Maneuver Strategy Research Articles

Evasion Maneuver and Timing Strategy for UAVs Under Direct Force Control

Given the capabilities of modern high-performance missiles, efficient evasion maneuver strategies are crucial for enhancing UAV survivability. This paper addresses the integration of evasion strategies and maneuver timing for targeted UAVs. First, a three-dimensional engagement scenario is modeled using the relative motion mechanism. Subsequently, a fundamental evasion strategy focusing on large roll maneuvers and two-phase acceleration is developed. With the initiation timing of direct force control designated as the control variable, the formulation of a constrained optimization problem to maximize miss distance is fully articulated. While preserving the high fidelity of the established switching model, the feasible timing intervals and the optimal timing are determined through numerical optimization. Finally, simulation experiments are conducted focusing on omnidirectional attacks from the upper hemisphere. The results demonstrate that by implementing specific evasion maneuvers at optimal timing, the target successfully escapes the effective range of the proximity fuse, thus confirming the feasibility of the proposed evasion maneuver and timing strategy.

Mathematical Model of Horizontal Track Conflict Prevention Algorithm in Detect-and-Avoid Framework

With the proliferation of Unmanned Aerial Vehicle (UAV) technology, the demand for effective collision avoidance technology has intensified. The DAIDALUS algorithm, devised by NASA Langley Research Center under the Detect-and-Avoid (DAA) framework, provides conflict prevention bands for remotely piloted UAVs navigating in intricate airspace. The algorithm computes the bands in two distinct phases: Conflict and Recovery. The formal model for both phases has been established and implemented through iterative programming approaches. However, the mathematical model remains incomplete. Therefore, based on the model, this paper proposes the mathematical model for the two phases of the horizontal track conflict prevention algorithm. Firstly, Cauchy’s inequality is proposed to formulate the model that addresses trajectory conflicts considering the UAV non-instantaneous maneuvering dynamics model, and then a prudent maneuvering strategy is designed to optimize the model for the recovery phase. Finally, the execution procedure of the algorithm within the two-stage mathematical model is also detailed. The results demonstrate that the proposed model achieves a higher precision in the preventive bands, implements an effective collision avoidance strategy, and consistently aligns with the DAIDALUS model while offering a larger buffer time or distance. This work theoretically validates the formal model of the DAIDLAUS algorithm and provides insights for further refinement.

Energy optimal path planning for wheeled mobile robots with circular obstacle

AbstractThe energy optimal motion planning algorithm for a differential drive robot with a circular obstacle in the maneuver space is introduced in this article. Rather than the traditional unicycle model, a non‐canonical state space model of the kinematics of a wheeled mobile robot is used to formulate the optimal control problem. The necessary conditions for optimality are formally derived using calculus of variations, resulting in constraints for the costates when the inequality constraint become active, revealing that the controls are required to be continuous. The optimality conditions permit decomposing the motion planning problem in two independent segments: prior to and after activation of the inequality constraints. Analytical expressions for the evolving states and control are found to be a function of Jacobi elliptic functions, permitting reducing the optimal control problem to a root‐finding problem. A comprehensive parametric study is included to demonstrate the impact on the optimal trajectories by varying the radius of the obstacle. The study shows that there are three distinct maneuver strategies with respect to the size of the obstacle. Furthermore, by including entering and exit time as optimization variables, a methodology for generalizing the development to any given motion scenario is developed and numerically illustrated.

Bounded Containment Maneuvering Protocols for Marine Surface Vehicles With Quantized Communications and Tracking Errors Constrained Guidance: Theory and Experiment.

A new type of containment maneuvering protocols for multiple marine surface vehicles (MSVs) is developed to follow a parameterized path in this work, where the tracking errors are constrained within finite time and the information needed to be transmitted is quantized during coordination. To achieve containment maneuvering of multiple MSVs, a two-objective coordinated control framework is proposed. For the geometric objective, by developing tan-type barrier Lyapunov functions (BLFs) and extended Lyapunov condition-based finite-time guidance laws, the performance of the parameterized line-of-sight guidance framework, including convergence speed and tracking error constraints, is improved. For the dynamic objective, based on quantized control strategy and smooth saturation functions, novel bounded containment maneuvering protocols are proposed to dramatically alleviate the burden of communications among MSVs and ensure more faster dynamic behavior on tracking the path updating speed. Both theoretical analysis and experimental tests with comparative studies illustrate the validity of the proposed containment maneuvering strategy.

A maneuvering strategy based on motion camouflage in three-player differential game

In this paper, we consider a three-player differential game in which the defender provides coverage for the evader by maneuvering toward the line of sight between the pursuer and the evader. The TAD game is divided into two pursuit-evasion games to obtain the saddle point strategy for each player. Different from the general solution, the defender uses motion camouflage conditions as its payoff function and gives the real-time maneuvering strategy. This approach ensures that the constraint in the gaming process is met by providing the saddle point solution for each time interval. Additionally, interception strategies are created for each player to get the required heading angle. These strategies and game dynamics can be predicted by examining the current state of the game. Consequently, we establish the advantage region for the pursuer and the defender based on these predictions. Based on the given initial conditions, the advantage region for the defender is presented in its initial velocity vector. Finally, the simulations validate the effectiveness of the proposed strategy and the capture region analysis.

Impulsive maneuver strategy for multi-agent orbital pursuit-evasion game under sparse rewards

To address the subjectivity of dense reward designs for the orbital pursuit-evasion game with multiple optimization objectives, this paper proposes the reinforcement learning method with a hierarchical network structure to guide game strategies under sparse rewards. Initially, to overcome the convergence challenges in the reinforcement learning training process under sparse rewards, a hierarchical network structure is proposed based on the hindsight experience replay. Subsequently, considering the strict constraints imposed by orbital dynamics on spacecraft state space, the reachable domain method is introduced to refine the subgoal space in the hierarchical network, further facilitating the achievement of subgoals. Finally, by adopting the centralized training-layered execution approach, a complete multi-agent reinforcement learning method with the hierarchical network structure is established, enabling networks at each level to learn effectively in parallel within sparse reward environments. Numerical simulations indicate that, under the single-agent reinforcement learning framework, the proposed method exhibits superior stability in the late training stage and enhances exploration efficiency in the early stage by 38.89% to 55.56% to the baseline method. Under the multi-agent reinforcement learning framework, as the relative distance decreases, the subgoals generated by the hierarchical network transition from long-term to short-term, aligning with human behavioral logic.

The Optimal Strategies of Maneuver Decision in Air Combat of UCAV Based on the Improved TD3 Algorithm

Nowadays, unmanned aerial vehicles (UAVs) pose a significant challenge to air defense systems. Unmanned combat aerial vehicles (UCAVs) have been proven to be an effective method to counter the threat of UAVs in application. Therefore, maneuver decision-making has become the crucial technology to achieve autonomous air combat for UCAVs. In order to solve the problem of maneuver decision-making, an autonomous model of UCAVs based on the deep reinforcement learning method was proposed in this paper. Firstly, the six-degree-of-freedom (DoF) dynamic model was built in three-dimensional space, and the continuous actions of tangential overload, normal overload, and roll angle were selected as the maneuver inputs. Secondly, to improve the convergence speed for the deep reinforcement learning method, the idea of “scenario-transfer training” was introduced into the twin delayed deep deterministic (TD3) policy gradient algorithm, the results showing that the improved algorithm could cut off about 60% of the training time. Thirdly, for the “nose-to-nose turns”, which is one of the classical maneuvers for experienced pilots, the optimal maneuver generated by the proposed method was analyzed. The results showed that the maneuver strategy obtained by the proposed method was highly consistent with that made by experienced fighter pilots. This is also the first time in a public article that compared the maneuver decisions made by the deep reinforcement learning method with experienced fighter pilots. This research can provide some meaningful references to generate autonomous decision-making strategies for UCAVs.

DYNAMICS AND STABILITY OF EMERGENCY VEHICLES ON DIFFICULT TERRAIN

This research paper examines the nuances of the dynamics and stability of emergency vehicles when maneuvering over complex terrain, offering a multifaceted analysis that combines theoretical modeling and empirical research. Central to the considerations is the study of the interaction between vehicle design and features of various landscapes, including mountain, forest and urban environments. The research uses a synergistic approach that interweaves advanced mathematical modeling techniques such as computational fluid dynamics and finite element analysis with experimental validation in real-world conditions. The methodology clarifies the critical role of vehicle design parameters, such as suspension tuning and aerodynamic properties, in determining vehicle performance in challenging environments. The study highlights the relevance of terrain-dependent speed limits and maneuvering strategies, advocating for modular vehicle designs and terrain-independent technologies. The paper not only makes a significant contribution to the optimization of emergency vehicles, but also paves the way for future research in the areas of artificial intelligence-driven control systems, sustainable propulsion technologies, and predictive performance modeling. Integrated solutions in this area re key to improving the efficiency and safety of rescue operations in diverse and challenging environments.

Mentha longifolia assisted nanostructures: An approach to obliterate microbial biofilms.

Microbes maneuver strategies to become incessant and biofilms perfectly play a role in scaling up virulence to cause long-lasting infections. The present study was designed to assess the use of an eco-friendly formulation of functionalized silver nanoparticles generated from Mentha longifolia leaf extract (MℓE) for the treatment of biofilm-producing microbes. Nanoparticles synthesized using MℓE as a reducing agent were optimized at different strengths of AgNO3 (1 mM, 2 mM, 3 mM, and 4 mM). Synthesis of M. longifolia silver nanoparticles (MℓAgNPs) was observed spectrophotometrically (450 nm) showing that MℓAgNPs (4 mM) had the highest absorbance. Various techniques e.g., Fourier transforms Infrared spectroscopy (FTIR), Dynamic light scattering (DLS), zeta potential (ZP), X-ray Diffraction (XRD), scanning electron microscope (SEM), and transmission electron microscope (TEM) were used to characterize MℓAgNPs. In the present study, the Kirby-Bauer method revealed 4mM was the most detrimental conc. of MℓAgNPs with MIC and MBC values of 0.62 μg/mL and 1.25 μg/mL, 0.03 μg/mL and 0.078 μg/mL, and 0.07 μg/mL and 0.15 μg/mL against previously isolated and identified clinical strains of Escherichia coli, Pseudomonas aeruginosa, Klebsiella pneumoniae, and Staphylococcus aureus, respectively. Moreover, the MℓAgNP antibiofilm activity was examined via tissue culture plate (TCP) assay that revealed biofilm inhibition of up to 87.09%, 85.6%, 83.11%, and 75.09% against E. coli, P. aeruginosa, K. pneumonia, and S. aureus, respectively. Herbal synthesized silver nanoparticles (MℓAgNPs) tend to have excellent antibacterial and antibiofilm properties and are promising for other biomedical applications involving the extrication of irksome biofilms. For our best knowledge, it is the first study on the use of the green-synthesized silver nanoparticle MℓAgNP as an antibiofilm agent, suggesting that this material has antibiotic, therapeutic, and industrial applications.

A Deep Reinforcement Learning-Based Intelligent Maneuvering Strategy for the High-Speed UAV Pursuit-Evasion Game

Given the rapid advancements in kinetic pursuit technology, this paper introduces an innovative maneuvering strategy, denoted as LSRC-TD3, which integrates line-of-sight (LOS) angle rate correction with deep reinforcement learning (DRL) for high-speed unmanned aerial vehicle (UAV) pursuit–evasion (PE) game scenarios, with the aim of effectively evading high-speed and high-dynamic pursuers. In the challenging situations of the game, where both speed and maximum available overload are at a disadvantage, the playing field of UAVs is severely compressed, and the difficulty of evasion is significantly increased, placing higher demands on the strategy and timing of maneuvering to change orbit. While considering evasion, trajectory constraint, and energy consumption, we formulated the reward function by combining “terminal” and “process” rewards, as well as “strong” and “weak” incentive guidance to reduce pre-exploration difficulty and accelerate convergence of the game network. Additionally, this paper presents a correction factor for LOS angle rate into the double-delay deterministic gradient strategy (TD3), thereby enhancing the sensitivity of high-speed UAVs to changes in LOS rate, as well as the accuracy of evasion timing, which improves the effectiveness and adaptive capability of the intelligent maneuvering strategy. The Monte Carlo simulation results demonstrate that the proposed method achieves a high level of evasion performance—integrating energy optimization with the requisite miss distance for high-speed UAVs—and accomplishes efficient evasion under highly challenging PE game scenarios.

Maneuver strategies of Starlink satellite based on SpaceX-released ephemeris

In the face of huge collision risks posed by the Starlink mega-constellation, the detection of unpredictable orbital maneuvers of Starlink satellites emerges as an important topic of Space Situational Awareness (SSA). The ephemerides released by the SpaceX company on the Space-track website provide a timely and valuable opportunity for addressing this problem. Based on the mean orbital elements extracted from Starlink ephemerides released at the end of 2023, the maneuver strategies, including maneuver frequency and characteristics of Starlink satellites in different orbital phases, are discussed, and the start time and end time with error ranges of confirmed maneuvers are acquired. It is found that operational satellites in Shell 1 perform an orbit maintenance maneuver approximately every 2 days, while those in Shells 2–5 execute a maneuver roughly once a day. Moreover, the parking satellites maneuver approximately every 0.25 days on average. As for satellites in the climbing orbit and the deorbiting orbit, they maneuver around every 0.1 days. The maneuver strategies of Starlink summarized in this paper can contribute to optimizing the observation plan of the Starlink constellation.

A method for Designing a Guidance Law Utilizing Linear Quadratic Forms based on Differential Games in 3-Dimensional Space

Consider a T-A-D game scenario in space: the target strives to evade the attacker, the defender intercepts the target, and the attacker must both avoid the target's counterattack and attempt to hit the target. It is assumed that all entities are intelligent, anticipating the next state of their adversaries and making the most suitable maneuvering strategy based on this prediction. This paper presents a 3-dimensional linear-quadratic differential game guidance law for target-attacker-defender scenarios, which is more intelligent than traditional guidance laws and is suitable for future intelligent environments.

An evolutionary multi-agent reinforcement learning algorithm for multi-UAV air combat

In recent years, multi-agent reinforcement learning (MARL) has been widely used as a multi-UAV autonomous countermeasure. However, because training is usually performed in a self-play manner, the existing algorithms may suffer from strategic cycles without progress. Moreover, in complex air combat, the state dimensions change dynamically with the varying number of unmanned aerial vehicles (UAVs), making it difficult for existing MARL algorithms with fixed-input-size network architectures to effectively handle the variations. Therefore, we propose an evolutionary MARL method that can avoid cycles through the evolution of a population of UAV groups. In addition, an attention mechanism with death masking was adopted to handle dynamic changes in the number of UAVs. Experimental results conducted in our constructed multi-UAV air combat environment show that our method outperforms state-of-the-art methods and can help UAVs generate effective maneuver strategies to defeat opponents.

Chandrayaan-3 Trajectory Design: Injection to Successful Landing

On August 23, 2023, India became the first country to successfully soft-land near the south pole of the moon. The end-to-end trajectory design from Earth-orbit injection to the moon landing was crucial to the overall success of the mission. This paper describes the trajectory design phase, which comprised the target lunar orbit design, the maneuver strategy implemented to acquire the specified orbit, and finally the multiphase lunar landing trajectory design carried out for the powered descent phase of the lander module. The objective of the trajectory design was not only to soft-land on the moon but also to do it at a predefined site when it was illuminated. Based on this design, the Chandrayaan-3 spacecraft was launched on July 14, 2023; successfully rendezvoused with the moon on August 5, 2023, as planned; and finally soft-landed near the lunar south pole.

A novel evasion guidance for hypersonic morphing vehicle via intelligent maneuver strategy

This paper presents a novel evasion guidance law for hypersonic morphing vehicles, focusing on determining the optimized wing's unfolded angle to promote maneuverability based on an intelligent algorithm. First, the pursuit-evasion problem is modeled as a Markov decision process. And the agent's action consists of maneuver overload and the unfolded angle of wings, which is different from the conventional evasion guidance designed for fixed-shape vehicles. The reward function is formulated to ensure that the miss distances satisfy the prescribed bounds while minimizing energy consumption. Then, to maximize the expected cumulative reward, a residual learning method is proposed based on proximal policy optimization, which integrates the optimal evasion for linear cases as the baseline and trains to optimize the performance for nonlinear engagement with multiple pursuers. Therefore, offline training guarantees improvement of the constructed evasion guidance law over conventional ones. Ultimately, the guidance law for online implementation includes only analytical calculations. It maps from the confrontation state to the expected angle of attack and the unfolded angle while retaining high computational efficiency. Simulations show that the proposed evasion guidance law can utilize the change of unfolded angle to extend the maximum overload capability. And it surpasses conventional maneuver strategies by ensuring better evasion efficacy and higher energy efficiency.

Intelligent maneuver strategy for hypersonic vehicles in three-player pursuit-evasion games via deep reinforcement learning.

Aiming at the rapid development of anti-hypersonic collaborative interception technology, this paper designs an intelligent maneuver strategy of hypersonic vehicles (HV) based on deep reinforcement learning (DRL) to evade the collaborative interception by two interceptors. Under the meticulously designed collaborative interception strategy, the uncertainty and difficulty of evasion are significantly increased and the opportunity for maneuvers is further compressed. This paper, accordingly, selects the twin delayed deep deterministic gradient (TD3) strategy acting on the continuous action space and makes targeted improvements combining deep neural networks to grasp the maneuver strategy and achieve successful evasion. Focusing on the time-coordinated interception strategy of two interceptors, the three-player pursuit and evasion (PE) problem is modeled as the Markov decision process, and the double training strategy is proposed to juggle both interceptors. In reward functions of the training process, the energy saving factor is set to achieve the trade-off between miss distance and energy consumption. In addition, the regression neural network is introduced into the deep neural network of TD3 to enhance intelligent maneuver strategies' generalization. Finally, numerical simulations are conducted to verify that the improved TD3 algorithm can effectively evade the collaborative interception of two interceptors under tough situations, and the improvements of the algorithm in terms of convergence speed, generalization, and energy-saving effect are verified.

Research on Deep Q-Network Hybridization with Extended Kalman Filter in Maneuvering Decision of Unmanned Combat Aerial Vehicles

To adapt to the development trend of intelligent air combat, it is necessary to research the autonomous generation of maneuvering decisions for unmanned combat aerial vehicles (UCAV). This paper presents a maneuver decision-making method for UCAV based on a hybridization of deep Q-network (DQN) and extended Kalman filtering (EKF). Firstly, a three-dimensional air combat simulation environment is constructed, and a flight motion model of UCAV is designed to meet the requirements of the simulation environment. Secondly, we evaluate the current situation of UCAV based on their state variables in air combat, for further network learning and training to obtain the optimal maneuver strategy. Finally, based on the DQN, the system state equation is constructed using the uncertain parameter values of the current network, and the observation equation of the system is constructed using the parameters of the target network. The optimal parameter estimation value of the DQN is obtained by iteratively updating the solution through EKF. Simulation experiments have shown that this autonomous maneuver decision-making method hybridizing DQN with EKF is effective and reliable, as it can eliminate the opponent and preserve its side.

Quantized Communications in Containment Maneuvering for Output Constrained Marine Surface Vehicles: Theory and Experiment

The aim of this paper is to derive a containment maneuvering coordination strategy for a team of marine surface vehicles (MSVs) to track one parameterized path guided by two virtual leaders, where the tracking errors are constrained and the communications among MSVs are quantized. A two-level coordinated framework for containment maneuvering is proposed. For the kinematic level, to obtain the performance of tracking error constraints, guidance laws are derived by developing parameterized line-of-sight (LOS)-based symmetrical <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$ln$</tex-math></inline-formula> -type Lyapunov functions, where the control functions are injected into the guidance laws to prevent constraint violations. For the coordination level, a communication protocol is proposed to reduce the amount of communication data by developing a quantized control strategy. Compared with the current containment maneuvering strategy, the whole design can bind the along-track and cross-track errors into the user-defined regions, increase the convergence speed of tracking errors, save bandwidth of the whole network and reduce the sensitivity of the initial positions of MSVs. Theoretical analysis shows that all signals are bounded. Experimental examples are also provided to verify the proposed containment maneuvering strategy.

Missile Threat Detection and Evasion Maneuvers With Countermeasures for a Low-Altitude Aircraft

This paper investigates Man-Portable Air Defense Systems (MANPAD) missile threat detection and an effective maneuvering strategy for aircraft (A/C) destruction avoidance. The 3D trajectory of a multistage missile (boost and sustain phases) under Pure Proportional Navigation is first estimated. The maximum likelihood estimator is used to estimate its motion parameters of the two-stage missile considered using only angle measurements from a low-altitude A/C. Missile threat detection is investigated based on two methods: estimation-based as a baseline method and threat plane-based. The former can provide a miss distance between the A/C and the missile, but more measurements are required. The latter is shown to give a faster threat alert, allowing the A/C to start the evasive maneuver earlier. An efficient evasion maneuver for avoiding destruction, is banking away from the missile by a turning maneuver in the horizontal plane, due to its low-altitude. The survivability probability of the A/C is evaluated with and w/o countermeasures (flares). There are two key factors that affect the evasion performance: maneuver start time and the maximum acceleration during the maneuver. Simulations show that the A/C can start maneuvering earlier using the threat plane-based method compared with the estimation-based method. It can successfully evade the missile with the help of maneuvering. It is also shown that using flares, the necessary maneuver is less drastic (lower <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$g$</tex-math></inline-formula> 's). Simulation results compare the necessary <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$g$</tex-math></inline-formula> 's for obtaining a certain minimal distance from the missile.

Effect of verbal instructions on trunk muscle activity during volitional preemptive abdominal contraction

ObjectiveThis study investigated the effect of Verbal Instruction (VI) strategies on trunk muscle contraction among healthy subjects. The effect of three VI Abdominal Drawing-In Maneuver (ADIM) and two VI Abdominal Bracing Maneuver (ABM) strategies on left Internal (LIO) and External Oblique (LEO) and bilateral superficial Multifidi (sMf) activation was examined. DesignWithin-subjects, repeated measure design. MethodsSurface EMG (sEMG) measured LIO, LEO, and sMf activity in 28 subjects (mean age 23.5 ± 5.5 years). Testing included five supine hook-lying and five quiet standing conditions. ResultsOne-way ANOVAs demonstrated no significant main effect for ADIM or ABM in supine or standing (p > .05). Muscle activation amplitudes during VPAC conditions demonstrated higher mean values for standing versus supine (p < .05) except for two conditions involving LEO. Friedman Tests for dominant strategy demonstrated a significant main effect for ADIM-VI and ABM-VI strategies. Post-hoc testing generally showed the dominant strategy to be significantly higher versus others. ConclusionNo single preferred VI cue for ADIM or ABM was observed. Each subject's dominant strategy dictated the most suitable VI. Standing was preferred for LIO and sMf activation, whereas position did not change LEO activation. Non-significant correlations between all muscle pairings during all ADIM and ABM strategies were observed. These findings may suggest the need for healthcare providers who understand the intricacies of trunk stability to teach and monitor VPAC with either ADIM or ABM options.