Robotic arms are highly common in various automation processes such as manufacturing lines. However, these highly capable robots are usually degraded to simple repetitive tasks such as pick-and-place. On the other hand, designing an optimal robot for one specific task consumes large resources of engineering time and costs. In this paper, we propose a novel concept for optimizing the fitness of a robotic arm to perform a specific task based on human demonstration. Fitness of a robot arm is a measure of its ability to follow recorded human arm and hand paths. The optimization is conducted using a modified variant of the Particle Swarm Optimization for the robot design problem. In the proposed approach, we generate an optimal robot design along with the required path to complete the task. The approach could reduce the time-to-market of robotic arms and enable the standardization of modular robotic parts. Novice users could easily apply a minimal robot arm to various tasks. Two test cases of common manufacturing tasks are presented yielding optimal designs and reduced computational effort by up to 92%.

Design of workplaces that include human-machine systems and manual material handling should consider both the productivity of workers and the risk of injury. In this study, a simulation-based optimization methodology for a manual material handling task design was developed. A new formulation of the optimization problem is presented, whose objective is to maximize worker productivity and at the same time not to exceed ergonomic thresholds (which represent injury-risk measures). The workplace and work process were simulated using digital human modeling software (Jack), and the best design was found using a genetic algorithm. The results show that the new formulation of the optimization problem improved the predicted productivity by 105%, compared to the formulation used in previous studies that used a multi-objective function. Meanwhile, the risk of injury did not exceed ergonomic thresholds.

This paper presents a set of algorithms that are developed for real-time dynamic switching between collaboration levels in a human-robot target recognition system. The algorithms were developed for a closed-loop controller to maximize system performance, despite deviations in the parameter values. These developments enable smooth real-time adaptation of the combined human-robot system to many possible changes of the environment, human operator, and robot performance. System performance was analyzed in simulations for a variety of target probability distributions. Two hundred independent simulations for each target probability distribution were conducted to calculate algorithm performance for a variety of conditions. Values for human operations were taken from a target recognition experiment dealing with detecting melons for a robotic melon harvester. The numerical analysis results indicated that the developed dynamic switching algorithms resulted in improved system performance that, in some cases, was increased by more than 90%.

Robotic systems in unstructured environments must cope with unknown, unpredictable, and dynamic situations. Inherent uncertainty, and limited sensor accuracy and reliability impede target recognition performance. Introducing a human operator into the system can help improve performance and simplify the robotic system. In this paper, four basic levels of collaboration were defined for human-robot collaboration in target recognition tasks. An objective function that includes operational and time costs was developed to quantify performance and determine the best collaboration level. Signal detection theory was applied to evaluate system performance. The optimal collaboration level for different cases was determined by using numerical analyses of the objective function. The findings indicate that the best system performance, the optimal values of performance measures, and the best collaboration level depend on the task, the environment, human and robot parameters, and the system characteristics. For the tested cases, the manual level was never the best collaboration level for achieving the optimal solution. The autonomous level was the best collaboration level when robot sensitivity was higher than human sensitivity. In general, collaboration of human and robot in target recognition tasks will improve upon the optimal performance of a single human detector.

Robots operating in unstructured environments must cope with uncertain, unpredictable and dynamic situations, resulting in lack of information. Introducing a human-operator into the system can help simplify the robotic system and improve performance. In previous work we presented a methodology to determine the best collaboration level for an integrated human-robot target recognition system in unstructured environments. The methodology is based on an objective function for target recognition in human-robot systems. In this paper the objective function was expanded to include operational and time costs that are both important for evaluating and optimizing system performance. Numerical analyses of the developed objective function, combined with signal detection theory, were applied for the predefined collaboration levels. Results indicate that optimal collaboration of human and robot in target recognition tasks will always improve the optimal performance of a single human detector.

This paper presents a methodology to determine the best collaboration level for an integrated human-robot target recognition system in unstructured environments. Four human-robot collaboration levels for target recognition tasks were defined, tested and evaluated. The collaboration levels were adjusted to an extensive range of automation, from manual to fully autonomous. An objective function for target recognition in human-robot systems was developed to allow computation of the expected value of system performance given the human, robot, environmental and task parameters. The objective function includes operational and time costs that are both important in evaluation and optimization of system performance. It quantifies the multitude of influencing parameters through a weighted sum of performance measures, and enables to predict system performance and the desirable level of collaborations and to help design optimal systems for specific tasks. Numerical analysis of the developed objective function combined with signal detection theory was applied for the defined collaboration levels. Based on the numerical analysis, the human and robot optimal values were determined for different task and environment parameters. Results indicate that collaboration of human and robot in target recognition tasks will always improve the optimal performance of a single human detector. When robot sensitivity is higher than human sensitivity, and, the overall system sensitivity never decreases beneath the robot sensitivity the best collaboration level is autonomous robot operation.