This paper presents a time-efficient scheme for Mars exploration by the cooperation of multiple drones and a rover. To maximize effective coverage of the Mars surface in the long run, a comprehensive framework has been developed with joint consideration for limited energy, sensor model, communication range and safety radius, which we call TIME-SC2 (TIme-efficient Mars Exploration of Simultaneous Coverage and Charging). First, we propose a multi-drone coverage control algorithm by leveraging emerging deep reinforcement learning and design a novel information map to represent dynamic system states. Second, we propose a near-optimal charging scheduling algorithm to navigate each drone to an individual charging slot, and we have proven that there always exists feasible solutions. The attractiveness of this framework not only resides on its ability to maximize exploration efficiency, but also on its high autonomy that has greatly reduced the non-exploring time. Extensive simulations have been conducted to demonstrate the remarkable performance of TIME-SC2 in terms of time-efficiency, adaptivity and flexibility.
We propose a novel framework for the recommendation of swarm-based drone delivery services based on the consumers preferences. We propose a density-based pruning approach that uses the concept of partnerships with charging station providers to reduce the search space of swarm-based drone service delivery providers. A weighted service composition algorithm is proposed that considers the providers capabilities and consumers' preferences in selecting the best next service. We propose a voting-based recommendation algorithm to select the best providers. We conduct a set of experiments to evaluate …
Landing is the most challenging and risky aspect of multirotor drone flight, and only simple landing methods exist for autonomous drones. We explore methods for autonomous drone landing in two scenarios. In the first scenario, we examine methods for landing on known landing pads using fiducial markers and a gimbal-mounted monocular camera. This method has potential in drone applications where a drone must land more accurately than GPS can provide (e.g.~package delivery in an urban canyon). We expand on previous methods by actuating the drone's camera to track the marker over time, and we addre…
Landing is a challenging part of autonomous drone flight and a great research opportunity. This PhD proposes to improve on fiducial autonomous landing algorithms by making them more flexible. Further, it leverages its location, Iceland, to develop a method for landing on lava flows in cooperation with analog Mars exploration missions taking place in Iceland now - and potentially for future Mars landings.
Scheduling Drone and Mobile Charger via Hybrid-Action Deep Reinforcement Learning
Beijing Forestry University（北京林业大学）；Engineering Research Center for Forestry-oriented Intelligent Information Processing, National Forestry and Grassland Administration（国家林业和草原局林业智能信息处理工程研究中心）
Interpretable Multimodal Gesture Recognition for Drone and Mobile Robot Teleoperation via Log-Likelihood Ratio Fusion
Department of Artificial Intelligence, Korea University（人工智能系，韩国大学）；Department of Computer Science, RPTU Kaiserslautern-Landau（计算机科学系，RPTU凯撒斯劳滕-兰道）；Embedded Intelligence, German Research Center for Artificial Intelligence (DFKI)（嵌入式智能，德国人工智能研究中心（DFKI））
University of Leeds（利兹大学）；University of Sheffield（谢菲尔德大学）
This paper introduces a testbed to study distributed sensing problems of Unmanned Aerial Vehicles (UAVs) exhibiting swarm intelligence. Several Smart City applications, such as transport and disaster response, require efficient collection of sensor data by a swarm of intelligent and cooperative UAVs. This often proves to be too complex and costly to study systematically and rigorously without compromising scale, realism and external validity. With the proposed testbed, this paper sets a stepping stone to emulate, within small laboratory spaces, large sensing areas of interest originated from e…
TransforMARS: Fault-Tolerant Self-Reconfiguration for Arbitrarily Shaped Modular Aerial Robot Systems
Department of Electrical and Computer Engineering, National University of Singapore（电子与计算机工程系，新加坡国立大学）；Department of Mechanical Engineering, National University of Singapore（机械工程系，新加坡国立大学）
LLM-Land: Large Language Models for Context-Aware Drone Landing
Department of Electrical and Computer Engineering（电气与计算机工程系）；Drexel University（德雷塞尔大学）；Department of Electrical and Systems Engineering（电气与系统工程系）；University of Pennsylvania（宾夕法尼亚大学）
We present a new algorithm for deploying passenger robots in marsupial robot systems. A marsupial robot system consists of a carrier robot (e.g., a ground vehicle), which is highly capable and has a long mission duration, and at least one passenger robot (e.g., a short-duration aerial vehicle) transported by the carrier. We optimize the performance of passenger robot deployment by proposing an algorithm that reasons over uncertainty by exploiting information about the prior probability distribution of features of interest in the environment. Our algorithm is formulated as a solution to a seque…
Collaborative Navigation and Exploration with $β$-Sparse Gaussian Processes
D. Guggenheim School of Aerospace Engineering, Georgia Tech, Atlanta, GA, USA（佐治亚理工学院D.Guggenheim航空航天工程学院）；Department of Electrical and Computer Engineering, UNC Charlotte, Charlotte, NC, USA（北卡罗来纳大学夏洛特分校电气与计算机工程系）
Geofenced Unmanned Aerial Robotic Defender for Deer Detection and Deterrence (GUARD)
Dept. of Computer Science & Engineering, University of Minnesota（计算机科学与工程系，明尼苏达大学）；Dept. of Mechanical Engineering, University of Minnesota（机械工程系，明尼苏达大学）
