Drones working together can create large 3D-printed structures made of foam or cement. The experiments are paving the way for a future where swarms of drones could help construct extremely tall or intricate buildings and other structures like bridges without the need for support scaffolding or large construction machinery. "We're talking about being able to build something of limitless size, theoretically speaking," says Robert Stuart-Smith at the University of Pennsylvania. Such creations would only be restricted by structural engineering constraints and factors like drone flight logistics. The drone swarm construction takes inspiration from animals such as wasps and termites.

Delivery robots aim to achieve high precision to facilitate complete autonomy. A precise three-dimensional point cloud map of sidewalk surroundings is required to estimate self-location. With or without the loop closing method, the cumulative error increases gradually after mapping for larger urban or city maps due to sensor drift. Therefore, there is a high risk of using the drifted or misaligned map. This article presented a technique for fusing GPS to update the 3D point cloud and eliminate cumulative error. The proposed method shows outstanding results in quantitative comparison and qualitative evaluation with other existing methods.

We consider several algorithms for exploring and filling an unknown, connected region, by simple, airborne agents. The agents are assumed to be identical, autonomous, anonymous and to have a finite amount of memory. The region is modeled as a connected sub-set of a regular grid composed of square cells. The algorithms described herein are suited for Micro Air Vehicles (MAV) since these air vehicles enable unobstructed views of the ground below and can move freely in space at various heights. The agents explore the region by applying various action-rules based on locally acquired information Some of them may settle in unoccupied cells as the exploration progresses. Settled agents become virtual pheromones for the exploration and coverage process, beacons that subsequently aid the remaining, and still exploring, mobile agents. We introduce a backward propagating information diffusion process as a way to implement a deterministic indicator of process termination and guide the mobile agents. For the proposed algorithms, complete covering of the graph in finite time is guaranteed when the size of the region is fixed. Bounds on the coverage times are also derived. Extensive simulation results exhibit good agreement with the theoretical predictions.

Serve Robotics has teamed with several companies including Uber, Domino's and Amazon to launch a pilot program for robot delivery. When one of its robots encountered crime scene tape, it was able to sneak by with a little help from a nearby TV cameraman.

Abstract--Although the use of multiple Unmanned Aerial Vehicles (UAVs) has great potential for fast autonomous exploration, it has received far too little attention. To effectively dispatch the UAVs, a pairwise interaction based on an online hgrid space decomposition is used. It ensures that all UAVs simultaneously explore distinct regions, using only asynchronous and limited communication. Further, we optimize the coverage paths of unknown space and balance the workloads partitioned to each UAV with a Capacitated Vehicle Routing Problem(CVRP) formulation. Given the task allocation, each UAV constantly updates the coverage path and incrementally extracts crucial information to support the exploration planning. A hierarchical planner finds exploration paths, refines local viewpoints and generates minimum-time trajectories in sequence to explore the unknown space agilely and safely. The proposed approach is evaluated extensively, showing high exploration efficiency, scalability and robustness to limited communication. Furthermore, for the first time, we achieve fully decentralized collaborative exploration with multiple UAVs in real world. Two quadrotors simultaneously explore a complex unknown environment. It is demonstrated that UAVs are particularly suited to exploring complex environments efficiently, thanks to their the coordination vulnerable and less effective. To improve the agility and flexibility. Secondly, many multi-robot exploration approaches been paid to multi-UAV systems. However, using a fleet solely consider the allocation of frontiers or viewpoints. of UAVs has incredible potential, since it not only enables Because the actual regions explored by each UAV are not faster accomplishment of exploration, but also is more faulttolerant accounted for, the strategies often result in interference among than a single UAV.

We propose a novel hybrid cable-based robot with manipulator and camera for high-accuracy, medium-throughput plant monitoring in a vertical hydroponic farm and, as an example application, demonstrate non-destructive plant mass estimation. Plant monitoring with high temporal and spatial resolution is important to both farmers and researchers to detect anomalies and develop predictive models for plant growth. The availability of high-quality, off-the-shelf structure-from-motion (SfM) and photogrammetry packages has enabled a vibrant community of roboticists to apply computer vision for non-destructive plant monitoring. While existing approaches tend to focus on either high-throughput (e.g. satellite, unmanned aerial vehicle (UAV), vehicle-mounted, conveyor-belt imagery) or high-accuracy/robustness to occlusions (e.g. turn-table scanner or robot arm), we propose a middle-ground that achieves high accuracy with a medium-throughput, highly automated robot. Our design pairs the workspace scalability of a cable-driven parallel robot (CDPR) with the dexterity of a 4 degree-of-freedom (DoF) robot arm to autonomously image many plants from a variety of viewpoints. We describe our robot design and demonstrate it experimentally by collecting daily photographs of 54 plants from 64 viewpoints each. We show that our approach can produce scientifically useful measurements, operate fully autonomously after initial calibration, and produce better reconstructions and plant property estimates than those of over-canopy methods (e.g. UAV). As example applications, we show that our system can successfully estimate plant mass with a Mean Absolute Error (MAE) of 0.586g and, when used to perform hypothesis testing on the relationship between mass and age, produces p-values comparable to ground-truth data (p=0.0020 and p=0.0016, respectively).

This paper addresses the problem of developing an algorithm for autonomous ship landing of vertical take-off and landing (VTOL) capable unmanned aerial vehicles (UAVs), using only a monocular camera in the UAV for tracking and localization. Ship landing is a challenging task due to the small landing space, six degrees of freedom ship deck motion, limited visual references for localization, and adversarial environmental conditions such as wind gusts. We first develop a computer vision algorithm which estimates the relative position of the UAV with respect to a horizon reference bar on the landing platform using the image stream from a monocular vision camera on the UAV. Our approach is motivated by the actual ship landing procedure followed by the Navy helicopter pilots in tracking the horizon reference bar as a visual cue. We then develop a robust reinforcement learning (RL) algorithm for controlling the UAV towards the landing platform even in the presence of adversarial environmental conditions such as wind gusts. We demonstrate the superior performance of our algorithm compared to a benchmark nonlinear PID control approach, both in the simulation experiments using the Gazebo environment and in the real-world setting using a Parrot ANAFI quad-rotor and sub-scale ship platform undergoing 6 degrees of freedom (DOF) deck motion.

The central idea involves automating the Navy helicopter ship landing procedure where the pilot utilizes the ship as the visual reference for long-range tracking; however, refers to a standardized visual cue installed on most Navy ships called the "horizon bar" for the final approach and landing phases. This idea is implemented using a uniquely designed nonlinear controller integrated with machine vision. The vision system utilizes machine learning based object detection for long-range ship tracking and classical computer vision for the estimation of aircraft relative position and orientation utilizing the horizon bar during the final approach and landing phases. The nonlinear controller operates based on the information estimated by the vision system and has demonstrated robust tracking performance even in the presence of uncertainties. The developed autonomous ship landing system was implemented on a quad-rotor UAV equipped with an onboard camera, and approach and landing were successfully demonstrated on a moving deck, which imitates realistic ship deck motions. Extensive simulations and flight tests were conducted to demonstrate vertical landing safety, tracking capability, and landing accuracy. The video of the real-world experiments and demonstrations is available at this URL.

Flocking control is a significant problem in multi-agent systems such as multi-agent unmanned aerial vehicles and multi-agent autonomous underwater vehicles, which enhances the cooperativity and safety of agents. In contrast to traditional methods, multi-agent reinforcement learning (MARL) solves the problem of flocking control more flexibly. However, methods based on MARL suffer from sample inefficiency, since they require a huge number of experiences to be collected from interactions between agents and the environment. We propose a novel method Pretraining with Demonstrations for MARL (PwD-MARL), which can utilize non-expert demonstrations collected in advance with traditional methods to pretrain agents. During the process of pretraining, agents learn policies from demonstrations by MARL and behavior cloning simultaneously, and are prevented from overfitting demonstrations. By pretraining with non-expert demonstrations, PwD-MARL improves sample efficiency in the process of online MARL with a warm start. Experiments show that PwD-MARL improves sample efficiency and policy performance in the problem of flocking control, even with bad or few demonstrations.

Delivery robots continue to expand their market while helping golf clubs' technological makeover. Autonomous robot delivery platform startup Neubility has launched what it says is "the world's first" self-driving robot service on a golf course. As part of that first start, Neubility has concluded an agreement with Korean Food Service Company, Samsung Welstory to provide autonomous delivery robots and plans to commercialize such delivery services for golf courses starting October. To this end, it will offer delivery robots to many locally renowned golf courses in Korea. Neubility has test-operated delivery robots since last March and have completed preliminary tests with flawless results.