Recent Developments in Aerial Robotics: A Survey and Prototypes Overview <s> B. UAV Papers Identification <s> Exploration of unknown environments is an important aspect to fielding teams of robots. Without the ability to determine on their own where to go in the environment, the full potential of robotic teams is limited to the abilities of human operators to deploy them for search and rescue, mapping, or other tasks that are predicated on gaining knowledge from the environment. This is of particular importance in real-world 3-Dimensional (3-D) environments where simple planar assumptions can lead to incomplete exploration, for example, real-world environments have areas underneath overhangs or inside caves. As an additional challenge, when the teams of robots have vastly different capabilities, the planning system must take those into account to efficiently utilize the available assets. In this paper, we present a combined air-ground system for conducting 3-D exploration in cluttered environments. We first describe the hardware and software components of the system. We then present our algorithm for planning 3-D goal locations for a heterogeneous team of robots to efficiently explore a previously unknown environment and demonstrate its applicability in real-world experiments. <s> BIB001 </s> Recent Developments in Aerial Robotics: A Survey and Prototypes Overview <s> B. UAV Papers Identification <s> Multi-robot search-and-rescue missions often face major challenges in adverse environments due to the limitations of traditional implicit and explicit communication. This paper proposes a novel multi-robot communication system (MRoCS), which uses a passive action recognition technique that overcomes the shortcomings of traditional models. The proposed MRoCS relies on individual motion, by mimicking the waggle dance of honey bees and thus forming and recognising different patterns accordingly. The system was successfully designed and implemented in simulation and with real robots. Experimental results show that, the pattern recognition process successfully reported high sensitivity with good precision in all cases for three different patterns thus corroborating our hypothesis. <s> BIB002
The UAV papers identification process involves three major steps. We first use a script to automatically collect more than thirty thousand instances of title and abstract from the mentioned eight journal/conference web pages since 2001, namely TRO, TME, IJRR, RAS, IROS, ICRA, ICUAS, HRI, and ROMAN. We also manually review the hard copies of the IROS and ICRA conferences' table of contents from 2001 to 2004, as we find that not all UAV papers in those years are listed on the website (IEEE Xplore). At the second step, we design a list of keywords (Table I) to search drone papers systematically from the titles and abstracts collected in the first step. Note that we search for both the full name of each keyword (e.g., Unmanned Aerial Vehicle) and its abbreviation (i.e., UAV) with an automatic program script. The keywords include most of the words that describe a UAV. For example, the word "quadcopter" or "quadrotor" could be detected by the keyword "copter" or "rotor". As long as one of the keywords is detected, the paper will pass this automated screening process. At the third step, we perform a manual screening to reject some non-drone papers. We read the abstract, section titles, related works, and experiment results of all the papers from the second step. If a paper passes all the five criteria below, we consider it a drone paper for this survey. 1) The paper must have more than two pages; we do not consider workshop and poster papers. 2) The paper must have at least one page of flight-related results. These can be either simulation/experiment results, prototyping/fabrication results, or insights/discussion/lesson learned. One exception is a survey/review paper, which normally does not present experiment results. Papers with details/photos of the UAV hardware are a plus. Note that the experiment results do not necessarily need to be a successful flight, e.g., flapping wing UAVs normally have on-the-bench test results. 3) In topics related to computer vision or image processing, the images must be collected from a UAV's onboard camera rather than a manually moving camera. 4) In topics related to computer vision or image processing, the images must be collected by the authors themselves. This is important, as authors who collect the dataset themselves often provide insights about their data collection and experiment results. 5) The paper which proposes a general method, e.g., path planning, must have related works and experiment results on drones. This is important, as some authors mention that their method can be applied to a UAV, but provide no experiment result to verify their statement. It is interesting to note that using the keyword "air" in the second step increases the number of false entries (since the keyword is used in many contexts) but helps to identify some rare drone-related papers that have only the keyword "air" in the title and abstract. By manually filtering the list in the third step, we successfully identify two of these drone papers  , BIB001 . Similarly, using the keyword "bee" can help to identify a rare drone paper BIB002 . On the other hand, we chose not to use the keyword of "wing" because it causes many false entries like the case of "following", "knowing", etc.