Look-ahead Algorithm Research Articles

A Novel Approach for Solving the N-Queen Problem Using a Non-Sequential Conflict Resolution Algorithm

The N-Queens problem is a fundamental challenge in combinatorial optimization, commonly used as a benchmark for assessing the efficiency of algorithms. Traditional algorithms, such as Backtracking with Forward Checking (BFC), constraint satisfaction problem (CSP) techniques, Lookahead algorithms, and heuristic-based methods, often face challenges with exponential time complexity, making them less practical for large-scale instances. This paper introduces a novel algorithm, non-sequential conflict resolution (NSCR), which improves performance over traditional algorithms through dynamic conflict resolution. The NSCR algorithm iteratively resolves conflicts among queens by adjusting their positions, aiming to optimize both time complexity and memory usage. While NSCR also operates within exponential time bounds, it demonstrates improved scalability and efficiency compared to traditional methods. A significant strength of the NSCR algorithm lies in its space complexity, which is O(n), and a time complexity that, while typically lower than traditional methods, can reach O(n3) in the worst-case scenario. This linear space complexity is highly advantageous, particularly when dealing with large problem sizes, as it ensures efficient use of memory resources. Comparative analysis with the aforementioned algorithms shows that NSCR offers superior resource management, using up to 60% less memory and reducing runtime by approximately 50%, making it an efficient option for large-scale instances of the N-Queens problem. The algorithm’s performance, evaluated on problem sizes ranging from 8 to 1000 queens, highlights its ability to manage computational resources effectively, despite the inherent challenges of exponential time complexity.

Multi-UAV Formation Path Planning Based on Compensation Look-Ahead Algorithm

Multi-UAV Formation Path Planning Based on Compensation Look-Ahead Algorithm

Stochastic inventory routing with dynamic demands and intra-day depletion

Businesses such as supermarkets face an inventory routing problem with distinct stochastic dynamic demand distributions for various weekdays and times of the day. Most research, however, considers aggregated per-period demand and inventory depletion, thereby oversimplifying actual events. We formulate the problem as a finite-horizon stochastic dynamic program that accounts for the dynamic demand throughout the planning horizon and within each planning period. The models include different levels of information aggregation to decide the replenishment quantities and times by dividing periods into sub-periods with distinct demand distributions. This division helps the supplier create efficient replenishment plans and account for intra-day inventory levels. The modeled characteristics include routing, holding and stock-out costs, delivery time windows, inventory and vehicle capacities, and delivery time windows. The problem is solved using an iterative lookahead algorithm with an adaptive large neighborhood search for the routing and policy learning for the replenishment decisions. Numerical evaluations show the benefit of intra-day planning, effects of instance sizes, different coefficients of variation, and holding costs compared to benchmark replenishment policies. Considering intra-day depletion and demand data for each sub-period helps achieve savings of more than 20% compared to planning for full periods. The savings are obtained by adjusting delivery quantities, incorporating intra-day consumption, creating more efficient delivery routes, and correctly timing replenishments.

A Real-time Look-ahead Trajectory Planning Methodology for Multi Small Line Segments Path

When a robot is required to machine a complex curved workpiece with high precision and speed, the tool path is typically dispersed into a series of points and transmitted to the robot. The conventional trajectory planning method requires frequent starts and stops at each dispersed point to complete the task. This method not only reduces precision but also causes damage to the motors and robot. A real-time look-ahead algorithm is proposed in this paper to improve precision and minimize damage. The proposed algorithm includes a path-smoothing algorithm, a trajectory planning method, and a bidirectional scanning module. The path-smoothing method inserts a quintic Bezier curve between small adjacent line segments to achieve G^{2} continuity at the junctions. The trajectory planning method utilizes a quartic polynomial and a double-quartic polynomial that can achieve a constant velocity at the velocity limitation. The bidirectional scanning module calculates the velocity at each trajectory planning segment point, simplifying calculation complexity and can be run in real time. The feasibility of the proposed algorithm is verified through simulations and experiments, which can be run in real time. In addition, high machining precision can be achieved by adjusting the relevant parameters.

A Novel 3D Path Following Scheme for a Class of Underactuated Underwater Vehicles with Unconventional Actuation

A 3D path following algorithm involving a novel motion control methodology is investigated for use on a new class of autonomous underwater vehicle (AUV). Unlike standard path following AUV control algorithms for underactuated vehicles, where roll perturbations are avoided, this unconventional algorithm utilizes active roll control to achieve a desired heading. The desired heading is calculated using traditional look-ahead techniques, and is achieved by performing a roll rotation to align the body-fixed yaw plane with the desired heading, and then a yaw rotation is performed to achieve that desired heading. It is demonstrated that this new path following algorithm achieves good tracking results, but experiences complications for increased forward velocities. It is also demonstrated that the complications at higher velocities can be overcome with adaptive adjustment of the look-ahead algorithm parameters.

Online resequencing of buffers for automotive assembly lines

Mixed-model assembly lines are state of the art in automotive production systems. Because of the high number of customizable options which can be ordered in a vehicle, there is a huge variety of possible products. An important problem in this context is the sequencing of such products. Inevitably, there will be deviations from the intended production sequence in the course of production, as disruptions occur. The products must then be resequenced to ensure an optimal sequence. In this work, we consider the usage of a buffer (in form of an automated storage and retrieval system) between the paint shop and the final assembly to resequence the orders. We consider a high number of variants and, with this, a random input sequence for the buffer. Additionally to the physical resequencing in the buffer, the options get decoupled from the products. That allows virtual resequencing, in which parts and materials are interchanged. The dispatching selection must be made without full information in an online problem. To solve this problem, different heuristics and a lookahead algorithm are applied to minimize the amount of utility work in a paced automotive assembly line.

Leveraging look-ahead information for optimal battery thermal management

The efficiency and range of electric vehicles (EVs) is an actual object of concern among manufacturers. The fast market share growth together with issues such as range anxiety demand evermore robust battery thermal management system (BTMS) controllers to maximize its electrical output capability. Current rule-based controllers often cannot cope with the high variability of energy demand from EVs, leading to oscillations where derating occurs and increasing the EV overall energy consumption. This study proposes a prediction horizon estimation of the future energy demand based on driven cycles. Together with a look-ahead algorithm, it is possible to keep track of an optimal battery temperature which avoids battery derating during the warm-up phase of the vehicle. A battery temperature estimation using a probability matrix based on a Markov chain is proposed in which the controller improves its estimations by repeating the same route over several trips. Results show that the method can minimize the use of the electric battery heater by predicting the necessary battery temperature over a prediction horizon. Therefore, up to 4% of overall energy consumption is saved when the EV performs a daily commute driving cycle, when compared to the original controller. Also, a learning method is implemented, improving the future estimations by storing route data as more cycles are performed.

Optimal Maneuvering for Autonomous Vehicle Self-Localization.

We consider the problem of optimal maneuvering, where an autonomous vehicle, an unmanned aerial vehicle (UAV) for example, must maneuver to maximize or minimize an objective function. We consider a vehicle navigating in a Global Navigation Satellite System (GNSS)-denied environment that self-localizes in two dimensions using angle-of-arrival (AOA) measurements from stationary beacons at known locations. The objective of the vehicle is to travel along the path that minimizes its position and heading estimation error. This article presents an informative path planning (IPP) algorithm that (i) uses the determinant of the self-localization estimation error covariance matrix of an unscented Kalman filter as the objective function; (ii) applies an l-step look-ahead (LSLA) algorithm to determine the optimal heading for a constant-speed vehicle. The novel algorithm takes into account the kinematic constraints of the vehicle and the AOA means of measurement. We evaluate the performance of the algorithm in five scenarios involving stationary and mobile beacons and we find the estimation error approaches the lower bound for the estimator. The simulations show the vehicle maneuvers to locations that allow for minimum estimation uncertainty, even when beacon placement is not conducive to accurate estimation.

Research on Disease Prediction Method Based on R-Lookahead-LSTM.

Cardiovascular disease is one of the most serious diseases that threaten human health in the world today. Therefore, establishing a high-quality disease prediction model is of great significance for the prevention and treatment of cardiovascular disease. In the feature selection stage, three new strong feature vectors are constructed based on the background of disease prediction and added to the original data set, and the relationship between the feature vectors is analyzed by using the correlation coefficient map. At the same time, a random forest algorithm is introduced for feature selection, and the importance ranking of features is obtained. In order to further improve the prediction effect of the model, a cardiovascular disease prediction model based on R-Lookahead-LSTM is proposed. The model based on the stochastic gradient descent algorithm of the fast weight part of the Lookahead algorithm is optimized and improved to the Rectified Adam algorithm; the Tanh activation function is further improved to the Softsign activation function to promote model convergence; and the R-Lookahead algorithm is used to further optimize the long-term memory network model. Therefore, the long- and short-term memory network model can be better improved so that the model tends to be stable as soon as possible, and it is applied to the cardiovascular disease prediction model.

Features of a real-time s-shaped acceleration/deceleration curve constructing with piecewise linear interpolation of complex-shaped surfaces

In this paper we considered the problem of an s-shaped acceleration/deceleration curve constructing in real time with linear-spline interpolation, taking into account given constraints on the contour acceleration, jerk, and feed rate. The input data for the s-curve were defined in the lookahead algorithm and the geometric smoothing module. The choice of a particular acceleration/deceleration strategy depends on the segment length, the allowable feed rates at the segment junction, and the given kinematic constraints. Each trajectory segment can have a maximum of seven time intervals and their rounding will produce inaccuracies when forming the velocity profile. Therefore, to compensate for rounding errors, the method of half division was applied, which made it possible to remove gaps in the velocity contour. The experimental data obtained indicate the correctness of the chosen approach for its implementation as part of the CNC for high-speed machining of surfaces with complex shapes. Key words Feed-rate planning algorithm, s-shaped acceleration/deceleration, trajectory smoothing, numerical control system, real-time, frame preview algorithm. Acknowledgements The research was carried out with financial support of Ministry of Science and Higher Education of Russian Federation in the frame of state assignment (project no.FSFS-2020-0031).

Uncovering feature interdependencies in high-noise environments with stepwise lookahead decision forests

Conventionally, random forests are built from “greedy” decision trees which each consider only one split at a time during their construction. The sub-optimality of greedy implementation has been well-known, yet mainstream adoption of more sophisticated tree building algorithms has been lacking. We examine under what circumstances an implementation of less greedy decision trees actually yields outperformance. To this end, a “stepwise lookahead” variation of the random forest algorithm is presented for its ability to better uncover binary feature interdependencies. In contrast to the greedy approach, the decision trees included in this random forest algorithm, each simultaneously consider three split nodes in tiers of depth two. It is demonstrated on synthetic data and financial price time series that the lookahead version significantly outperforms the greedy one when (a) certain non-linear relationships between feature-pairs are present and (b) if the signal-to-noise ratio is particularly low. A long-short trading strategy for copper futures is then backtested by training both greedy and stepwise lookahead random forests to predict the signs of daily price returns. The resulting superior performance of the lookahead algorithm is at least partially explained by the presence of “XOR-like” relationships between long-term and short-term technical indicators. More generally, across all examined datasets, when no such relationships between features are present, performance across random forests is similar. Given its enhanced ability to understand the feature-interdependencies present in complex systems, this lookahead variation is a useful extension to the toolkit of data scientists, in particular for financial machine learning, where conditions (a) and (b) are typically met.

Anytime and Efficient Multi-agent Coordination for Disaster Response

The Coalition Formation with Spatial and Temporal constraints Problem (CFSTP) is a multi-agent task allocation problem where the tasks are spatially distributed, with deadlines and workloads, and the number of agents is typically much smaller than the number of tasks. To maximise the number of completed tasks, the agents may have to schedule coalitions. The state-of-the-art CFSTP solver, the Coalition Formation with Look-Ahead (CFLA) algorithm, has two main limitations. First, its time complexity is exponential with the number of agents. Second, as we show, its look-ahead technique is not effective in real-world scenarios, such as open multi-agent systems, where new tasks can appear at any time. In this work, we study its design and define a variant, called Coalition Formation with Improved Look-Ahead (text {CFLA}2), which achieves better performance. Since we cannot eliminate the limitations of CFLA in text {CFLA}2, we also develop a novel algorithm to solve the CFSTP, the first to be simultaneously anytime, efficient and with convergence guarantee, called Cluster-based Task Scheduling (CTS). In tests where the look-ahead technique is highly effective, CTS completes up to 30% (resp. 10%) more tasks than CFLA (resp. text {CFLA}2) while being up to 4 orders of magnitude faster. We also propose S-CTS, a simplified but parallel variant of CTS with even lower time complexity. Using scenarios generated by the RoboCup Rescue Simulation, we show that S-CTS is at most 10% less performing than high-performance algorithms such as Binary Max-Sum and DSA, but up to 2 orders of magnitude faster. Our results affirm CTS as the new state-of-the-art algorithm to solve the CFSTP.

Minibatch Forward-Backward-Forward Methods for Solving Stochastic Variational Inequalities

We develop a new stochastic algorithm for solving pseudomonotone stochastic variational inequalities. Our method builds on Tseng’s forward-backward-forward algorithm, which is known in the deterministic literature to be a valuable alternative to Korpelevich’s extragradient method when solving variational inequalities over a convex and closed set governed by pseudomonotone Lipschitz continuous operators. The main computational advantage of Tseng’s algorithm is that it relies only on a single projection step and two independent queries of a stochastic oracle. Our algorithm incorporates a minibatch sampling mechanism and leads to almost sure convergence to an optimal solution. To the best of our knowledge, this is the first stochastic look-ahead algorithm achieving this by using only a single projection at each iteration.

Development of an Adaptive-Feedrate Planning and Iterative Interpolator for Parametric Toolpath with Normal Jerk Constraint

Compared to conventional linear and circular segments, the parametric curve has advantages in approximating and machining, guaranteeing higher machining precision and better machining quality. Therefore, in Computer Numerical Control (CNC) systems, parametric toolpath has become more and more popular for its high-performance in practical machining and the performance of the parametric interpolator has become a standard to illustrate the advanced CNC systems. In this paper, adaptive-feedrate planning and the iterative interpolator have been developed to generate sampling points with respect to error and higher order kinematic constraints. Firstly, a jerk-limited feedrate profile and look-ahead algorithm are utilized to provide reachable feedrate at critical points. Afterwards, normal jerk (i.e. the jerk in a normal direction) are limited to improve machining precision and quality. In theory, it is proved that the normal jerk limitations have relationship with curvature derivative and a novel iterative interpolator is proposed to ensure the normal jerk limitation by considering the corresponding constraints at curvature derivative extreme points. Finally, simulation and experiments are conducted to demonstrate the efficiency and contour performance of the proposed method compared to conventional method and time-optimal method.

Resilient composition of drone services for delivery

We propose a novel resilient drone service composition framework for delivery in dynamic weather conditions. We use a skyline approach to select an optimal set of candidate drone services at the source node in a skyway network. Drone services are initially composed using a novel constraint-aware deterministic lookahead algorithm using the multi-armed bandit tree exploration. We propose a heuristic-based resilient service composition approach that adapts to runtime changes and periodically updates the composition to meet delivery expectations. Experimental results prove the efficiency of the proposed approach.

Novel algorithms for sequential fault diagnosis based on greedy method

Test sequencing for binary systems is a nondeterministic polynomial-complete problem, where greedy algorithms have been proposed to find the solution. The traditional greedy algorithms only extract a single kind of information from the D-matrix to search the optimal test sequence, so their application scope is limited. In this study, two novel greedy algorithms that combine the weight index for fault detection with the information entropy are introduced for this problem, which are defined as the Mix1 algorithm and the Mix2 algorithm. First, the application scope for the traditional greedy algorithms is demonstrated in detail by stochastic simulation experiments. Second, two new heuristic formulas are presented, and their scale factors are determined. Third, an example is used to show how the two new algorithms work, and four real-world D-matrices are employed to validate their universality and stability. Finally, the application scope of the Mix1 and Mix2 algorithms is determined based on stochastic simulation experiments, and the two greedy algorithms are also used to improve a multistep look-ahead heuristic algorithm. The Mix1 and Mix2 algorithms can obtain good results in a reasonable time and have a wide application scope, which also can be used to improve the multistep look-ahead heuristic algorithm.

Semantics and algorithms for trustworthy commitment achievement under model uncertainty

We focus on how an agent can exercise autonomy while still dependably fulfilling commitments it has made to another, despite uncertainty about outcomes of its actions and how its own objectives might evolve. Our formal semantics treats a probabilistic commitment as constraints on the actions an autonomous agent can take, rather than as promises about states of the environment it will achieve. We have developed a family of commitment-constrained (iterative) lookahead algorithms that provably respect the semantics, and that support different tradeoffs between computation and plan quality. Our empirical results confirm that our algorithms’ ability to balance (selfish) autonomy and (unselfish) dependability outperforms optimizing either alone, that our algorithms can effectively handle uncertainty about both what actions do and which states are rewarding, and that our algorithms can solve more computationally-demanding problems through judicious parameter choices for how far our algorithms should lookahead and how often they should iterate.

Flicker control in mains with distributed generation plants

The use of distributed generation (DG) plants in power supply systems is a rapid development line. However, the impact of DG on power quality is multivalued. On the one hand, the presence of DG allows to reduce voltage losses. On the other hand, a phenomenon called flicker and associated with rapid voltage fluctuations is possible. This effect is usually manifested at an abrupt voltage drop in the DG generator connection unit. The processes taking place in the network when flicker occurs in networks with DG have not been sufficiently studied. The article presents results of the flicker modeling in a network equipped with DG plants, implemented on the basis of synchronous generators. The results obtained indicated that with sharp disturbances caused by switching on and off an additional load, flicker is observed in networks with unregulated generators, accompanied by voltage and frequency fluctuations. Based on the wavelet transformation and spectral analysis methods, it was found that the power spectral density of the generated flicker-noise is inversely proportional to the frequency. The use of look-ahead control algorithms to control the excitation and rotors rotational speed of the DG plants generators, as well as concordant adjustment of their controllers, increases stability and removes flicker completely.

Study on High-Speed and Smooth Transfer of Robot Motion Trajectory Based on Modified S-Shaped Acceleration/Deceleration Algorithm

To meet the requirements on industrial robots concerning their fast-moving pick-and-place or obstacle avoidance, this article puts forward a novel high-speed and smooth transfer control algorithm for spatial elliptic trajectory - an modified S-shaped acceleration/deceleration (ACC/DEC) control algorithm based on a piecewise continuous jerk curve in order to achieve the smooth motion at a high speed. Through mathematical modeling and interpolation point algorithm of spatial elliptic trajectory, the interpolation point information can be obtained. Then, using the speed look-ahead algorithm, the optimal speed at the switching point can be calculated during the trajectory transfer. According to the analysis on the experiment and simulation results of a sorting case, the velocity curve turned out to be smoother and the run time decreased by 33.69%. In this way, this transfer algorithm can guarantee the smooth motion of industrial robots and also improve the motion efficiency significantly.

Look-Ahead and Learning Approaches for Energy Harvesting Communications Systems

This work investigates the performance of an energy harvesting communications system. This system consists of a transmitter and a receiver. The transmitter is equipped with an infinite buffer to store data, and energy harvesting capability to harvest renewable energy and store it in a finite battery. The goal is to maximize the expected cumulative throughput of such systems. The problem of finding an optimal power allocation policy is formulated as a Markov decision process. Two cases are considered based on the availability of statistical knowledge about the channel gain and energy harvesting processes. When this knowledge is available, an algorithm is designed to maximize the expected throughput, while reducing the complexity of traditional methods (e.g., value iteration). This algorithm exploits instant knowledge about the channel, harvested energy, and current battery level to find a near-optimal policy. For the second scenario, when the statistical knowledge is unavailable, reinforcement learning is used. Two different exploration algorithms, convergence-based and the epsilon-greedy algorithms, are used. Simulations and comparisons with conventional algorithms show the effectiveness of the look-ahead algorithm when the statistical knowledge is available, and the effectiveness of reinforcement learning in optimizing the system performance when this knowledge is unavailable.