# Service Robots: Trends and Technology
## 3 Results
### Trends  
The revision of the literature on service robots identifies diverse trends in the fields of healthcare, industry, home service, and multi-purpose indoor environments. Regarding the type of robots, trends in autonomous navigation [99; 100], mobile robots [35], unmanned autonomous systems [101; 102], and imitation learning systems [103; 104] were identified. Healthcare: The current global population trend has shown an increase in elderly persons, as well as an increase in more populated cities; therefore, there is a higher demand for healthcare services such as medicine and nursing [105]. According to [PERSON] and [PERSON] [105], three types of service robots oriented to health exist: doctor, nurse, and home health care robots. Regarding nursing robots, applications have been implemented for feeding assistance, automated soaping and showering for the elderly, robotic therapeutic companions, pharmaceutical transporters, pick-and-place patients in bed, and ambulation assistants [105]. A human action recognition algorithm using depth cameras, object detection, and human joint identification techniques supports patients with mild cognitive impairment (MCI) at their homes. Assistive living robots could be of use to MCI patients, as they can identify potential risks due to errors and avoid dangerous accidents [106]. Other studies have implemented physical exercise programs in elderly health centers with success. In this study case, 41 volunteers underwent a training program with a humanoid Aldebaran NAO robot and with a human coach [107]. The study results showed a good response towards the humanoid coach, at least for the training program. When analyzing a factual information task between robot and human, the human coach was more efficient. A biomechanical rehabilitation, one degree of freedom (DOF) robotic handle for post-stroke patients applies an adaptive reinforcement learning (RL) algorithm [108]. Byconstantly adapting the difficulty level of a virtual \"nut-catching\" game, depending on the skill level of volunteers, patients can learn at a faster pace while not losing their motivation. A telemedicine robot (see Figure 10) was developed in [109], composed of a four-wheel base, a robotic arm, and a tablet, acting as the head of the robot. The robot also includes an array of ultrasound sensors and cameras. The robot allows automated navigation combining its sensors and actuators, including obstacle avoidance and object manipulation tasks (e.g., the floor and shelves). It implements routines such as TakeMedicine, WallFollowing, and Doorpassing. Other applications are also included, such as fetching, providing reminders, calendar, and interpersonal communication [109]. Industry: Different applications of service robots have been implemented for the industrial context. In [104], a machine learning technology enables a chatbot to provide support to customers in financial-product sales. Using robots to work continuously, text information from FAQs, call center response manuals, and office documents were used as input to a machine learning model to generate artificial conversation about bank services to interested customers. The use of unmanned aerial vehicles (UAV) has been reported in high risk tasks, such as transmission line inspection in China, Japan, Spain, and Britain [102]. By implementing UAV control and combining image processing and artificial intelligence, UAVs have performed autonomous inspection. These types of robots use a combination of visible light and thermal infrared sensing, as well as LiDAR technology [102]. Although UAVs are useful for safety purposes, manual inspection still outperforms in some scenarios, and it can also perform repairs, while UAV cannot. Collaborative robots, usually robotic arms or semi-humanoid robots, represent an intermediate automation level between manual and fully automated manufacturing. In this approach, the robot acts as an assistant to a human during specific tasks.