FIRST ON FOX: Dozens of drones that traipsed over Langley Air Force base in late 2023 revealed an astonishing oversight: Military officials did not believe they had the authority to shoot down the unmanned vehicles over the U.S. homeland. A new bipartisan bill, known as the COUNTER Act, seeks to rectify that, offering more bases the opportunity to become a "covered facility," or one that has the authority to shoot down drones that encroach on their airspace. The new bill has broad bipartisan and bicameral support, giving it a greater chance of becoming law. It's led by Armed Services Committee members Tom Cotton, R-Ark., and Kirsten Gillibrand, D-N.Y., in the Senate, and companion legislation is being introduced by August Pfluger, R-Texas, and Chrissy Houlahan, D-Pa., in the House. Currently, only half of the 360 domestic U.S. bases are considered "covered facilities" that are allowed to engage with unidentified drones.

This paper explores the method of achieving autonomous navigation of unmanned vehicles through Deep Reinforcement Learning (DRL). The focus is on using the Deep Deterministic Policy Gradient (DDPG) algorithm to address issues in high-dimensional continuous action spaces. The paper details the model of a Ackermann robot and the structure and application of the DDPG algorithm. Experiments were conducted in a simulation environment to verify the feasibility of the improved algorithm. The results demonstrate that the DDPG algorithm outperforms traditional Deep Q-Network (DQN) and Double Deep Q-Network (DDQN) algorithms in path planning tasks.

Path planning module is a key module for autonomous vehicle navigation, which directly affects its operating efficiency and safety. In complex environments with many obstacles, traditional planning algorithms often cannot meet the needs of intelligence, which may lead to problems such as dead zones in unmanned vehicles. This paper proposes a path planning algorithm based on DDQN and combines it with the prioritized experience replay method to solve the problem that traditional path planning algorithms often fall into dead zones. A series of simulation experiment results prove that the path planning algorithm based on DDQN is significantly better than other methods in terms of speed and accuracy, especially the ability to break through dead zones in extreme environments. Research shows that the path planning algorithm based on DDQN performs well in terms of path quality and safety. These research results provide an important reference for the research on automatic navigation of autonomous vehicles.

In order to solve the problem of frequent deceleration of unmanned vehicles when approaching obstacles, this article uses a Deep Q-Network (DQN) and its extension, the Double Deep Q-Network (DDQN), to develop a local navigation system that adapts to obstacles while maintaining optimal speed planning. By integrating improved reward functions and obstacle angle determination methods, the system demonstrates significant enhancements in maneuvering capabilities without frequent decelerations. Experiments conducted in simulated environments with varying obstacle densities confirm the effectiveness of the proposed method in achieving more stable and efficient path planning.

Pawleys Island, South Carolina, Mayor Brian Henry tells "Your World" that Hurricane Ian was different and brought a significant storm surge to the island. A high-tech sailing drone was deployed onto the Atlantic Ocean near Charleston, South Carolina, this past weekend to collect weather data directly from wicked hurricanes. The autonomous ocean drone, known as a saildrone, was redeployed by California-based company Saildrone Inc., which designs and operates autonomous ocean drones, in partnership with the National Oceanic and Atmospheric Administration (NOAA) to assist the agency in data collection on hurricanes. The same saildrone made international headlines in 2021 when it captured the "first-ever video from inside a major hurricane at sea" when Hurricane Sam barreled across the Atlantic. NOAA has previously incorporated drones into its research of hurricanes and 2023 will see an even larger and more high-tech fleet.

Existing intelligent driving technology often has a problem in balancing smooth driving and fast obstacle avoidance, especially when the vehicle is in a non-structural environment, and is prone to instability in emergency situations. Therefore, this study proposed an autonomous obstacle avoidance control strategy that can effectively guarantee vehicle stability based on Attention-long short-term memory (Attention-LSTM) deep learning model with the idea of humanoid driving. First, we designed the autonomous obstacle avoidance control rules to guarantee the safety of unmanned vehicles. Second, we improved the autonomous obstacle avoidance control strategy combined with the stability analysis of special vehicles. Third, we constructed a deep learning obstacle avoidance control through experiments, and the average relative error of this system was 15%. Finally, the stability and accuracy of this control strategy were verified numerically and experimentally. The method proposed in this study can ensure that the unmanned vehicle can successfully avoid the obstacles while driving smoothly.

Autonomous vehicles require more than simple artificial intelligence. A self-driving car receives data from various sources such as sonars, cameras, radars, GPS, and lidars allowing it to navigate in any environment. Information from these devices should be processed quickly, and data volumes are massive. The information from sensors is processed not only by the car's computer in real-time. Some data is sent to peripheral data centers for further analysis.

The Japanese government passed a bill Tuesday to introduce new rules for next-generation mobility, such as unmanned self-driving vehicles, automated delivery robots and electric kick scooters. The bill to revise the current road traffic law was approved at a plenary meeting of the House of Representatives, the lower chamber of the Diet, following its passage at the House of Councillors, the upper chamber, last week. Under the revised law, a license system will be introduced for operators of transport services using unmanned vehicles with Level 4 autonomy, which requires no driver in the remotely monitored vehicle within a limited area. Such vehicles are expected to be used for residents in depopulated areas. The new rules obligate the operators of unmanned vehicles to prepare a system to ensure that staff would be sent out to the site of any accidents.

We present an algorithm providing a heuristic solution to the NP-hard optimization problem known as the watchman route problem (WRP) within a 3D virtual environment testbed populated by simulated unmanned vehicles (UVs). The contribution made by our algorithm is three-fold. First, we utilize photon mapping as our means of representing the information sensed by a UV. Second, we use the photon map to generate an online solution to the closely-related NP-hard art gallery problem (AGP). Third, we use a 3D Chan-Vese segmentation algorithm initialized by our AGP-solver to produce a candidate set of path-planning waypoints. The use of photon mapping with our online AGP solver allows us to adapt UV operation to accommodate variable, less-than-ideal environmental circumstances. The use of our 3D Chan-Vese segmentation algorithm creates a set of candidate waypoints that yield greater visibility coverage when computing the WRP than would be obtainable otherwise. Our algorithm provides for quick learning among the unmanned vehicles operating within the testbed's virtual environment by generating easily-transferrable WRP-solving waypoints.

Drones became the most loved gadget nowadays. Drones are getting huge demand in the market. Amazing aerial photography is the main reason drones are used by photographers, businesses for spectacular shots. Drones could be extremely helpful during rescue operations in the mountains and in the forests. Just imagine how many lives they can save with timely delivered medical supplies or simply a bottle of water!! Drones were used mostly by the military in the old days.