Distributed multi-agent target search and tracking with Gaussian process and reinforcement learning

Deploying multiple robots for target search and tracking has many practical applications, yet the challenge of planning over unknown or partially known targets remains difficult to address. With recent advances in deep learning, intelligent control techniques such as reinforcement learning have enabled agents to learn autonomously from environment interactions with little to no prior knowledge. Such methods can address the exploration-exploitation tradeoff of planning over unknown targets in a data-driven manner, eliminating the reliance on heuristics typical of traditional approaches and streamlining the decision-making pipeline with end-to-end training. In this paper, we propose a multi-agent reinforcement learning technique with target map building based on distributed Gaussian process. We leverage the distributed Gaussian process to encode belief over the target locations and efficiently plan over unknown targets. We evaluate the performance and transferability of the trained policy in simulation and demonstrate the method on a swarm of micro unmanned aerial vehicles with hardware experiments.Comment: 10 pages, 6 figures; preprint submitted to IJCAS; first two authors contributed equall

SGGNet2^2: Speech-Scene Graph Grounding Network for Speech-guided Navigation

The spoken language serves as an accessible and efficient interface, enabling non-experts and disabled users to interact with complex assistant robots. However, accurately grounding language utterances gives a significant challenge due to the acoustic variability in speakers' voices and environmental noise. In this work, we propose a novel speech-scene graph grounding network (SGGNet$^2$) that robustly grounds spoken utterances by leveraging the acoustic similarity between correctly recognized and misrecognized words obtained from automatic speech recognition (ASR) systems. To incorporate the acoustic similarity, we extend our previous grounding model, the scene-graph-based grounding network (SGGNet), with the ASR model from NVIDIA NeMo. We accomplish this by feeding the latent vector of speech pronunciations into the BERT-based grounding network within SGGNet. We evaluate the effectiveness of using latent vectors of speech commands in grounding through qualitative and quantitative studies. We also demonstrate the capability of SGGNet$^2$ in a speech-based navigation task using a real quadruped robot, RBQ-3, from Rainbow Robotics.Comment: 7 pages, 6 figures, Paper accepted for the Special Session at the 2023 International Symposium on Robot and Human Interactive Communication (RO-MAN), [Dohyun Kim, Yeseung Kim, Jaehwi Jang, and Minjae Song] contributed equally to this wor

Microscopic observation of a liquid-liquid-(semi)solid phase in polluted PM2.5

Atmospheric aerosol particles are complex mixtures having various physicochemical properties. To predict the role and characteristics of such complex aerosol particles in air pollution and related atmospheric chemistry, our knowledge of the number and types of phases in complex aerosol particles should be improved. However, most studies on the phase behavior of aerosol particles have been conducted in the laboratory and have not used real-world aerosol particles. In this study, using a combination of optical microscopy and poke-and-flow technique, we investigated the number and types of phases of actual aerosol particles of particulate matter < 2.5 µm (PM2.5) collected on heavily polluted days in Seosan, South Korea in winter 2020–2021. From the microscopic observations at 293 K, it showed that the PM2.5 particles exist in a single liquid phase at relative humidity (RH) >∼85%, a liquid-liquid phase at ∼70% < RH <∼85%, a liquid-liquid-(semi)solid phase at ∼30% < RH <∼70%, and a (semi)solid phase at RH <∼30% upon dehydration. This reveals that three phases of atmospheric aerosol particles coexisting as liquid-liquid and liquid-liquid-(semi)solid would be the most common phases in the atmosphere considering ambient RH ranges. These observations provide fundamental properties necessary for improved predictions of air quality and aerosol chemistry such as reactive uptake of N2O5, size distributions, and mass concentrations of aerosol particles

Model Predictive Control for an Aerial Manipulator Opening a Hinged Door

Aerial manipulation has been widely studied to be employed in various tasks such as exploration and transportation. To incorporate aerial manipulation into more sophisticated tasks like pulling or pushing a heavy cargo, an active interaction with surrounding structures should be considered. Unlike physical contact with a static structure which was mainly studied in previous papers, interaction with a movable structure requires a consideration of dynamics of the structure which makes the scenario more complex. In this paper, an aerial manipulator opening a hinged door is presented. Coupled dynamics between an aerial manipulator and a hinged door is derived, and a model predictive control (MPC) algorithm using iterative Linear Quadratic Regulator (iLQR) method for the derived dynamic equation is proposed. Through our proposed control strategy, sub-optimal state and input trajectories robust to model uncertainties while satisfying input constraints are generated. Our dynamic model and control algorithm are validated through simulations.N

Analysis of the Transformer Characteristics for an Integration System with a Wireless Power Transfer Device and Linear Motor

This paper proposed the transformer characteristic analysis method for the wireless power transfer (WPT) device and linear motor (LM) integration system that can be applied to industrial cleanroom transfer systems. A cable is required to supply the power in conventional systems. In comparison, the proposed system utilizes a WPT device that can simplify power transfers and make a better space utilization. The shape of the wireless power transmission system is proposed along with the discussion of the 2D FEA analysis method about the inductance analyzing method, which are important parameters in magnetic coupling. In addition, ferrite iron loss was calculated based on the analysis results, and applied to the entire modeling circuit to verify the validity of the measured and analyzed values. Finally, the proposed analysis method for the transformer coupling characteristics of the wireless power transfer combined with the transfer system is verified by experiments and simulations

Networked operation of a UAV using Gaussian process-based delay compensation and model predictive control

This study addresses an operation of unmanned aerial vehicles (UAVs) in a network environment where there is time-varying network delay. The network delay entails undesirable effects on the stability of the UAV control system due to delayed state feedback and outdated control input. Although several networked control algorithms have been proposed to deal with the network delay, most existing studies have assumed that the plant dynamics is known and simple, or the network delay is constant. These assumptions are improper to multirotor-type UAVs because of their nonlinearity and time-sensitive characteristics. To deal with these problems, we propose a networked control system using model predictive control (MPC) designed under the consideration of multirotor characteristics. We also apply a Gaussian process (GP) to learn an unknown nonlinear model, which increases the accuracy of path planning and state estimation. Flight experiments show that the proposed algorithm successfully compensates the network delay and Gaussian process learning improves the UAV's path tracking performance.N

Efficient networked UAV control using event-triggered predictive control

In this paper, we propose a method to improve the networked UAV control system using event-triggered control and model predictive control (MPC). Although the UAV control over the network has many advantages, it involves a long-time delay and packet loss, which adversely affect real-time control performance. Delay compensation algorithms in the networked control system (NCS) have been proposed to address such issues, however, they do not consider the resource limit of the network so that the network congestion may occur. In that case, the packet loss and network delay issues can even be worsened. In this study, we propose a method to reduce the generation of less important control signals and to use the network more efficiently by using event-triggered control. Since the event-triggered control method is also influenced by the network delay, an event trigger function suitable for NCS is designed. We validated the effectiveness of networked UAV control system and event-triggered control by simulation. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.N

Real-time Optimal Planning and Model Predictive Control of a Multi-rotor with a Suspended Load

This paper presents planning and control algorithms for a multi-rotor with a suspended load. The suspended load cannot be controlled easily by the multi-rotor due to severe dynamic coupling between them. Difficulties are exacerbated by under-actuated, highly nonlinear nature of multi-rotor dynamics. Although many studies have been proposed to plan trajectories and control this system, there exist only a few reports on real-time trajectory generation. With this in mind, we propose a planning method which is capable of generating collision-free trajectories real-time and applicable to a high-dimensional nonlinear system. Using a differential flatness property, the system can be linearized entirely with elaborately chosen flat outputs. Convexification of non-convex constraints is carried out, and concave obstacle-avoidance constraints are converted to convex ones. After that, a convex optimization problem is solved to generate an optimal trajectory, but semi-feasible trajectory which considers only some parts of the initial state. We apply model predictive control with a sequential linear quadratic solver to compute a feasible collision-free trajectory and to control the system. Performance of the algorithm is validated by flight experiment.N

Palladium-Catalyzed Asymmetric Nitrogen-Selective Addition Reaction of Indoles to Alkoxyallenes

A new palladium-catalyzed asymmetric addition reaction of indoles to alkoxyallenes is reported. Remarkably, the reaction showed complete regioselectivity toward the nitrogen. A new mechanism distinct from that of conventional π-allyl chemistry is proposed to explain this unique selectivity. The utility of the reaction is demonstrated by highly efficient and flexible synthesis of <i>N</i>-glycosylindoles