Determining multi-robot motion policies for persistently monitoring a region with limited sensing, communication, and localization constraints in non-GPS environments is a challenging problem. To take the localization constraints into account, in this paper, we consider a heterogeneous robotic system consisting of two types of agents: anchor agents with accurate localization capability and auxiliary agents with low localization accuracy. To localize itself, the auxiliary agents must be within the communication range of an anchor, directly or indirectly. The robotic team’s objective is to minimize environmental uncertainty through persistent monitoring. We propose a multi-agent deep reinforcement learning (MARL) based architecture with graph convolution called Graph Localized Proximal Policy Optimization (GALOPP), which incorporates the limited sensor field-of-view, communication, and localization constraints of the agents along with persistent monitoring objectives to determine motion policies for each agent. We evaluate the performance of GALOPP on open maps with obstacles having a different number of anchor and auxiliary agents. We further study 1) the effect of communication range, obstacle density, and sensing range on the performance and 2) compare the performance of GALOPP with area partition, greedy search, random search, and random search with communication constraint strategies. For its generalization capability, we also evaluated GALOPP in two different environments – 2-room and 4-room. The results show that GALOPP learns the policies and monitors the area well. As a proof-of-concept, we perform hardware experiments to demonstrate the performance of GALOPP.Note to Practitioners—Persistent monitoring is performed in various applications like search and rescue, border patrol, wildlife monitoring, etc. Typically, these applications are large-scale, and hence using a multi-robot system helps achieve the mission objectives effectively. Often, the robots are subject to limited sensing range and communication range, and they may need to operate in GPS-denied areas. In such scenarios, developing motion planning policies for the robots is difficult. Due to the lack of GPS, alternative localization mechanisms, like SLAM, high-accurate INS, UWB radio, etc. are essential. Having SLAM or a highly accurate INS system is expensive, and hence we use agents having a combination of expensive, accurate localization systems (anchor agents) and low-cost INS systems (auxiliary agents) whose localization can be made accurate using cooperative localization techniques. To determine efficient motion policies, we use a multi-agent deep reinforcement learning technique (GALOPP) that takes the heterogeneity in the vehicle localization capability, limited sensing, and communication constraints into account. GALOPP is evaluated using simulations and compared with baselines like random search, random search with ensured communication, greedy search, and area partitioning. The results show that GALOPP outperforms the baselines. The GALOPP approach offers a generic solution that be adopted with various other applications.

Bathymetry mapping of static water bodies like lakes is essential for sustainable ecosystem development strategies. However, bathymetry mapping using (i) traditional manual sampling approaches using single-beam echosounder (SBE) has large mapping errors and (ii) performing multi-beam survey is very expensive. Alternatively, performing lawn-mower SBE surveys is time consuming due to limited field-of-view. In order to address the above issues, in this paper, we present an on-the-way sampling approach with Gaussian Process Upper Confidence Bound (GPUCB) algorithm called as OAS-GPUCB that can adaptively sample the lake to minimize the bathymetry error while reducing the distance travelled to achieve a given mapping accuracy. We validate the proposed approach using simulations on actual lake bathymetry maps and also carry out real-world experiments using an Autonomous Surface Vehicle(ASV) with SBE. Further, we compare OAS-GPUCB to lawn-mower, GPUCB, and GPUCB with fixed radius approaches. The results consistently show that the proposed approach can achieve less than 10% bathymetry error while achieving distance reduction of more than 55%compared to the lawn-mower approach, and more than 90% less distance travelled compared to GPUCB and GPUCB with fixed radius approaches. The results shows the general applicability of OAS-GPUCB for bathymetry mapping of water bodies without any prior information maps.

Metaheuristics have become increasingly popular due to the complex and extensive optimization models that have emerged in the circular supply chain field. This study aims to fill a gap in the literature by conducting a comprehensive analysis of the role of metaheuristics in reverse logistics and circular supply chains. The methodology involves a meticulous review of 77 meticulously selected research articles published until 2023. The findings of our analysis indicate a significant increase in the number of publications in recent times. Through descriptive and content analyses, key themes emerge, including types of metaheuristics used, addressed supply chain issues, and circularity aspects. This study presents a novel approach to analyzing the application of metaheuristics from a circularity perspective. It also introduces a conceptual framework and suggests cluster-based future research directions. The results of the study hold valuable implications for researchers and other stakeholders in terms of understanding and advancement of the field.

Learning to read in a second language is challenging, but highly rewarding. For low-income children in developing countries, this task can be significantly more challenging because of lack of access to high-quality schooling, but can potentially improve economic prospects at the same time. A synthesis of research findings suggests that practicing recalling and vocalizing words for expressing an intended meaning could improve word reading skills - including reading in a second language - more than silent recognition of what the given words mean. Unfortunately, many language learning software do not support this instructional approach, owing to the technical challenges of incorporating speech recognition support to check that the learner is vocalizing the correct word. In this paper, we present results from a usability test and two subsequent experiments that explore the use of two speech recognition-enabled mobile games to help rural children in India read words with understanding. Through a working speech recognition prototype, we discuss two major contributions of this work: first, we give empirical evidence that shows the extent to which productive training (i.e. vocalizing words) is superior to receptive vocabulary training, and discuss the use of scaffolding hints to ""unpack"" factors in the learner's linguistic knowledge that may impact reading. Second, we discuss what our results suggest for future research in HCI.