Payload Constraints Research Articles

Imaging-based measurement of lunar dust velocity and particle size.

This paper introduces an optical-mechanical system designed for the dynamic detection and analysis of lunar dust, typically characterized as particles under 20micrometers on the lunar surface. The system's design is both compact and lightweight, aligning with the payload constraints of lunar exploration missions. It is capable of real-time tracking and recording the motion of lunar dust at various altitudes, a crucial capability for understanding the environmental dynamics of the lunar surface. By capturing images and applying sophisticated algorithms, the system accurately measures the velocity and size of dust particles. This approach significantly advances the quantitative analysis of lunar dust, especially during agitation events, filling a critical gap in our current understanding of lunar surface phenomena. The insights gained from this study are not only pivotal for developing theoretical models of lunar surface air flow disturbances and dust movement but also instrumental in designing effective dust mitigation and hazard avoidance strategies for future lunar missions, thereby enhancing both scientific knowledge and the engineering applications in lunar exploration.

Dispatching point selection for a drone-based delivery system operating in a mixed Euclidean–Manhattan grid

AbstractIn this paper, we present a drone-based delivery system that assumes to deal with a mixed-area, i.e., two areas, one rural and one urban, placed side-by-side. In the mixed-areas, called EM-grids, the distances are measured with two different metrics, and the shortest path between two destinations concatenates the Euclidean and Manhattan metrics. Due to payload constraints, the drone serves a single customer at a time returning back to the dispatching point (DP) after each delivery to load a new parcel for the next customer. In this paper, we present the $$1$$ 1 -Median Euclidean–Manhattan grid Problem (MEMP) for EM-grids, whose goal is to determine the drone’s DP position that minimizes the sum of the distances between all the locations to be served and the point itself. We study the MEMP on two different scenarios, i.e., one in which all the customers in the area need to be served (full-grid) and another one where only a subset of these must be served (partial-grid). For the full-grid scenario we devise optimal and approximation algorithms, while for the partial-grid scenario we devise an optimal algorithm.

Route planning of heterogeneous unmanned aerial vehicles under recharging and mission time with carrying payload constraints

Purpose: We consider the problem of route planning of multiple rechargeable heterogeneous UAVs with multiple trips under mission time and payload carrying constraints. The goal is to determine the types and number of UAVs to be deployed and their flying paths that minimizes the monetary cost, which is a sum of the recharging energy cost of each UAV, the UAV rental cost, and the cost of violating the mission time deadline.Design/methodology/approach: The problem is formulated as a mixed integer programming (MIP). Then, the genetic algorithm (GA) is developed to solve the model and the solutions are compared to those obtained from the exact method (Branch-and-Bound). Novel chromosome encoding and population initializations are designed, and standard procedures for crossover and mutation are adapted to this work. Test problems on grid networks and real terrains are used to evaluate the runtime efficiency and solution optimality, and the sensitivity of GA parameters is studied based on two-level factorial experiments.Findings: The proposed GA method can find optimal solutions for small problem sizes but with much less computation time than the exact method. For larger problem sizes, the exact method failed to find optimal solutions within the limits of time and disk space constraints (24 hours and 500 GB) while the GA method yields the solutions within a few minutes with as high as 49% better objective values. Also, the proposed GA method is shown to well explore the solution space based on the variation of the total costs obtained.Originality/value: The unique aspects of this work are that the model optimizes the sum of three different costs – the electricity recharging cost, the UAV rental cost, the penalty cost for mission deadline violation, and the recharging period based on the remaining energy, the payload capacity, and the heterogeneity of UAVs are incorporated into the model. The model is formulated as a mixed integer programming and the genetic algorithm is developed to solve the program. Novel chromosome encoding and population initializations are designed, and standard procedures for crossover and mutation are adapted to this work.

Compact and automated eDNA sampler for in situ monitoring of marine environments

Using environmental DNA (eDNA) to monitor biodiversity in aquatic environments is becoming an efficient and cost-effective alternative to other methods such as visual and acoustic identification. Until recently, eDNA sampling was accomplished primarily through manual sampling methods; however, with technological advances, automated samplers are being developed to make sampling easier and more accessible. This paper describes a new eDNA sampler capable of self-cleaning and multi-sample capture and preservation, all within a single unit capable of being deployed by a single person. The first in-field test of this sampler took place in the Bedford Basin, Nova Scotia, Canada alongside parallel samples taken using the typical Niskin bottle collection and post-collection filtration method. Both methods were able to capture the same aquatic microbial community and counts of representative DNA sequences were well correlated between methods with R^{2} values ranging from 0.71–0.93. The two collection methods returned the same top 10 families in near identical relative abundance, demonstrating that the sampler was able to capture the same community composition of common microbes as the Niskin. The presented eDNA sampler provides a robust alternative to manual sampling methods, is amenable to autonomous vehicle payload constraints, and will facilitate persistent monitoring of remote and inaccessible sites.

Attitude control actuator scaling laws for orbiting solar reflectors

The rapid evolution of in-orbit manufacturing will enable the fabrication of low-cost, large-scale space structures. In particular, the use of 3D printing technologies will remove traditional payload constraints associated with launch vehicles, due to fairing size and launch loads, thus allowing the construction of larger and lighter structures, such as orbiting solar reflectors. These structures will require efficient attitude control systems, able to provide the necessary torque for maneuvers and to counteract perturbations, such as gravity gradient and solar radiation pressure. In this paper, a top-level overview of actuator performances for orbiting solar reflectors is provided, and scaling laws associated with the required actuator mass and input power are developed. For each class of actuator, upper bounds on the maximum size of the structure which can be effectively controlled are presented. The results can also be extended to other classes of large planar Earth-pointing structures such as solar power satellites, solar sails, or large antennae.

Visual-Inertial Odometry With Online Calibration of Velocity-Control Based Kinematic Motion Models

Visual-inertial odometry (VIO) is an important technology for autonomous robots with power and payload constraints. In this paper, we propose a novel approach for VIO with stereo cameras which integrates and calibrates the velocity-control based kinematic motion model of wheeled mobile robots online. Including such a motion model can help to improve the accuracy of VIO. Compared to several previous approaches proposed to integrate wheel odometer measurements for this purpose, our method does not require wheel encoders and can be applied when the robot motion can be modeled with velocity-control based kinematic motion model. We use radial basis function (RBF) kernels to compensate for the time delay and deviations between control commands and actual robot motion. The motion model is calibrated online by the VIO system and can be used as a forward model for motion control and planning. We evaluate our approach with data obtained in variously sized indoor environments, demonstrate improvements over a pure VIO method, and evaluate the prediction accuracy of the online calibrated model.

Distributed Resource Optimization for NOMA Transmission in Beamforming SATCOM

This work studies the application of nonorthogonal transmission in beamforming (BF) based forward links for next-generation satellite communication (SATCOM) with multiple gateways. With the aim of enhancing the throughput of BF SATCOM systems, the state-of-the-art nonorthogonal multiple access (NOMA) technique is exploited by serving multiple users per beam in the same time slot. In this regard, the feeder link limitations and multibeam satellite payload constraints must be considered for BF design and power allocation (PA) optimization in nonorthogonal SATCOM. To address these challenges, distributed resource optimization strategies are investigated for BF and flexible payload power resource allocation in multigateway (multi-GW) nonorthogonal SATCOM systems. Specifically, a per-feed available power-constrained BF strategy via maximization of the worst-user signal-to-leakage-and-noise ratio (SLNR) is explored with local channel state information (CSI) for a distributed operation of GWs. As an upper-bound performance limit, a centralized multilayer BF strategy is processed in a central unit with full global CSI and data sharing. After the BF direction optimization, a weighted sum-rate maximization-based (WSRM-based) power resource optimization strategy is locally applied at each GW to efficiently use the power resources for higher performance increment. The nonconvex WSRM problem, under the constraints of the practical satellite payload power budget, successful successive interference cancellation (SIC) decoding, and minimum data rate, is recast into an equivalent weighted sum-MSE minimization (WMMSE) counterpart for a tractable solution. Finally, an efficient user scheduling is designed to enable the operator to capture a substantial system-throughput gain. Accurate simulations are conducted with the near-to-real coverage area (footprints), the random distributions of users, and interference, relying on geographical locations of users. The results over a realistic simulation environment show the efficiency of our strategies.

Comparison of lunar and Martian regolith simulant-based geopolymer cements formed by alkali-activation for in-situ resource utilization

Future human space exploration and habitation on the lunar and Martian surfaces necessitates in-situ resource utilization (ISRU) for the development of construction materials tailored for infrastructure and environmental protection. Here we explore the use of lunar and Martian regoliths to create construction materials with properties suitable for such structures as landing pads. Alkali activation of a spectrum of lunar and Martian regolith simulants generates geopolymer binders under ambient and vacuum curing conditions as well as exposure to extreme high and low temperatures (600 and −80 °C). Compressive strength is reduced for binders prepared from each simulant after curing under vacuum and exposure to sub-zero temperatures. In lunar simulant binders, the compressive strength is increased after heating to 600 °C, but the opposite effect is observed in the Martian simulant binder. Amorphous aluminosilicate content and percentage of small particles in the simulants are hypothesized to have a positive impact on compressive strength under ambient curing. Iron and magnesium content may be responsible for decreased compressive strength of the Martian binder after heating. This study offers a robust framework for comparing performance of different simulants under the same curing protocols and environmental exposures, as well as offering insight as to the effects of vacuum curing, and exposure to high and low temperature environments on cured binder samples. Developing a landing pad by transporting activator to the lunar surface is shown to be conceptually feasible within current payload constraints.

Ground Vehicle and UAV Collaborative Routing and Scheduling for Humanitarian logistics using Random Walk Based Ant Colony Optimization

A well-planned humanitarian logistics involving rescuing people and providing on-time lifesaving facilities to disaster-affected areas can significantly mitigate the aftermath of disasters. However, damaged bridges and blocked roads can hinder last-mile deliveries in disaster-affected areas by ground vehicles only. So, in this paper, we propose a ground vehicle (GV) and unmanned air vehicle (UAV) collaborative delivery system in such areas. Here, a fleet of homogenous ground vehicles each equipped with a certain number of UAVs is deployed for last-mile deliveries. UAVs make the flight from GVs, deliver to end locations and return to the GV for battery replacement and/or start another flight. The objective of the model is to minimize the total delivery time within UAV flight endurance and payload constraints. Firstly K-means clustering algorithm has been used to cluster the disaster-affected region into different sectors. Then GV_Touring and UAV_Routing have been scheduled using nearest neighbor heuristic to serve ground approachable locations and UAV served locations respectively. Finally, the random walk based ant colony optimization-based (ACS_RW) has been developed to further optimize the overall travel time. Experimentation results show the potential benefits of the proposed algorithm over other available truck-drone collaborative transportation models.

AEMB: An Automated Exploit Mitigation Bypassing Solution

Modern operating systems set exploit mitigations to thwart the exploit, which has also become a barrier to automated exploit generation (AEG). Many current AEG solutions do not fully account for exploit mitigations, and as a result, they are unable to accurately assess the exploitability of vulnerabilities in such settings.This paper proposes AEMB, an automated solution for bypassing exploit mitigations and generating useable exploits (EXPs). Initially, AEMB identifies exploit mitigations in the system based on characteristics of the program execution environment. Then, AEMB implements exploit mitigations bypassing the payload generation by modeling expert experience and constructs the corresponding constraints. Next, during the program’s execution, AEMB uses symbol execution to collect symbol information and create exploit constraints. Finally, AEMB utilizes a solver to solve the constraints, including payload constraints and exploit constraints, to generate the EXP. In this paper, we evaluated a prototype of AEMB on six test programs and seven real-world applications. Furthermore, we conducted 54 sets of experiments on six different combinations of exploit mitigations. Experiment results indicate that AEMB can automatically overcome exploit mitigations and produce successful exploits for 11 out of 13 applications.

Multi-robot task allocation in disaster response: Addressing dynamic tasks with deadlines and robots with range and payload constraints

This paper tackles a class of multi-robot task allocation (MRTA) problems called “Single-Task Robots and Single-Robot Tasks” or SR–ST problems, subject to the following additional characteristics: tasks with deadlines, tasks that are generated during the mission, and robots with range and payload constraints (thus requiring multiple tours per robot). While these characteristics are typical of various disaster response and commercial applications, there is a lack of online MRTA solutions to address them. To solve this class of complex MRTA problems, an efficient online method (which is also suitable for decentralized deployment) is developed based on the construction and weighted matching of bipartite graphs. An exact integer linear programming (ILP) formulation of this class of MRTA problems is also developed, the solution of which serves both as an offline MRTA approach and as a provably optimal benchmark against which the online method is compared. The new methods are applied to a flood response problem where multiple unmanned aerial vehicles must respond to victims spread out over a large area. The results show that the new online algorithm closely trails the offline ILP method in terms of task completion performance, while being >103 times more computationally efficient compared to the ILP method. Dedicated case studies provide further insights into the favorable scalability of the online method with an increasing number of UAVs — offering up to 46% higher task completion compared to a random walk baseline in huge 1000-task problems. Lastly, application to a slightly different class of SR–ST problems and comparison of the ensuing results with that of corresponding state-of-the-art methods demonstrate the potential wider applicability of the proposed online MRTA method.

BepiColombo’s Cruise Phase: Unique Opportunity for Synergistic Observations

The investigation of multi-spacecraft coordinated observations during the cruise phase of BepiColombo (ESA/JAXA) are reported, with a particular emphasis on the recently launched missions, Solar Orbiter (ESA/NASA) and Parker Solar Probe (NASA). Despite some payload constraints, many instruments onboard BepiColombo are operating during its cruise phase simultaneously covering a wide range of heliocentric distances (0.28 AU–0.5 AU). Hence, the various spacecraft configurations and the combined in-situ and remote sensing measurements from the different spacecraft, offer unique opportunities for BepiColombo to be part of these unprecedented multipoint synergistic observations and for potential scientific studies in the inner heliosphere, even before its orbit insertion around Mercury in December 2025. The main goal of this report is to present the coordinated observation opportunities during the cruise phase of BepiColombo (excluding the planetary flybys). We summarize the identified science topics, the operational instruments, the method we have used to identify the windows of opportunity and discuss the planning of joint observations in the future.

MILP formulation improvement with k-means clustering for the beam layout optimization in multibeam satellite systems

This paper addresses an NP-hard design optimization problem in a multibeam satellite communication system. This problem consists in designing irregular beam layouts to satisfy non-uniform user traffic demands over the satellite service area, under antenna constraints, satellite payload constraints and a telecommunication mission criterion. Efficiently solving this problem is of crucial importance due to its impact on the system performance and cost. We compare three mixed-integer linear programming formulations. The first one, issued from previous work, is based on a linearization of both convex and non-convex Euclidean distance constraints. The two other aim at reducing the solution space size and at breaking symmetry inherent to the first formulation. For that purpose, we introduce a new process to interface k-means clustering with mixed-integer linear programming. We examine an exact and a heuristic approach for exploiting these principles that yield two new formulations. The heuristic approach outperforms the others based on our tests on a set of large-scale realistic problem instances, allowing to use mixed-integer linear programming in the industrial context.

Transporting COVID-19 testing specimens by routing unmanned aerial vehicles with range and payload constraints: the case of Istanbul

ABSTRACT This paper aims to emphasize the impacts of the COVID-19 pandemic at the healthcare logistics and supply chain networks and to transport polymerase chain reaction testing samples between hospitals and laboratories by using unmanned aerial vehicles (UAVs). To achieve the mission, range and payload capacitated UAV routing problem is defined and a mixed-integer linear programming model (MILP) is developed. The MILP model aims to decide the requisite quantity of the UAVs and to minimize the total transportation distance while satisfying the predetermined constraints. As a real-life instance, the healthcare network of Istanbul is selected and the network includes 219 hospitals and 23-unit basic testing laboratories for the COVID-19 pandemic. The results indicated that the proposed optimization model can find the needed number of UAVs with the minimum tour distances in reasonable run times and achieves to transport 25,000 testing specimens between hospitals and laboratories via UAVs.

Research on Optimization of Heterogeneous Air Cargo Network Based on Hybrid Particle Swarm Algorithm

In order to solve the problem of poor timeliness and high operating cost caused by the failure to consider the types of cargo aircrafts and the lack of scientific and reasonable optimization model in the process of transportation scheduling optimization of China’s air cargo network, a transport network optimization model of heterogeneous cargo aircraft formation is established, which takes the total flight cost and the number of all-cargo aircrafts as two optimization objectives. The model takes into account the demand time window of air nodes, the maximum payload constraint of cargo aircrafts, the dynamic loading and unloading time of cargo aircrafts, the take-off and landing time and the handover time of cargo aircrafts. In this paper, a hybrid particle swarm algorithm is proposed. The crossover operator of genetic algorithm is used to keep the directivity of particles, and the acceptance criterion of simulated annealing is used to enhance the ability of the algorithm to jump out of local optimum. The effectiveness of the algorithm and the model is verified by the simulation optimization and comparative analysis of a numerical example.

Simultaneous, Robot-Compatible γ-Ray Spectroscopy and Imaging of an Operating Nuclear Reactor

The design and test of a robot-compatible, radiation detection instrument providing simultaneous $\gamma $ -ray imaging and $\gamma $ -ray spectroscopy is described. The sensing system comprises a cerium bromide inorganic scintillation detector and a cylindrical, lead slot collimator that is configured with a robot-compatible, on-board data acquisition system. The mount for the sensor is a lightweight, bespoke 3-axis gimbal actuated by servos for pan, tilt and rotation of the collimator for imaging capability. This paper discusses the integration of this relatively low-cost radiation detection apparatus with a commercially available, Robot-Operating-System-controlled robotic platform (a Clearpath Robotics™ Jackal). The detection system is compliant with the power and mass payload constraints of the robot. Its performance has been evaluated by means of two practical examples: a) measurements in a laboratory environment to assess the ability of the system to resolve two caesium-137 point sources, and b) deployment at the Jožef Stefan Institute TRIGA Mark II research reactor to assess the ability of the system to characterise the $\gamma $ -ray emission at 1 kW from a horizontal tangential beam port in the reactor hall and from the reactor sample pool above the core.

Energy-Constrained Delivery of Goods With Drones Under Varying Wind Conditions

In this paper, we study the feasibility of sending drones to deliver goods from a depot to a customer by solving what we call the Mission-Feasibility Problem (MFP). Due to payload constraints, the drone can serve only one customer at a time. To this end, we propose a novel framework based on time-dependent cost graphs to properly model the MFP and tackle the delivery dynamics. When the drone moves in the delivery area, the global wind may change thereby affecting the drone's energy consumption, which in turn can increase or decrease. This issue is addressed by designing three algorithms, namely: (i) compute the route of minimum energy once, at the beginning of the mission, (ii) dynamically reconsider the most convenient trip towards the destination, and (iii) dynamically select only the best local choice. We evaluate the performance of our algorithms on both synthetic and real-world data. The changes in the drone's energy consumption are reflected by changes in the cost of the edges of the graphs. The algorithms receive the new costs every time the drone flies over a new vertex, and they have no full knowledge in advance of the weights. We compare them in terms of the percentage of missions that are completed with success (the drone delivers the goods and comes back to the depot), with delivered (the drone delivers the goods but cannot come back to the depot), and with failure (the drone neither delivers the goods nor comes back to the depot).

Mission Design and Analysis for Mars Orbiter Mission

Mars orbiter mission (MOM) is India’s first interplanetary mission. MOM was planned to be launched by Polar Satellite Launch Vehicle (PSLV) into an Earth elliptic parking orbit (EPO) in Oct-Nov 2013 utilizing the 2013 opportunity to rendezvous with Mars. This paper covers the mission design and analysis carried out for the MOM/PSLV mission. New software tools were designed and developed to design interplanetary trajectories, to generate total maneuver strategy from earth EPO to target Martian orbit, to design trajectory correction maneuvers in interplanetary cruise phase targeting to desired B-plane parameters and to design Mars orbit insertion maneuver. The launch window and optimum EPO requirements were generated and provided to PSLV. The target Martian orbit was selected considering all the specified s/c platform and payload constraints. Maneuver strategies were designed considering operational constraints and total mission profiles were studied comprising orbital events and Earth-Satellite-Sun geometry and distance characteristics. Non-nominal mission scenarios were studied, comprising launcher dispersions, missing any of the earth phase burns and backup maneuver plans generated. Mars orbit insertion maneuver strategy was designed considering nominal and non-nominal scenarios. MOM was launched on 5th November, 2013 and successfully captured into the desired Martian orbit on 24th September, 2014 achieving the main technological objective of the mission in India’s first interplanetary endeavor.

Hyperspectral demosaicking and crosstalk correction using deep learning

Precision agriculture using unmanned aerial vehicles (UAVs) is gaining popularity. These UAVs provide a unique aerial perspective suitable for inspecting agricultural fields. With the use of hyperspectral cameras, complex inspection tasks are being automated. Payload constraints of UAVs require low weight and small hyperspectral cameras; however, such cameras with a multispectral color filter array suffer from crosstalk and a low spatial resolution. The research described in this paper aims to reduce crosstalk and to increase spatial resolution using convolutional neural networks. We propose a similarity maximization framework which is trained to perform end-to-end demosaicking and crosstalk-correction of a $$4 \times 4$$ raw mosaic image. The proposed method produces a hyperspectral image cube with 16 times the spatial resolution of the original cube while retaining a median structural similarity (SSIM) index of 0.85 (compared to an SSIM of 0.55 when using bilinear interpolation). Furthermore, this paper provides insight into the beneficial effects of crosstalk for hyperspectral demosaicking and gives best practices for several architectural and hyperparameter variations as well as a theoretical reasoning behind certain observations.

Design and analysis of Weak Stability Boundary trajectories to Moon

An algorithm is developed to find Weak Stability Boundary transfer trajectories to Moon in high fidelity force model using forward propagation. The trajectory starts from an Earth Parking Orbit (circular or elliptical). The algorithm varies the control parameters at Earth Parking Orbit and on the way to Moon to arrive at a ballistic capture trajectory at Moon. Forward propagation helps to satisfy launch vehicle’s maximum payload constraints. Using this algorithm, a number of test cases are evaluated and detailed analysis of capture orbits is presented.