Human vs. Algorithmic Path Planning for Search and Rescue by Robot Teams

Substantial automation will be needed to allow operators to control the large teams of robots envisioned for search and rescue, perimeter patrol, and a wide variety of military tasks. Both analysis and research point to navigation and path planning as prime candidates for automation. When operators are isolated from robot navigation, however, there may be loss of situation awareness (SA) and difficulties in monitoring robots for failures or abnormal behavior. Operator's navigational strategies are quite complex and extremely changeable at foraging tasks in unknown environment reflecting background knowledge and expectations about human and natural environments. These considerations are missing from automated path planning algorithms leading to differences in search patterns and exploration biases between human and automatically generated paths. Effectively integrating automated path planning into multirobot systems would require demonstrating that: 1-automated path planning performs as well as humans on measures such as area coverage and 2- use of automated path planning does not degrade performance of related human tasks such as finding and marking victims. In this paper we seek to compare the divergence between human manual control and autonomous path planning at an urban search and rescue (USAR) task using fractal analysis to characterize the paths generated by the two methods. Area coverage and human contributions to mixed-initiative planning are compared with fully automated path planning. Finally, the impact of automated planning on related victim identification and marking tasks is compared for automated paths and paths generated by previous participants.

[1]  C. Michael Lewis,et al.  Modeling Individual Differences at a Process Control Task , 1990 .

[2]  Vilis O Nams,et al.  Improving Accuracy and Precision in Estimating Fractal Dimension of Animal movement paths , 2006, Acta biotheoretica.

[3]  G. Swaminathan.  Robot Motion Planning , 2006 .

[4]  Andrew B. Kahng,et al.  Cooperative Mobile Robotics: Antecedents and Directions , 1997, Auton. Robots.

[5]  Jean Scholtz,et al.  Common metrics for human-robot interaction , 2006, HRI '06.

[6]  Patrick W. Colgan,et al.  Individual variation in learning by foraging juvenile bluegill sunfish ( Lepomis macrochirus ). , 1988 .

[7]  Michael A. Goodrich,et al.  Validating human-robot interaction schemes in multitasking environments , 2005, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[8]  Bruce T. Milne,et al.  Applications of Fractal Geometry in Wildlife Biology , 1997 .

[9]  Simon Benhamou,et al.  An olfactory orientation model for mammals' movements in their home ranges , 1989 .

[10]  B. Mandelbrot.  How Long Is the Coast of Britain? Statistical Self-Similarity and Fractional Dimension , 1967, Science.

[11]  Sebastian Thrun,et al.  Perspectives on standardization in mobile robot programming: the Carnegie Mellon Navigation (CARMEN) Toolkit , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).

[12]  Prasanna Velagapudi,et al.  Human teams for large scale multirobot control , 2009, 2009 IEEE International Conference on Systems, Man and Cybernetics.

[13]  John A. Bissonette,et al.  Wildlife and Landscape Ecology: Effects Of Pattern And Scale , 2012 .

[14]  Arnoud Visser,et al.  Towards heterogeneous robot teams for disaster mitigation: Results and performance metrics from RoboCup rescue , 2007, J. Field Robotics.

[15]  P. E. Rapp,et al.  Dynamical analysis reveals individuality of locomotion in goldfish , 2004, Journal of Experimental Biology.

[16]  Prasanna Velagapudi,et al.  How search and its subtasks scale in N robots , 2009, 2009 4th ACM/IEEE International Conference on Human-Robot Interaction (HRI).