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1. Backtracking Hybrid Α* for Resource Constrained Path Planning

   https://doi.org/10.2514/6.2022-1592  

   Ford, Bryce T. ; Aggarwal, Rachit ; Kumar, Mrinal ; Manyam, Satyanarayana G. ; Casbeer, David ; Grymin, David ( January 2022 , AIAA SCITECH 2022 Forum)

   This paper considers resource constrained path planning for a Dubins agent. Resource constraints are modeled as path integrals that exert a path-dependent load on the agent that must not exceed an upper bound. A backtracking mechanism is proposed for the Hybrid-A* graph search algorithm to determine the minimum time path in the presence of the path loading constraint. The new approach is built on the premise that inadmissibility of a node on the graph must depend on the loading accumulated along the path taken to arrive at its location. Conventional hybrid-A* does not account for this fact, causing it to become suboptimal or even infeasible in the presence of resource constraints. The new approach helps "reset'' the graph search by backing away from a node when the loading constraint is exceeded, and redirecting the search to explore alternate routes to arrive at the same location, while keeping the path load under its stipulated threshold. Backtracking Stopping criterion is based on relaxation of the path load along the search path. Case studies are presented and numerical comparisons are made with the Lagrange relaxation method to solving equivalent resource-constrained shortest path problems. 

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2. ECHO: Empirical Characterization and Height Optimization of UAV-to-Underground Channels

   Hashir, S.M. ; Camp, J ; Vuran, M.C. ( September 2023 , IEEE IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC))

   This paper explores the nexus of two emerging Internet of Things (IoT) components in precision agriculture, which requires vast amounts of agriculture fields to be monitored from air and soil for food production with efficient resource utilization. On the one hand, unmanned aerial vehicles (UAVs) have gained interest in agricultural aerial inspection due to their ubiquity and observation scale. On the other hand, agricultural IoT devices, including buried soil sensors, have gained interest in improving natural resource efficiency in crop production. In this work, the path loss and fading characteristics in wireless links between a UAV and underground (UG) nodes (Air2UG link) are studied to design a UAV altitude optimization solution. A path loss model is developed for the Air2UG link, including fading in the channel, where fading is modeled using a Rician distribution and validated using the Kolmogorov-Smirnov test. Moreover, Rician-K is found to be dependent on the UAV altitude, which is modeled with a Gaussian function with an RMSE of 0.4-1.3 dB. Furthermore, a novel altitude optimization solution is presented to minimize the bit error rate (BER). Results show that the lowest possible altitude does not always minimize the BER. Optimizing the altitude reduces the Air2UG link BER by as much as 8.6-fold. Likewise, altitude optimization can minimize the impacts of increasing burial depth on the BER. Our results and analysis are the first in this field and can be exploited to optimize the altitude and resources of a UAV node to communicate with the sensors embedded in the soil efficiently. 

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3. A Path Planning Algorithm for Collective Monitoring Using Autonomous Drones

   https://doi.org/10.1109/CISS.2019.8693023  

   Islam, Shafkat ; Razi, Abolfazl ( March 2019 , 53rd Annual Conference on Information Sciences and Systems (CISS))

   This paper presents a novel mission-oriented path planning algorithm for a team of Unmanned Aerial Vehicles (UAVs). In the proposed algorithm, each UAV takes autonomous decisions to find its flight path towards a designated mission area while avoiding collisions to stationary and mobile obstacles. The main distinction with similar algorithms is that the target destination for each UAV is not apriori fixed and the UAVs locate themselves such that they collectively cover a potentially time-varying mission area. One potential application for this algorithm is deploying a team of autonomous drones to collectively cover an evolving forest wildfire and provide virtual reality for firefighters. We formulated the algorithm based on Reinforcement Learning (RL) with a new method to accommodate continuous state space for adjacent locations. To consider a more realistic scenario, we assess the impact of localization errors on the performance of the proposed algorithm. Simulation results show that the success probability for this algorithm is about 80% when the observation error variance is as high as 100 (SNR:-6dB). 

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4. Agent models of customer journeys on retail high streets

   https://doi.org/10.1007/s11403-022-00350-z  

   Torrens, Paul M. ( May 2022 , Journal of Economic Interaction and Coordination)

   In this review paper, we aim to make the case that a concept from retail analytics and marketing—thecustomer journey—can provide promising new frameworks and support for agent-based modeling, with a broad range of potential applications to high-resolution and high-fidelity simulation of dynamic phenomena on urban high streets. Although not the central focus of the review, we consider agent-based modeling of retail high streets against a backdrop of broader debate about downtown vitality and revitalization, amid a climate of economic challenges for brick-and-mortar retail. In particular, we consider how agent-based modeling, supported by insights from consideration of indoor shopping, can provide planning and decision support in outdoor high street settings. Our review considers abstractions of customers through conceptual modeling and customer typology, as well as abstractions of retailing as stationary and mobile. We examine high-level agency of shop choice and selection, as well as low-level agency centered on perception and cognition. Customer journeys are most often trips through geography; we therefore review path-planning, generation of foot traffic, wayfinding, steering, and locomotion. On busy high streets, journeys also manifest within crowd motifs; we thus review proximity, group dynamics, and sociality. Many customer journeys along retail high streets are dynamic, and customers will shift their journeys as they come into contact with experiences and service offerings. To address this, we specifically consider treatment of time and timing in agent-based models. We also examine sites for customer journeys, looking in particular at how agent-based models can provide support for the analysis of atmospherics, artifacts, and location-based services. Finally, we examine staff-side agency, considering store staff as potential agents outdoors; and we look at work to build agent-based models of fraud from customer journey analysis.

    

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5. Evaluation of Direct Collocation Optimal Control Problem Formulations for Solving the Muscle Redundancy Problem

   https://doi.org/10.1007/s10439-016-1591-9  

   De Groote, Friedl ; Kinney, Allison L. ; Rao, Anil V. ; Fregly, Benjamin J. ( January 2016 , Annals of Biomedical Engineering)

   Estimation of muscle forces during motion involves solving an indeterminate problem (more unknown muscle forces than joint moment constraints), frequently via optimization methods. When the dynamics of muscle activation and contraction are modeled for consistency with muscle physiology, the resulting optimization problem is dynamic and challenging to solve. This study sought to identify a robust and computationally efficient formulation for solving these dynamic optimization problems using direct collocation optimal control methods. Four problem formulations were investigated for walking based on both a two and three dimensional model. Formulations differed in the use of either an explicit or implicit representation of contraction dynamics with either muscle length or tendon force as a state variable. The implicit representations introduced additional controls defined as the time derivatives of the states, allowing the nonlinear equations describing contraction dynamics to be imposed as algebraic path constraints, simplifying their evaluation. Problem formulation affected computational speed and robustness to the initial guess. The formulation that used explicit contraction dynamics with muscle length as a state failed to converge in most cases. In contrast, the two formulations that used implicit contraction dynamics converged to an optimal solution in all cases for all initial guesses, with tendon force as a state generally being the fastest. Future work should focus on comparing the present approach to other approaches for computing muscle forces. The present approach lacks some of the major limitations of established methods such as static optimization and computed muscle control while remaining computationally efficient. 

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