This work proposes an autonomous multi-robot exploration pipeline that coordinates the behaviors of robots in an indoor environment composed of multiple rooms. Contrary to simple frontier-based exploration approaches, we aim to enable robots to methodically explore and observe an unknown set of rooms in a structured building, keeping track of which rooms are already explored and sharing this information among robots to coordinate their behaviors in a distributed manner. To this end, we propose (1) a geometric cue extraction method that processes 3D point cloud data and detects the locations of potential cues such as doors and rooms, (2) a circular decomposition for free spaces used for target assignment. Using these two components, our pipeline effectively assigns tasks among robots, and enables a methodical exploration of rooms. We evaluate the performance of our pipeline using a team of up to 3 aerial robots, and show that our method outperforms the baseline by 33.4% in simulation and 26.4% in real-world experiments.

Small-sized unmanned aerial vehicles (UAVs) have been widely investigated for use in a variety of applications such as remote sensing and aerial surveying. Direct three-dimensional (3D) mapping using a small-sized UAV equipped with a laser scanner is required for numerous remote sensing applications. In direct 3D mapping, the precise information about the position and attitude of the UAV is necessary for constructing 3D maps. In this study, we propose a novel and robust technique for estimating the position and attitude of small-sized UAVs by employing multiple low-cost and light-weight global navigation satellite system (GNSS) antennas/receivers. Using the "redundancy" of multiple GNSS receivers, we enhance the performance of real-time kinematic (RTK)-GNSS by employing single-frequency GNSS receivers. This method consists of two approaches: hybrid GNSS fix solutions and consistency examination of the GNSS signal strength. The fix rate of RTK-GNSS using single-frequency GNSS receivers can be highly enhanced to combine multiple RTK-GNSS to fix solutions in the multiple antennas. In addition, positioning accuracy and fix rate can be further enhanced to detect multipath signals by using multiple GNSS antennas. In this study, we developed a prototype UAV that is equipped with six GNSS antennas/receivers. From the static test results, we conclude that the proposed technique can enhance the accuracy of the position and attitude estimation in multipath environments. From the flight test, the proposed system could generate a 3D map with an accuracy of 5 cm.

Unmanned aerial vehicle (UAV) is a promising technology for last-mile cargo delivery. However, the limited on-board battery capacity, cellular unreliability, and frequent handoffs in the airspace are the main obstacles to unleash its full potential. Given that existing cellular networks were primarily designed to service ground users, re-utilizing the same architecture for highly mobile aerial users, e.g., cargo-UAVs, is deemed challenging. Indeed, to ensure a safe delivery using cargo-UAVs, it is crucial to utilize the available energy efficiently, while guaranteeing reliable connectivity for command-and-control and avoiding frequent handoff. To achieve this goal, we propose a novel approach for joint cargo-UAV trajectory planning and cell association. Specifically, we formulate the cargo-UAV mission as a multi-objective problem aiming to 1) minimize energy consumption, 2) reduce handoff events, and 3) guarantee cellular reliability along the trajectory. We leverage reinforcement learning (RL) to jointly optimize the cargo-UAV's trajectory and cell association. Simulation results demonstrate a performance improvement of our proposed method, in terms of handoffs, disconnectivity, and energy consumption, compared to benchmarks.

A pose-graph-based optimization technique is widely used to estimate robot poses using various sensor measurements from devices such as laser scanners and cameras. The global navigation satellite system (GNSS) has recently been used to estimate the absolute 3D position of outdoor mobile robots. However, since the accuracy of GNSS single-point positioning is only a few meters, the GNSS is not used for the loop closure of a pose graph. The main purpose of this study is to generate a loop closure of a pose graph using a time-relative real-time kinematic GNSS (TR-RTK-GNSS) technique. The proposed TR-RTK-GNSS technique uses time-differential carrier phase positioning, which is based on carrier-phase-based differential GNSS with a single GNSS receiver. Unlike a conventional RTK-GNSS, we can directly compute the robot's relative position using only a stand-alone GNSS receiver. The initial pose graph is generated from the accumulated velocity computed from GNSS Doppler measurements. To reduce the accumulated error of velocity, we use the TR-RTK-GNSS technique for the loop closure in the graph-based optimization framework. The kinematic positioning tests were performed using an unmanned aerial vehicle to confirm the effectiveness of the proposed technique. From the tests, we can estimate the vehicle's trajectory with approximately 3 cm accuracy using only a stand-alone GNSS receiver.

This paper proposes a highly accurate trajectory estimation method for outdoor mobile robots using global navigation satellite system (GNSS) time differences of carrier phase (TDCP) measurements. By using GNSS TDCP, the relative 3D position can be estimated with millimeter precision. However, when a phenomenon called cycle slip occurs, wherein the carrier phase measurement jumps and becomes discontinuous, it is impossible to accurately estimate the relative position using TDCP. Although previous studies have eliminated the effect of cycle slip using a robust optimization technique, it was difficult to completely eliminate the effect of outliers. In this paper, we propose a method to detect GNSS carrier phase cycle slip, estimate the amount of cycle slip, and modify the observed TDCP to calculate the relative position using the factor graph optimization framework. The estimated relative position acts as a loop closure in graph optimization and contributes to the reduction in the integration error of the relative position. Experiments with an unmanned aerial vehicle showed that by modifying the cycle slip using the proposed method, the vehicle trajectory could be estimated with an accuracy of 5 to 30 cm using only a single GNSS receiver, without using any other external data or sensors.

A new robust and accurate approach for the detection and localization of flying objects with the purpose of highly dynamic aerial interception and agile multi-robot interaction is presented in this paper. The approach is proposed for use onboard an autonomous aerial vehicle equipped with a 3D LiDAR sensor providing input data for the algorithm. It relies on a novel 3D occupancy voxel mapping method for the target detection and a cluster-based multiple hypothesis tracker to compensate uncertainty of the sensory data. When compared to state-of-the-art methods of onboard detection of other flying objects, the presented approach provides superior localization accuracy and robustness to different environments and appearance changes of the target, as well as a greater detection range. Furthermore, in combination with the proposed multi-target tracker, sporadic false positives are suppressed, state estimation of the target is provided and the detection latency is negligible. This makes the detector suitable for tasks of agile multi-robot interaction, such as autonomous aerial interception or formation control where precise, robust, and fast relative localization of other robots is crucial. We demonstrate the practical usability and performance of the system in simulated and real-world experiments.

Yemen's Houthi movement says it has targeted two Israeli ships with an armed drone and a naval missile, reports a spokesperson for the group's military. The spokesperson said the two ships, Unity Explorer and Number Nine, were targeted after they rejected warnings from the group's navy, the Reuters news agency reported on Sunday. British maritime security company Ambrey said a bulk carrier ship had been hit by at least two drones while sailing in the Red Sea. Another container ship reportedly suffered damage from a drone attack about 101km (63 miles) northwest of the northern Yemeni port of Hodeida, it added. The Pentagon also said a US warship and multiple commercial ships came under attack in the Red Sea, potentially marking a major escalation in a series of maritime attacks since the Israel-Hamas war began on October 7. "We are aware of reports regarding attacks on the USS Carney and commercial vessels in the Red Sea and will provide information as it becomes available," the Pentagon said.

NSC Communications Coordinator John Kirby responds to progressive pushback against U.S. aid to Israel on'FOX News Sunday.' The Pentagon said Sunday a U.S. warship and multiple commercial vessels are under attack in the Red Sea. The development signifies a serious escalation in a series of maritime attacks in the Middle East linked to the Israel-Hamas war. "We're aware of reports regarding attacks on the USS Carney and commercial vessels in the Red Sea and will provide information as it becomes available, later," Pentagon spokesman told Fox News, confirming reports of an attack on a U.S. warship in the Red Sea. The Pentagon initially told the Associated Press, "We're aware of reports regarding attacks on the USS Carney and commercial vessels in the Red Sea and will provide information as it becomes available." USS Carney is a Arleigh Burke-class guided-missile destroyer that has been shooting down drones and cruise missiles in recent weeks launched by Iran-backed Houthi rebels, who claimed credit for Sunday's attack.

NSC Communications Coordinator John Kirby responds to progressive pushback against U.S. aid to Israel on'FOX News Sunday.' Three commercial vessels were attacked in the Red Sea on Sunday, prompting a U.S. warship to shoot down multiple unmanned aerial vehicles (UAV) headed toward them. The development could signify a serious escalation in a series of maritime attacks in the Middle East linked to the Israel-Hamas war. "Today, there were four attacks against three separate commercial vessels operating in international waters in the southern Red Sea," a statement by U.S. Central Command (CENTCOM) explained. "These three vessels are connected to 14 separate nations." The USS Carney was in the southern Red Sea, just north of the Bab al-Mandab Strait, when it shot down three Houthi drones heading in its direction, a U.S. official told Fox News, adding that the action was taken in self-defense. The drones were launched from Houthi-controlled areas of Yemen, the official claimed.

Due to its decentralised, distributed and scalable nature, swarm robotics has great potential for applications ranging from agriculture to environmental monitoring and logistics. Various swarm control methods and algorithms are currently known, such as virtual leader, vector and potential field, and others. Such methods often show good results in specific conditions and tasks. The variety of tasks solved by the swarm requires the development of a universal control algorithm. In this paper, we propose an evolution of a thermal motion equivalent method (TMEM) inspired by the behavioural similarity of thermodynamic interactions between molecules. Previous research has shown the high efficiency of such a method for terrain monitoring tasks. This work addresses the problem of swarm formation of geometric structures, as required for logistics and formation movement tasks. It is shown that the formation of swarm geometric structures using the TMEM is possible with a special nonlinear interaction function of the agents. A piecewise linear interaction function is proposed that allows the formation of a stable group of agents. The results of the paper are validated by numerical modelling of the swarm dynamics. A linear quadrocopter model is considered as an agent. The fairness of the choice of the interaction function is shown.