Switching Between Collaboration Levels in a Human-Robot Target Recognition System

Itshak Tkach, Avital Bechar, Yael Edan

IEEE Transactions on Systems, man and cybernetics – Part C: Applications and Reviews. Vol. 41, No.6, November 2011

Summary

                  Human operator (HO) excel in recognition capabilities, therefore a combined system robot and human working together, may work together well in this kind of tasks. Bechar et al. previously defined four human-robot collaboration levels for target recognition tasks in unstructured environments, based on the assumption that integration of an HO in a robotic system tends to reduce complexity of the robotic system. Performances in a human-robot environment are mainly affected by: the state of human, the environmental conditions and the system parameters. According on different conditions of the working environment, it might be good to have the possibility of switching between collaboration levels, as proposed in this paper, proposing a logical controller that considers important parameters to maintain maximum performance. The human-robot environment is defined as a system composed by the HO subsystem, where a human perceived information from a display, the robot subsystem is comprising the autonomous operations that are defined through programming. The HO has the possibility of deciding how and when to intervene in the current work state in any collaboration level (which, from Bechar et al. are: the human operator detect and marks targets solely, the human operator supervises the robot, the human operator completes robot’s detections and the robot acts autonomously). The objective function is expressed through the operation cost: V_IS=V_HS+V_MS+V_FAS+V_CRS+V_TS  (where V_FAS and V_HS are penalties, V_HS is the gain from detecting targets, V_MS is the cost of missed targets, V_CRS is the benefit gained from correct rejection and V_TS is the cost of time and actions). The terms V_FAS and V_HS have negative values (they are penalties), so the idea is improving them through technology characteristics enhancements, other terms are estimated through their probabilities. The system time is computed as a superposition of all the possible time probabilities (time for the human to confirm robots hits, time for the human to detect additional targets, time for the human to correct the robot false alarms, time for the human to mark false alarms, time for the robot to process the image and to achieve hits or false alarms). The time computation appears to be important for the calculation of V_TS. The parameters used for the calculation of the objective function are divided in 4 categories: human performance parameters, robot performance parameters, task performance parameters, environmental performance parameters. The controller is designed so that it is capable of switching between collaboration levels to provide optimal collaboration level, the human-robot system appears then to be a closed loop system with logical controller, where through the score of the optimal collaboration level (OCL), compared with the current collaboration level (CCL), it is able of apply the change and provides a manipulated input to the process u(t). Assumptions to the system are considering human not influencing robots, human and robot not influencing the target, the new image sampling after target achieved, noise and signals with the same distribution and system inputs achieved prior to each image sample, so that the switching objective function is V_IS= (V_ISoptimal- V_IScurrent) + t_response x V_t + Ψ x V_p , where V_p is the penality to switch earlier than the nominal value of the switching frequency and Ψ is the deviation value from the switching frequency and V_t is the penality for the system’s response time.

Different switching algorithms are proposed (page 961-062).

Key Concepts

Team Working, Human Robot Cooperation

Key Results

Dynamic switching in 100% conditions may not increase necessary performances, global optimum may not be achieved because of influence of local optimums, but it works under different probabilities and greatly increases the system performance in most of the simulated scenarios.