Enhanced Object Matching and Identification for Multi-Robot Systems in Complex Environments

For missions in Safety, Security, and Rescue Robotics (SSRR) maps are often a core deliverable. Hence it is of high interest to assess the quality of maps in a simple and efficient way. Since SSRR is mostly taking place in unstructured environments, 3D mapping has become more and more important. Here a method to evaluate the quality of 3D maps is presented that extends the previously developed 2D Fiducial approach to the third dimension. The artificial features are identified and located in 2D cross-sections of the map as well as in the 3D maps. It is then attempted to proof this concept using a ground truth map and robot generated maps from the RoboCup Rescue competition 2013 in Eindhoven.

2018 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR)

Editorial: Special Issue on Safety, Security, and Rescue Robotics (SSRR), Part 2

<div class="">In this work, we demonstrate the importance of High Performance Computing (HPC) and Urgent Computing (UC) in rapid disaster response to man-made catastrophes. Using the specific case study of a potential dam breach at the Kyiv cistern reservoir in Ukraine, we show how these technologies can be used to assess the potential hazards and impacts of such an event.</div> <div class=""> </div> <div class="">The Copernicus Emergency Management Service Risk & Recovery Mapping service was activated to derive hazard assessment mapping for the protection of citizens and infrastructure in the event of a potential dam breach. The Kiev Hydroelectric Power Plant, located upstream of Kiev, has been the target of a previous rocket attack, and the threat of another successful attack cannot be ruled out. <span class="">The potential consequences of a dam breach are severe and include flooding and the erosion and transport of radioactive sediment.</span><span class=""> </span></div> <div class=""> </div> <div class="">To determine the impact of a potential dam breach and predict its effects on citizens and infrastructure, we used the Dambreak-HySEA model, which is part of the HySEA suite. This model is specifically designed to accurately reproduce the evolution of wet/dry fronts in geophysical flows such as river flooding, flooding in rural and urban areas, and dam failures. Developed by the research group EDANYA of the University of Malaga, the model was implemented using Graphics Processor Units (GPUs) with CUDA, resulting in a significant speed-up compared to a traditional CPU implementation.</div> <div class=""> </div> <div class="">The use of HPC resources, specifically those provided by the Spanish Network for Supercomputing (RES) program Urgent computing for citizen problems, was crucial in obtaining the results within the 10-day delivery time frame with the simulation of four scenarios required by the activation: partial and complete breakage of the Kyiv Dam and the same scenarios for the Irpin Dam. These simulations were computationally complex, requiring the solution of nearly 700 million computational cells per scenario.</div> <div class=""> </div> <div class="">Overall, this study highlights the importance of utilizing HPC and UC in disaster response and risk management. By utilizing advanced numerical models and computational resources, we can more accurately predict the potential hazards and impacts of man-made catastrophes such as dam breaches, and take necessary measures to protect citizens and infrastructure.</div>

As mobile robots are becoming more and more widely used, it is of great significance to design an efficient path planning method for multi-robot systems (MRS) that can adapt to complex and unknown environments. In this paper, we present a deep reinforcement learning (DRL) based method to navigate the MRS with collision avoidance in unknown dynamic environments, which consists of centralized learning and decentralized executing paradigm. The proposed policy maps original laser information into robot control commands without constructing global maps. The learned policy is tested in Gazebo environments with three robot systems, which shows the effective performance in terms of success rate, extra time rate, and formation maintenance rate.

<div class="section abstract"><div class="htmlview paragraph">In the domain of advanced driver assistance systems and autonomous vehicles, precise perception and interpretation of the vehicle's environment are not merely requirements they are the very foundation upon which every aspect of functionality and safety is constructed. One prevalent method of representing the environment is through the use of an occupancy grid map. This map segments the environment into distinct grid cells, each of which is evaluated to determine if it is occupied or free. This evaluation operates under the assumption that each grid cell is independent of the others. The underlying mathematical structure of this system is the binary Bayes filter (BBF). The BBF integrates sensor data from various sources and can incorporate measurements taken at different times. The occupancy grid map does not rely on the identification of individual objects, which allows it to depict obstacles of any shape. This flexibility is a key advantage of this approach. Traditional occupancy grid maps fall short when it comes to predicting dynamic environments due to their lack of a process model. A notable enhancement to this static model is the Bayesian Occupancy Filter (BOF), which, unlike its predecessor, estimates a velocity distribution for each grid cell's occupancy using a histogram filter. However, the BOF's computational demands are high. To address this, research propose representing the dynamic state of grid cells using particles. This method enables the computation of dynamic grid maps in real-time applications, even with larger grid cell sizes and higher resolution. Despite these advancements, dynamic occupancy grid maps remain a relatively new field of study, especially when compared to more established object-tracking approaches. Until now, the BOF has been treated as a distinct research area with minimal overlap with other tracking methodologies. This methodology aims to bridge that gap and foster a more integrated approach to dynamic environment estimation.</div><div class="htmlview paragraph">This study introduces a novel approach to dynamic grid mapping, conceptualized as an approximation of a Random Finite Set (RFS) filter. An RFS is a probabilistic representation of a finite, random collection of objects and their respective states. Finite Set Statistics (FISST) provide a framework for Bayesian filtering of these random finite sets and form the foundation for several multi-object tracking methodologies, such as the Probability Hypothesis Density (PHD) filter. By characterizing the grid as an RFS, we can apply sophisticated concepts from the well-established domain of RFS filtering to dynamic grid mapping. The research develops a filter known as the Probability Hypothesis Density/Multi-Instance Bernoulli (PHD/MIB) filter. This filter alternately represents and propagates the dynamic grid map as a PHD and as multiple instances of Bernoulli filters, thereby offering a more integrated and efficient approach to dynamic environment estimation.</div><div class="htmlview paragraph">Furthermore, this research introduces a Sequential Monte Carlo (SMC) implementation of the PHD/MIB filter, as well as an approximation within the Dempster-Shafer framework, termed the Dempster-Shafer PHD/MIB (DS-PHD/MIB) filter. This DS-PHD/MIB filter necessitates fewer particles than the original PHD/MIB filter, thereby enhancing computational efficiency. The study provides a comprehensive description of an efficient, massively parallel implementation of the DS-PHD/MIB filter. The algorithm's pseudo code is also outlined, offering a clear and concise understanding of its workings. This approach further strengthens the integrated and efficient methodology for dynamic environment estimation.</div><div class="htmlview paragraph">In conclusion, the research delineates the attributes of the DS-PHD/MIB filter and debates its pros and cons in comparison to object-based tracking methodologies, using practical examples for illustration. A quantitative assessment using real-world data demonstrates that the DS-PHD/MIB filter yields consistent state estimation outcomes. It effectively models both the stochastic multi-object transition process and the stochastic multi-object observation process. Moreover, the evaluation affirms the real-time capability of the parallelized implementation of the DS-PHD/MIB filter. It validates its utility for state estimation in dynamic vehicle environments, thereby underscoring its potential for practical applications in dynamic environment estimation. This comprehensive approach offers a promising avenue for future research and development in this field.</div></div>

Telemedicine has been introduced as a new and effective method in dealing with public health challenges, improving access to health care, and reducing healthcare costs in today's world. In order to solve the problems of a single access network and the unknown network status of the telemedicine system in complex environments, a multi-link aggregation algorithm based on link priority (MLA-LP) was proposed. Firstly, the network parameters, such as packet loss rate (PLR) and round-trip time (RTT), of each link were obtained, and then MLA-LP was realized by using the congestion control algorithm based on sender and the traffic allocation algorithm based on link priority. Finally, the MLA-LP algorithm was tested on the test platform based on HoloWAN 1200 network simulator. The comparison algorithm was the multilink aggregation algorithm based on the weightedround-robin algorithm (MLA-WRR). MLA-LP algorithm has higher uplink throughput than MLA-WRR under the same link PLR and delay. When the link is suddenly abnormal (burst packet loss, delay, etc.), the MLA-LP algorithm could quickly avoid the abnormal link and transfer traffic within 2 s; In the mobile scenario, compared with a single network and MLA-WRR algorithm, MLA-LP algorithm has the best video transmission effect, with an average delay of 136 ms, an average bit rate of 2.31 Mbps, an average PLR of 0.04%, and video jamming times close to 0. The MLA-LP algorithm could effectively aggregate link bandwidth and quickly avoid abnormal links, ensuring the robustness of the access network.In order to solve the problems of a single access network and the unknown network status of telemedicine system in complex environment, a MLA-LP was proposed. MLA-LP algorithm has higher uplink throughput and could quickly avoid the abnormal link and transfer traffic within 2 s, ensuring the robustness of the access network.

<div class="section abstract"><div class="htmlview paragraph">Now-a-days, Advanced driver-assistance systems (ADAS) is equipping cars and drivers with advance information and technology to make them become aware of the environment and handle potential situations in better way semi-autonomously. High-quality training and test data is essential in the development and validation of ADAS systems which lay the foundation for autonomous driving technology.</div><div class="htmlview paragraph">ADAS uses the technology like radar, vision and combinations of various sensors including LIDAR to automatize dynamic driving tasks like steering, braking, and acceleration of vehicle for controlled and safe driving. And to integrate these advance technologies, the ADAS needs labeled data to train the algorithm to detect the various objects and moments of driver. Image annotation is one the well-known service to create such training data for computer vision.</div><div class="htmlview paragraph">There are number of open source annotated datasets available viz. COCO, KITTI etc. But these datasets are limited to either US or European road environment scenarios. There is hardly any dataset available for Indian Specific road conditions. In order to capture all the India Specific objects on road, we are capturing data through various environmental conditions, road conditions and annotating objects on the collected camera images. 2-D bounding boxes are being used to annotate objects in an image.</div><div class="htmlview paragraph">These annotated images will then be used to train a machine learning model. Finally this trained model will be then used to detect Indian specific objects with real time data acquired through camera sensors mounted on the vehicle. Object detection is one of the prime aspects for ADAS functionality. The complete process will demonstrate end to end solution for object detection techniques from data generation to real-time object identification and recognition.</div></div>

Facing the implementation problems such like low growth reward, long training time, and poor stability of the multiagent learning methods when dealing with complex environment and more agents, this paper proposes a fast optimal coordination method for multiagent in complex environment (FOC-MACE). Firstly, the environment exploration strategy is introduced into the policy network based on the MADDPG method for higher growth rewards. Then, the parallel computing technology is adopted in the critic network, in purpose to effectively reduce the training time. These tactics together are beneficial to enhance the stability of multiagent learning. Lastly, the optimal resource allocation is carried out to realize optimal coevolution of the multiagents and further improve the learning ability of the agents’ group. To verify the effectiveness of our proposal, the FOC-MACE is compared with several advanced methods at current stage in the MPE environment. Three different experiments prove that by using our method, the growth reward is increased by up to 37.1%, the training is speed up significantly, and the stability of the method, which represented by standardized variance, is also improved. In addition, this paper validated the fast optimal coordination method for multiagent systems in the context of UAV scenarios, demonstrating the practical performance of the approach. Through comprehensive experiments and scenario validations, the study successfully confirmed the effectiveness of the proposed fast optimal coordination method for multiagent systems in complex environments.

<div class="section abstract"><div class="htmlview paragraph">This paper presents an integrated modeling approach for real-time discretionary lane-changing decisions by autonomous vehicles, aiming to achieve human-like behavior. The approach incorporates a two-player normal-form game and a novel risk field method. The normal-form game represents the strategic interactions among traffic participants. It captures the trade-offs between lane-changing benefits and risks based on vehicle motion states during a lane change. By continuously determining the Nash equilibrium of the game at each time step, the model decides when it is appropriate to change the lane. A novel risk field method is integrated with the game to model risks in the game pay-offs. The risk field introduces regions along the desired target lane with different time headway ranges and risk weights, capturing traffic participants' complex risk perceptions and considerations in lane-changing scenarios. It goes beyond simple gap acceptance assumptions used in previous studies, providing more human-like risk estimations. Discretionary lane-changing data from human drivers extracted from the NGSIM I80 dataset were employed to calibrate the integrated model for human-like lane-change decisions. The calibration results demonstrate the high prediction accuracy of the proposed model compared to previous studies. The calibrated risk field parameters in the model provide interpretability and contribute to a deeper understanding of human lane-changing decisions. The proposed model also exhibits improved consistency in lane-changing decisions within a continuous time range around the lane-crossing moment. It outperforms previous game-theoretic models that rely on acceleration and time pay-offs with specific assumptions about future vehicle motions. Several case studies were carried out in the co-simulations of CARLA and SUMO software and based on the NGSIM dataset samples. The model's ability to produce reliable and interpretable lane-changing decisions enhances autonomous vehicles' overall safety and user experience.</div></div>

<div class="htmlview paragraph">The human element is perhaps one of the most critical challenges presented by the Space Exploration Initiative (SEI). Whether living on the surface of the Moon or traveling to the planetary surface of Mars, crews will have to depend upon one another for long periods of time and live and work together in extremely isolated, confined, and high risk environments. How does one select crews for such conditions and what training is required? How can stress be monitored and what interventions are necessary to prevent behavioral and psychological problems? The best analogs for understanding crew dynamics and performance may be Antarctic expeditions and undersea experiences. Another important source of information is the Soviet long-duration space experiences. This paper reviews some of the available information from U.S. and Soviet spaceflight experiences to gain insights on selecting crews and maintaining productivity for the human exploration missions.</div>

Accurately describing the electronic properties of heavy-element molecular systems in complex environments is essential for advancing technologies such as optoelectronics and solar cells. However, achieving accurate predictions remains challenging because both relativistic and electron correlation effects must be considered equally, along with interactions involving other species in the complex environment (e.g., solvent). This paper extends our real-time time-dependent Dirac-Kohn-Sham (rt-TDDKS) implementation in PyBERTHA-RT to include environmental effects using the "uncoupled" Frozen-Density-Embedding (FDE) scheme, where only the active subsystem evolves dynamically in time. This adaptation utilizes existing FDE functionality within the PyEmbed module of the PyADF scripting framework. The native Python APIs of PyBERTHA-RT and PyADF provide an ideal environment for development, enhancing readability and reusability. We demonstrate that the FDE potential maintains the numerical stability of the active subsystem's density matrix propagation. Illustrative results for lead halides (PbCl2 and PbI2) in γ-butyrolactone (GBL) solution show the effects of increasing solvent molecules on absorption spectra. This case study demonstrates the new implementation's applicability to realistic systems, offering a basis for studying electron dynamics in heavy-element molecules in complex environments under linear and nonlinear regimes, relevant to perovskite precursor chemistry.

In this paper, we apply deep reinforcement learning (DRL) to solve the flocking control problem of multi-robot systems in complex environments with dynamic obstacles. Starting from the traditional flocking model, we propose a DRL framework for implementing multi-robot flocking control, eliminating the tedious work of modeling and control designing. We adopt the multi-agent deep deterministic policy gradient (MADDPG) algorithm, which additionally uses the information of multiple robots in the learning process to better predict the actions that robots will take. To address the problems such as low learning efficiency and slow convergence speed of the MADDPG algorithm, this paper studies a prioritized experience replay (PER) mechanism and proposes the Prioritized Experience Replay-MADDPG (PER-MADDPG) algorithm. Based on the temporal difference (TD) error, a priority evaluation function is designed to determine which experiences are sampled preferentially from the replay buffer. In the end, the simulation results verify the effectiveness of the proposed algorithm. It has a faster convergence speed and enables the robot group to complete the flocking task in the environment with obstacles.

Aiming at the problem that obstacle avoidance flexibility and formation integrity are difficult to coexist in multi-robot formation motion, a path-deformation mapping mechanism is proposed, which deeply integrates artificial potential field and affine transformation, and drives formation adaptive adjustment in real time through path information. By using the non-uniform scaling characteristics of the affine transformation, the limitation of traditional conformal transformation is broken through, and the unity of flexibility and integrity is realized. The effectiveness of the algorithm is verified by experiments, which provide a practical solution for cooperative obstacle avoidance of multi-robot systems in complex environments. In order to verify the performance of the algorithm, a numerical simulation is carried out, and an experimental platform composed of seven omnidirectional mobile robots is built for physical verification. The simulation and experimental results show that the formation can complete the obstacle avoidance task in the complex static obstacle environment, and the average formation tracking error is maintained below 0.05 m. Compared with the traditional local obstacle avoidance or formation switching method, this algorithm significantly improves the fluency of the obstacle avoidance process and the integrity of the formation while ensuring a success rate of 100% obstacle avoidance.

Rapid advances in energy applications require new theory and computational models to provide guidance for interpretation of experimental results and mechanistic understanding. New theory development is necessary to treat systems of increasing complexity, size, and relevance to real applications. Functional transition metal compounds, including molecules, clusters, nanoparticles, surfaces, and solids, provide particular promise for magnetic, optical, and catalytic applications. However, such systems can be exceptionally challenging to model. To make progress in understanding and designing transition metal compounds for energy applications, theory must be able to simulate such systems in complex environments, as well as simulate the spectra of such complex systems to provide direct connections with experiment. Leveraging the independent expertise of the team’s members, this project aimed to make inroads to the theoretical and computational challenges associated with studying transition metal compounds, their reaction chemistry, photophysics and photochemistry, and response to spectroscopic interrogation.