Impact of Background Music on the Performance of Laparoscopy Teams

BACKGROUND: Music and noise have different impacts on individuals in the operating room. Their effects on the performance of surgical teams in simulated environments are not well documented. We investigated if laparoscopic teams operating under favorable acoustic conditions would perform better than under noisy conditions.METHODS: We recruited 114 surgical residents and built 57 two-person teams. Each team was required to perform two laparoscopic tasks (object transportation and collaborative suturing) on a simulation training box under musical, neutral, and noisy acoustic conditions. Data were extracted from video recordings of each performance for analysis. Task performance was measured by the duration of time to complete a task and the total number of errors, and objective performance scores. The measures were compared over the three acoustic conditions.RESULTS: A musical environment elicited higher performance scores than a noisy environment for both the object transportation (performance score: 66.3 ± 8.6 vs. 57.6 ± 11.2; p < 0.001) and collaborative suturing tasks (78.6 ± 5.4 vs. 67.2 ± 11.1; p < 0.001). Task times in the musical and noisy environments was subtracted to produce a musical-noisy difference time. Pearson correlation coefficient analysis showed a significant negative relationship between the team experience score and the musical-noisy difference time on the object transportation (r = -0.246, p = 0.046) and collaborative suturing tasks (r = -0.248, p = 0.044). CONCLUSIONS: As to individuals, music enhances the performance of a laparoscopy team while noise worsens performance. The negative correlation between team experience and musical-noisy difference time suggests that laparoscopy teams composed of experienced surgeons are less likely affected by an acoustic distraction than novice teams. Team resistance to acoustic distraction may lead to a new way for assessing team skills.

A Distributed Approach for Autonomous Cooperative Transportation

Autonomous mobile robots have now emerged as a means of transportation in several applications, such as warehouse, factory, space, and deep-sea where direct human intervention is impossible or impractical. Since explicit communication provides a better and reliable way of multi-robot coordination compared to implicit communication, so it is preferred in critical missions, such as search and rescue, where efficient and continuous coordination between robots is required. Cooperative object transportation is needed when the object is either heavy or too large or needs extra care to handle (e.g., shifting a glass table) or has a complex shape, which makes it difficult for a single robot to transport. All group members need no participation in the physical act of transport; cooperation can still be achieved when some robots transport the object, and others are involved in, say, coordination and navigation along the desired trajectory and/or clear obstacles along the path. A distributed approach for autonomous cooperative transportation in a dynamic multi-robot environment is discussed.

Cooperative Object Transportation Using Curriculum-Based Deep Reinforcement Learning

This paper presents a cooperative object transportation technique using deep reinforcement learning (DRL) based on curricula. Previous studies on object transportation highly depended on complex and intractable controls, such as grasping, pushing, and caging. Recently, DRL-based object transportation techniques have been proposed, which showed improved performance without precise controller design. However, DRL-based techniques not only take a long time to learn their policies but also sometimes fail to learn. It is difficult to learn the policy of DRL by random actions only. Therefore, we propose two curricula for the efficient learning of object transportation: region-growing and single- to multi-robot. During the learning process, the region-growing curriculum gradually extended to a region in which an object was initialized. This step-by-step learning raised the success probability of object transportation by restricting the working area. Multiple robots could easily learn a new policy by exploiting the pre-trained policy of a single robot. This single- to multi-robot curriculum can help robots to learn a transporting method with trial and error. Simulation results are presented to verify the proposed techniques.

Intelligent Multi-Robot System for Collaborative Object Transportation Tasks in Rough Terrains

Human missions on other planets require constructing outposts and infrastructures, and one may need to consider relocating such large objects according to changes in mission spots. A multi-robot system would be a good option for such a transportation task because it can carry massive objects and provide better system reliability and redundancy when compared to a single robot system. This paper proposes an intelligent and decentralized approach for the multi-robot system using a genetic fuzzy system to perform an object transportation mission that not only minimizes the total travel distance of the multi-robot system but also guarantees the stability of the whole system in a rough terrain environment. The proposed fuzzy inference system determines the multi-robot system’s input for transporting an object to a target position and is tuned in the training process by a genetic algorithm with an artificially generated structured environment employing multiple scenarios. It validates the optimality of the proposed approach by comparing the training results with the results obtained by solving the formulated optimal control problem subject to path inequality constraints. It highlights the performance of the proposed approach by applying the trained fuzzy inference systems to operate the multi-robot system in unstructured environments.