A year ago researchers from the University of Zurich showcased their autonomous drones that were able to beat the fastest human pilots. However, that race wasn't "fair" in the sense that the AI algorithm commanding the drones had extra information that human pilots didn't have. In particular, the algorithm had access to near-perfect location and velocity estimation of the drones using motion capture systems, high-quality maps of the race course beforehand, and stereo cameras that can give depth information. This year, the team's autonomous drones raced on even playing fields without these handicaps, and its AI was able to beat the best human-controlled time by 0.5s in a three-lap race, a significant lead in the world of drone racing. Our take: This development is representative of AI progress ins many fields, where the researchers first make a working system with additional assumptions and then slowly chip away at these assumptions for a more robust and adaptable AI system.

In this work, we optimize the 3D trajectory of an unmanned aerial vehicle (UAV)-based portable access point (PAP) that provides wireless services to a set of ground nodes (GNs). Moreover, as per the Peukert effect, we consider pragmatic non-linear battery discharge for the battery of the UAV. Thus, we formulate the problem in a novel manner that represents the maximization of a fairness-based energy efficiency metric and is named fair energy efficiency (FEE). The FEE metric defines a system that lays importance on both the per-user service fairness and the energy efficiency of the PAP. The formulated problem takes the form of a non-convex problem with non-tractable constraints. To obtain a solution, we represent the problem as a Markov Decision Process (MDP) with continuous state and action spaces. Considering the complexity of the solution space, we use the twin delayed deep deterministic policy gradient (TD3) actor-critic deep reinforcement learning (DRL) framework to learn a policy that maximizes the FEE of the system. We perform two types of RL training to exhibit the effectiveness of our approach: the first (offline) approach keeps the positions of the GNs the same throughout the training phase; the second approach generalizes the learned policy to any arrangement of GNs by changing the positions of GNs after each training episode. Numerical evaluations show that neglecting the Peukert effect overestimates the air-time of the PAP and can be addressed by optimally selecting the PAP's flying speed. Moreover, the user fairness, energy efficiency, and hence the FEE value of the system can be improved by efficiently moving the PAP above the GNs. As such, we notice massive FEE improvements over baseline scenarios of up to 88.31%, 272.34%, and 318.13% for suburban, urban, and dense urban environments, respectively.

Drones equipped with cameras can significantly enhance human ability to perceive the world because of their remarkable maneuverability in 3D space. Ironically, object detection for drones has always been conducted in the 2D image space, which fundamentally limits their ability to understand 3D scenes. Furthermore, existing 3D object detection methods developed for autonomous driving cannot be directly applied to drones due to the lack of deformation modeling, which is essential for the distant aerial perspective with sensitive distortion and small objects. To fill the gap, this work proposes a dual-view detection system named DVDET to achieve aerial monocular object detection in both the 2D image space and the 3D physical space. To address the severe view deformation issue, we propose a novel trainable geo-deformable transformation module that can properly warp information from the drone's perspective to the BEV. Compared to the monocular methods for cars, our transformation includes a learnable deformable network for explicitly revising the severe deviation. To address the dataset challenge, we propose a new large-scale simulation dataset named AM3D-Sim, generated by the co-simulation of AirSIM and CARLA, and a new real-world aerial dataset named AM3D-Real, collected by DJI Matrice 300 RTK, in both datasets, high-quality annotations for 3D object detection are provided. Extensive experiments show that i) aerial monocular 3D object detection is feasible; ii) the model pre-trained on the simulation dataset benefits real-world performance, and iii) DVDET also benefits monocular 3D object detection for cars. To encourage more researchers to investigate this area, we will release the dataset and related code in https://sjtu-magic.github.io/dataset/AM3D/.

Drones can provide a minimally-constrained adapting camera view to support robot telemanipulation. Furthermore, the drone view can be automated to reduce the burden on the operator during teleoperation. However, existing approaches do not focus on two important aspects of using a drone as an automated view provider. The first is how the drone should select from a range of quality viewpoints within the workspace (e.g., opposite sides of an object). The second is how to compensate for unavoidable drone pose uncertainty in determining the viewpoint. In this paper, we provide a nonlinear optimization method that yields effective and adaptive drone viewpoints for telemanipulation with an articulated manipulator. Our first key idea is to use sparse human-in-the-loop input to toggle between multiple automatically-generated drone viewpoints. Our second key idea is to introduce optimization objectives that maintain a view of the manipulator while considering drone uncertainty and the impact on viewpoint occlusion and environment collisions. We provide an instantiation of our drone viewpoint method within a drone-manipulator remote teleoperation system. Finally, we provide an initial validation of our method in tasks where we complete common household and industrial manipulations.

A decade after Amazon made its first foray into robotics with its acquisition of Kiva Systems, the e-commerce giant is acquiring iRobot for $1.7 billion. While completion of the transaction is still subject to customary closing conditions, the deal expands Amazon's already extensive robotics portfolio. Here's a look at the company's robotics acquisitions and some of its investments and notable robots developed internally. It's impossible to talk about Amazon's history in robotics without talking about Kiva Systems. Amazon acquired the mobile robot company in 2012 for $775 million.

Rapid aerial grasping through robots can lead to many applications that utilize fast and dynamic picking and placing of objects. Rigid grippers traditionally used in aerial manipulators require high precision and specific object geometries for successful grasping. We propose RAPTOR, a quadcopter platform combined with a custom Fin Ray gripper to enable more flexible grasping of objects with different geometries, leveraging the properties of soft materials to increase the contact surface between the gripper and the objects. To reduce the communication latency, we present a new lightweight middleware solution based on Fast DDS (Data Distribution Service) as an alternative to ROS (Robot Operating System). We show that RAPTOR achieves an average of 83% grasping efficacy in a real-world setting for four different object geometries while moving at an average velocity of 1 m/s during grasping. In a high-velocity setting, RAPTOR supports up to four times the payload compared to previous works. Our results highlight the potential of aerial drones in automated warehouses and other manipulation applications where speed, swiftness, and robustness are essential while operating in hard-to-reach places.

The Taliban said Thursday they have no knowledge of Ayman al-Zawahiri's presence in Afghanistan, days after US President Joe Biden announced the Al-Qaeda chief's killing by a drone strike in Kabul. Zawahiri's assassination is the biggest blow to Al-Qaeda since US special forces killed Osama bin Laden in 2011, and calls into question the Taliban's promise not to harbour militant groups. "The Islamic Emirate of Afghanistan has no information about Ayman al-Zawahiri's arrival and stay in Kabul," said an official statement -- the Taliban's first mention of his name since Biden's announcement. Zawahiri was believed to be in charge of steering Al-Qaeda's operations -- including the 9/11 attacks -- as well as serving as bin Laden's personal doctor. A senior US administration official said the 71-year-old Egyptian was on the balcony of a three-storey house in the Afghan capital when targeted with two Hellfire missiles early on Sunday.

The Taliban says it is investigating a "claim" by the United States that it killed al-Qaeda leader Ayman al-Zawahiri in a drone attack in Kabul, says a Taliban official, indicating the group's leadership was not aware of his presence there. The US said it killed al-Zawahiri with a missile fired from a drone while he stood on a balcony at his Kabul hiding place on Sunday. US officials said the killing was the biggest blow to the armed group since its founder, Osama bin Laden was shot dead more than 10 years ago. "The government and the leadership wasn't aware of what is being claimed, nor any trace there," Suhail Shaheen, the designated Taliban representative to the United Nations, who is based in Doha, told journalists in a message. "Investigation is under way now to find out about the veracity of the claim," he said, adding that the results of the investigation would be shared publicly.

Hours after U.S. House Speaker Nancy Pelosi left Taipei after her controversial trip to the island, two Chinese drones breached Taiwan's airspace for the first time. Two People's Liberation Army (PLA) drones were spotted inside the airspace of Kinmen, an outlying Taiwanese island. Taiwan's Kinmen Defense Command announced two unidentified aircraft were spotted flying over Kinmen and Beiding islands Wednesday night at an altitude of about 2,000 meters. An analysis by the military determined they were unmanned aerial vehicles (UAVs), said Taiwan News, quoting an official statement. No Chinese military aircraft have conducted a flyover at Kinmen (alternatively known as Quemoy) since the 1950s.

Traditionally, traffic incident management (TIM) programs coordinate the deployment of emergency resources to immediate incident requests without accommodating the interdependencies on incident evolutions in the environment. However, ignoring inherent interdependencies on the evolution of incidents in the environment while making current deployment decisions is shortsighted, and the resulting naive deployment strategy can significantly worsen the overall incident delay impact on the network. The interdependencies on incident evolution in the environment, including those between incident occurrences, and those between resource availability in near-future requests and the anticipated duration of the immediate incident request, should be considered through a look-ahead model when making current-stage deployment decisions. This study develops a new proactive framework based on the distributed constraint optimization problem (DCOP) to address the above limitations, overcoming conventional TIM models that cannot accommodate the dependencies in the TIM problem. Furthermore, the optimization objective is formulated to incorporate Unmanned Aerial Vehicles (UAVs). The UAVs' role in TIM includes exploring uncertain traffic conditions, detecting unexpected events, and augmenting information from roadway traffic sensors. Robustness analysis of our model for multiple TIM scenarios shows satisfactory performance using local search exploration heuristics. Overall, our model reports a significant reduction in total incident delay compared to conventional TIM models. With UAV support, we demonstrate a further decrease in the overall incident delay through the shorter response time of emergency vehicles, and a reduction in uncertainties associated with the estimated incident delay impact.