A self-driving food delivery robot did not take kindly to the yellow caution tape surrounding a crime scene in Los Angeles as it plowed through the area, according to a video shared on Twitter Tuesday. William Gude is the owner of Film the Police LA, a watchdog Twitter account documenting police activity in the Los Angeles area. Gude was on the perimeter of a suspected shooting at Hollywood High School at approximately 10 a.m. PDT when he saw a food delivery robot rolling down the sidewalk, approaching the yellow caution tape delineating the crime scene. "I just saw it coming and I couldn't believe what I was seeing," Gude told Gizmodo on the phone.

The fifth and sixth generations of wireless communication networks are enabling tools such as internet of things devices, unmanned aerial vehicles (UAVs), and artificial intelligence, to improve the agricultural landscape using a network of devices to automatically monitor farmlands. Surveying a large area requires performing a lot of image classification tasks within a specific period of time in order to prevent damage to the farm in case of an incident, such as fire or flood. UAVs have limited energy and computing power, and may not be able to perform all of the intense image classification tasks locally and within an appropriate amount of time. Hence, it is assumed that the UAVs are able to partially offload their workload to nearby multi-access edge computing devices. The UAVs need a decision-making algorithm that will decide where the tasks will be performed, while also considering the time constraints and energy level of the other UAVs in the network. In this paper, we introduce a Deep Q-Learning (DQL) approach to solve this multi-objective problem. The proposed method is compared with Q-Learning and three heuristic baselines, and the simulation results show that our proposed DQL-based method achieves comparable results when it comes to the UAVs' remaining battery levels and percentage of deadline violations. In addition, our method is able to reach convergence 13 times faster than Q-Learning.

Tehran, Iran – The United Kingdom's defence ministry has backed a Ukrainian claim that Ukraine's forces likely shot down an Iranian-made drone that was used by Russia in its offensive against its neighbouring country. In its latest military intelligence update on Wednesday, the ministry said it was "highly likely" that Russia has deployed unmanned aerial vehicles (UAV) made by Iran in the nearly seven-month war in Ukraine. "Russia is almost certainly increasingly sourcing weaponry from other heavily sanctioned states like Iran and North Korea as its own stocks dwindle," it said. The statement came a day after the Ukrainian military published several images and said it had likely shot down a drone near Kupiansk in Kharkiv that appeared to be an Iranian Shahed-136 model. The Iranian government has yet to comment on the claims, but its officials have previously denied supplying Russia with drones to be used in Ukraine, saying Iran would not assist either side in the war as it backed its resolution through dialogue. There are no known official specifications for the Shahed-136, but it is a so-called "suicide drone" that is capable of carrying a warhead over long distances.

We propose a system solution to achieve data-efficient, decentralized state estimation for a team of flying robots using thermal images and inertial measurements. Each robot can fly independently, and exchange data when possible to refine its state estimate. Our system front-end applies an online photometric calibration to refine the thermal images so as to enhance feature tracking and place recognition. Our system back-end uses a covariance-intersection fusion strategy to neglect the cross-correlation between agents so as to lower memory usage and computational cost. The communication pipeline uses Vector of Locally Aggregated Descriptors (VLAD) to construct a request-response policy that requires low bandwidth usage. We test our collaborative method on both synthetic and real-world data. Our results show that the proposed method improves by up to 46 % trajectory estimation with respect to an individual-agent approach, while reducing up to 89 % the communication exchange. Datasets and code are released to the public, extending the already-public JPL xVIO library.

For robotic systems to succeed in high risk, real-world situations, they have to be quickly deployable and robust to environmental changes, under-performing hardware, and mission subtask failures. These robots are often designed to consider a single sequence of mission events, with complex algorithms lowering individual subtask failure rates under some critical constraints. Our approach utilizes common techniques in vision and control, and encodes robustness into mission structure through outcome monitoring and recovery strategies. In addition, our system infrastructure enables rapid deployment and requires no central communication. This report also includes lessons in rapid field robotic development and testing. We developed and evaluated our systems through real-robot experiments at an outdoor test site in Pittsburgh, Pennsylvania, USA, as well as in the 2020 Mohamed Bin Zayed International Robotics Challenge. All competition trials were completed in fully autonomous mode without RTK-GPS. Our system placed fourth in Challenge 2 and seventh in the Grand Challenge, with notable achievements such as popping five balloons (Challenge 1), successfully picking and placing a block (Challenge 2), and dispensing the most water onto an outdoor, real fire with an autonomous UAV (Challenge 3).

Unmanned Aerial Vehicles (UAV) have been standing out due to the wide range of applications in which they can be used autonomously. However, they need intelligent systems capable of providing a greater understanding of what they perceive to perform several tasks. They become more challenging in complex environments since there is a need to perceive the environment and act under environmental uncertainties to make a decision. In this context, a system that uses active perception can improve performance by seeking the best next view through the recognition of targets while displacement occurs. This work aims to contribute to the active perception of UAVs by tackling the problem of tracking and recognizing water surface structures to perform a dynamic landing. We show that our system with classical image processing techniques and a simple Deep Reinforcement Learning (Deep-RL) agent is capable of perceiving the environment and dealing with uncertainties without making the use of complex Convolutional Neural Networks (CNN) or Contrastive Learning (CL).

Many emerging Internet of Things (IoT) applications rely on information collected by sensor nodes where the freshness of information is an important criterion. \textit{Age of Information} (AoI) is a metric that quantifies information timeliness, i.e., the freshness of the received information or status update. This work considers a setup of deployed sensors in an IoT network, where multiple unmanned aerial vehicles (UAVs) serve as mobile relay nodes between the sensors and the base station. We formulate an optimization problem to jointly plan the UAVs' trajectory, while minimizing the AoI of the received messages. This ensures that the received information at the base station is as fresh as possible. The complex optimization problem is efficiently solved using a deep reinforcement learning (DRL) algorithm. In particular, we propose a deep Q-network, which works as a function approximation to estimate the state-action value function. The proposed scheme is quick to converge and results in a lower AoI than the random walk scheme. Our proposed algorithm reduces the average age by approximately $25\%$ and requires down to $50\%$ less energy when compared to the baseline scheme.

The visual camera is an attractive device in beyond visual line of sight (B-VLOS) drone operation, since they are low in size, weight, power, and cost, and can provide redundant modality to GPS failures. However, state-of-the-art visual localization algorithms are unable to match visual data that have a significantly different appearance due to illuminations or viewpoints. This paper presents iSimLoc, a condition/viewpoint consistent hierarchical global re-localization approach. The place features of iSimLoc can be utilized to search target images under changing appearances and viewpoints. Additionally, our hierarchical global re-localization module refines in a coarse-to-fine manner, allowing iSimLoc to perform a fast and accurate estimation. We evaluate our method on one dataset with appearance variations and one dataset that focuses on demonstrating large-scale matching over a long flight in complicated environments. On our two datasets, iSimLoc achieves 88.7\% and 83.8\% successful retrieval rates with 1.5s inferencing time, compared to 45.8% and 39.7% using the next best method. These results demonstrate robust localization in a range of environments.

We study a resource allocation problem for the cooperative aerial-ground vehicle routing application, in which multiple Unmanned Aerial Vehicles (UAVs) with limited battery capacity and multiple Unmanned Ground Vehicles (UGVs) that can also act as a mobile recharging stations need to jointly accomplish a mission such as persistently monitoring a set of points. Due to the limited battery capacity of the UAVs, they sometimes have to deviate from their task to rendezvous with the UGVs and get recharged. Each UGV can serve a limited number of UAVs at a time. In contrast to prior work on deterministic multi-robot scheduling, we consider the challenge imposed by the stochasticity of the energy consumption of the UAV. We are interested in finding the optimal recharging schedule of the UAVs such that the travel cost is minimized and the probability that no UAV runs out of charge within the planning horizon is greater than a user-defined tolerance. We formulate this problem ({Risk-aware Recharging Rendezvous Problem (RRRP))} as an Integer Linear Program (ILP), in which the matching constraint captures the resource availability constraints and the knapsack constraint captures the success probability constraints. We propose a bicriteria approximation algorithm to solve RRRP. We demonstrate the effectiveness of our formulation and algorithm in the context of one persistent monitoring mission.

Autonomous mobile robots, including unmanned aerial vehicles (UAVs), have received significant attention for their applications in construction. These platforms have great potential to automate and enhance the quality and frequency of the required data for many tasks such as construction schedule updating, inspections, and monitoring. Robust localization is a critical enabler for reliable deployments of autonomous robotic platforms. Automated robotic solutions rely mainly on the global positioning system (GPS) for outdoor localization. However, GPS signals are denied indoors, and pre-built environment maps are often used for indoor localization. This entails generating high-quality maps by teleoperating the mobile robot in the environment. Not only is this approach time-consuming and tedious, but it also is unreliable in indoor construction settings. Layout changes with construction progress, requiring frequent mapping sessions to support autonomous missions. Moreover, the effectiveness of vision-based solutions relying on visual features is highly impacted in low texture and repetitive areas on site. To address these challenges, we previously proposed a low-cost, lightweight tag-based visual-inertial localization method using AprilTags. Tags, in this method, are paper printable landmarks with known sizes and locations, representing the environment's quasi-map. Since tag placement/replacement is a manual process, it is subject to human errors. In this work, we study the impact of human error in the manual tag installation process and propose a stochastic approach to account for this uncertainty using the Lie group theory. Employing Monte Carlo simulation, we experimentally show that the proposed stochastic model incorporated in our on-manifold formulation improves the robustness and accuracy of tag-based localization against inevitable imperfections in manual tag installation on site.