has been found with dogs catching Frisbees that change in direction (Shaffer, Kruchunas, Eddy & McBeath, 2004), and with human fielders chasing randomly moving toy helicopters (Shaffer, Marken et al, 2013).…”

“…Past research exploring the navigational strategy used to intercept a projectile like a baseball or football has established that this task can be accomplished using simple optical navigational control heuristics (Babler & Dannemiller, 1993;Chapman, 1968;Fajen, 2013;Marken, 2014;McBeath, Nathan, Bahill, & Baldwin, 2008;McBeath, Shaffer, & Kaiser, 1995a;Michaels & Oudejans, 1992;Shaffer, McBeath, Kruchunas, & Sugar, 2008;Shaffer, Marken, Dolgov, & Maynor, 2013). This research examines whether the same navigational heuristics apply under vastly different physical conditions when attempting to intercept targets moving along complex, evasive, ground-based pathways in a real-world environment.…”

Implementation of a Perceptual Controller for an Inverted Pendulum Robot

“…To date, empirical support for PCT has come largely from constructing and testing computational models of human performance against behavioural data. These models have shown close fits across a range of experimental contexts [10,11]. There have also been a number of examples of successful applications of PCT to robotics, including an real-world autonomous robotic rover [12] control of a robotic arm [13] and a hexapod simulation [14].…”