Incremental Search Algorithm Research Articles

Lazy incremental search for efficient replanning with bounded suboptimality guarantees

We present a lazy incremental search algorithm, Lifelong-GLS (L-GLS), along with its bounded suboptimal version, Bounded L-GLS (B-LGLS) that combine the search efficiency of incremental search algorithms with the evaluation efficiency of lazy search algorithms for fast replanning in problem domains where edge evaluations are more expensive than vertex expansions. The proposed algorithms generalize Lifelong Planning A* (LPA*) and its bounded suboptimal version, Truncated LPA* (TLPA*), within the Generalized Lazy Search (GLS) framework, so as to restrict expensive edge evaluations only to the current shortest subpath when the cost-to-come inconsistencies are propagated during repair. We also present dynamic versions of the L-GLS and B-LGLS algorithms, called Generalized D* (GD*) and Bounded Generalized D* (B-GD*), respectively, for efficient replanning with non-stationary queries, designed specifically for navigation of mobile robots. We prove that the proposed algorithms are complete and correct in finding a solution that is guaranteed not to exceed the optimal solution cost by a user-chosen factor. Our numerical and experimental results support the claim that the proposed integration of the incremental and lazy search frameworks can help find solutions faster compared to the regular incremental or regular lazy search algorithms when the underlying graph representation changes often.

Distributed Feature Selection for Power System Dynamic Security Region Based on Grid-Partition and Fuzzy-Rough Sets

In order to satisfy the requirements of modern online security assessment of power systems with continuously increasing complexity in terms of structure and scale, it is desirable to develop a power system dynamic security region (DSR) analysis. However, data-driven methods suffer from expensive model training costs and overfitting when determining DSR boundaries with high-dimensional grid features. Given this problem, a distributed feature selection method based on grid partition and fuzzy-rough sets is proposed in this paper. The method first employs the Louvain algorithm to partition the power grid and divide the original feature set so that high-dimensional features can be allocated to multiple computational units for distributed screening. At this point, the connections between features of different computational units are minimized to a relatively low level, thereby avoiding large errors in the distributed results. Then, an incremental search algorithm based on the fuzzy-rough set theory (FRST) is used for feature selection at each computational unit, which can effectively take into account the intrinsic connections between features. Finally, the results of all computational units are integrated in the coordination unit to complete the overall feature selection. The experimental results based on the IEEE-39 bus system show that the proposed method can help simplify the power system DSR analysis with high-dimensional features by screening the critical features. And compared with other commonly used filter methods, it has higher screening accuracy and lower time costs.

Pushing diversity into higher dimensions: The LID effect on diversified similarity searching

Diversified similarity queries extend similarity searches by retrieving data elements simultaneously similar to a given object (reference) but dissimilar to other elements in the result set. Although diversity-related querying routines are expected to be costlier than similarity searches, e.g., k-NN vs. diversified k-NN, experimental evaluations of their quality and performance for exploring datasets embedded in high-dimensional spaces are still open issues. In this manuscript, we apply the concept of Local Intrinsic Dimensionality (LID) to examine the behavior of the method diversity browsing in the exploration of high-dimensional data. Diversity browsing extends the k-NN incremental search algorithm distance browsing towards diversified similarity queries by using both proximity and inner dissimilarity criteria (called Influence rule) to dismiss result set candidates. We empirically investigate the effect of real-world data LIDs over diversity browsing and found shreds of evidence that indicate: (i) the amount of diversity (measured by the total of “retrievable” diversified neighbors) is constrained by both the data fold LID and the Influence-based partitioning of the search space. Counter-intuitively, this number increases with the LID because inner dissimilarities between diversified neighbors grow slower in intrinsically high-dimensional spaces, which generates an inverse correlation between Influence-based pruning and data LID; (ii) exploratory diversity searches (the retrieval of all diversified neighbors) over varying LID sets produce query-based manifolds as the recovered elements are also data samples with enhanced measures of Relative Contrast, Relative Variance, and Intrinsic Dimensionality in comparison to the original dataset. Since exploratory diversity searches preserve the viewpoint of the query object, their outputs also provide an alternative for data visualization with meaningful distance relationships. We illustrate such a data visualization potential with bubble-clustered scatter plots by using Influence rules to determine the size of each cluster; (iii) the tuning of metric indexes is still a relevant factor for speeding up diversified similarity queries over low and medium LID data folds. In particular, our evaluation showed that metric pivots chosen with Maximal Variance criteria outperform Random pivots regarding diversity browsing searches implemented over VP-Tree indexes for most LID ranges; and (iv) the performance difference from diversity browsing to distance browsing reduces with the LID, which suggests that the querying of high-dimensional datasets by diversity may provide richer results at proportional costs to those of similarity searches.

Discrete Phase Shifts of Intelligent Reflecting Surface Systems Considering Network Overhead.

In this study, the performance of intelligent reflecting surfaces (IRSs) with a discrete phase shift strategy is examined in multiple-antenna systems. Considering the IRS network overhead, the achievable rate model is newly designed to evaluate the practical IRS system performance. Finding the optimal resolution of the IRS discrete phase shifts and a corresponding phase shift vector is an NP-hard combinatorial problem with an extremely large search complexity. Recognizing the performance trade-off between the IRS passive beamforming gain and IRS signaling overheads, the incremental search method is proposed to present the optimal resolution of the IRS discrete phase shift. Moreover, two low-complexity sub-algorithms are suggested to obtain the IRS discrete phase shift vector during the incremental search algorithms. The proposed incremental search-based discrete phase shift method can efficiently obtain the optimal resolution of the IRS discrete phase shift that maximizes the overhead-aware achievable rate. Simulation results show that the discrete phase shift with the incremental search method outperforms the conventional analog phase shift by choosing the optimal resolution of the IRS discrete phase shift. Furthermore, the cumulative distribution function comparison shows the superiority of the proposed method over the entire coverage area. Specifically, it is shown that more than 20% of coverage extension can be accomplished by deploying IRS with the proposed method.

Computationally efficient and sub-optimal trajectory planning framework based on trajectory-quality growth rate analysis.

A planetary exploration rover has been used for scientific missions or as a precursor for a future manned mission. The rover's autonomous system is managed by a space-qualified, radiation-hardened onboard computer; hence, the processing performance for such a computer is strictly limited, owing to the limitation to power supply. Generally, a computationally efficient algorithm in the autonomous system is favorable. This study, therefore, presents a computationally efficient and sub-optimal trajectory planning framework for the rover. The framework exploits an incremental search algorithm, which can generate more optimal solutions as the number of iterations increases. Such an incremental search is subjected to the trade-off between trajectory optimality and computational burden. Therefore, we introduce the trajectory-quality growth rate (TQGR) to statistically analyze the relationship between trajectory optimality and computational cost. This analysis is conducted in several types of terrain, and the planning stop criterion is estimated. Furthermore, the relation between terrain features and the stop criterion is modeled offline by a machine learning technique. Then, using the criterion predicted by the model, the proposed framework appropriately interrupts the incremental search in online motion planning, resulting in a sub-optimal trajectory with less computational burden. Trajectory planning simulation in various real terrain data validates that the proposed framework can, on average, reduce the computational cost by 47.6% while maintaining 63.8% of trajectory optimality. Furthermore, the simulation result shows the proposed framework still performs well even though the planning stop criterion is not adequately predicted.

Multi-Objective Path-Based D* Lite

Incremental graph search algorithms such as D* Lite reuse previous, and perhaps partial, searches to expedite subsequent path planning tasks. In this article, we are interested in developing incremental graph search algorithms for path finding problems to simultaneously optimize multiple objectives such as travel risk, arrival time, etc. This is challenging because in a multi-objective setting, the number of "Pareto-optimal" solutions can grow exponentially with respect to the size of the graph. This article presents a new multi-objective incremental search algorithm called Multi-Objective Path-Based D* Lite (MOPBD*) which leverages a path-based expansion strategy to prune dominated solutions. Additionally, we introduce a sub-optimal variant of MOPBD* to improve search efficiency while approximating the Pareto-optimal front. We numerically evaluate the performance of MOPBD* and its variants in various maps with two and three objectives. Results show that our approach is more efficient than search from scratch, and runs up to an order of magnitude faster than the existing incremental method for multi-objective path planning.

Position Paper: Incremental Search Algorithms Considered Poorly Understood

Incremental search algorithms, such as D* Lite, reuse information from previous searches to speed up the current search and can thus solve sequences of similar search problems faster than Repeated A*, which performs repeated A* searches. In this position paper, we study goal-directed navigation in initially unknown terrain and point out that it is currently not well understood when D* Lite runs faster than Repeated A*. In general, it appears that Repeated A* runs faster than D* Lite for easy navigation problems (where the agent reaches the goal with only a small number of searches), which means that it runs faster than D* Lite quite often in practice. We draw two conclusions, namely that incremental search algorithms need to be evaluated in more diverse testbeds to improve our understanding of their properties and that they can be improved to be more competitive for easy navigation problems.

HyPED: Modeling and Analyzing Action Games as Hybrid Systems

Platformers and action-adventure games have high-dimensional state spaces with difficult, non-linear constraints on character movement; even worse, game environments often respond to the player in complex ways that can cause exponential expansion of the planning search space. Planning problems in these high-dimensional spaces generally require domain-specific knowledge and manually abstracted models of game rules to replicate the intuition of human designers or playtesters. In this work, we outline a system for modeling these complex games at a precise and low level in terms of hybrid automata. With this representation, standard incremental search algorithms can be used to answer reachable-region queries, taking advantage of the domain information embedded in the system.

IADA*-RL: Anytime Graph-Based Path Planning with Deep Reinforcement Learning for an Autonomous UAV

Path planning algorithms are of paramount importance in guidance and collision systems to provide trustworthiness and safety for operations of autonomous unmanned aerial vehicles (UAV). Previous works showed different approaches mostly focusing on shortest path discovery without a sufficient consideration on local planning and collision avoidance. In this paper, we propose a hybrid path planning algorithm that uses an anytime graph-based path planning algorithm for global planning and deep reinforcement learning for local planning which applied for a real-time mission planning system of an autonomous UAV. In particular, we aim to achieve a highly autonomous UAV mission planning system that is adaptive to real-world environments consisting of both static and moving obstacles for collision avoidance capabilities. To achieve adaptive behavior for real-world problems, a simulator is required that can imitate real environments for learning. For this reason, the simulator must be sufficiently flexible to allow the UAV to learn about the environment and to adapt to real-world conditions. In our scheme, the UAV first learns about the environment via a simulator, and only then is it applied to the real-world. The proposed system is divided into two main parts: optimal flight path generation and collision avoidance. A hybrid path planning approach is developed by combining a graph-based path planning algorithm with a learning-based algorithm for local planning to allow the UAV to avoid a collision in real time. The global path planning problem is solved in the first stage using a novel anytime incremental search algorithm called improved Anytime Dynamic A* (iADA*). A reinforcement learning method is used to carry out local planning between waypoints, to avoid any obstacles within the environment. The developed hybrid path planning system was investigated and validated in an AirSim environment. A number of different simulations and experiments were performed using AirSim platform in order to demonstrate the effectiveness of the proposed system for an autonomous UAV. This study helps expand the existing research area in designing efficient and safe path planning algorithms for UAVs.

Two Server Dynamic Coverage Location Model Under Stochastic Travel Time

This paper describes an optimization location model that minimizes the number of two kinds of ambulances, which are; advance life support (ALS) and basic life support (BLS), while meeting the coverage requirement. The purpose of this research is to improve the performance of the emergency medical service (EMS) by developing a two-server dynamic covering location model (TS-DCL). This model is considered with realistic operating conditions for the EMS system that is solved using a developed incremental search algorithm (ISA). The TS-DCL model includes the stochastic nature of travel time in response to service, so the ambulance location model becomes more realistic. Also, the approximate hypercube model is used to determine the dependence of ambulance busy fractions. An experimental study is carried out to examine the effects of the proposed model on the design of the ambulance services system. It is also including the application of the proposed model to a 144-node problem representing Al-karkh District in Baghdad City in Iraq. The experimental study shows that the TS-DCL model requires at least 26 ALS, 12 BLS backups and 24 BLS ambulances to meet the 95% coverage requirement of both the critical and non-critical calls.

IADA*: Improved Anytime Path Planning and Replanning Algorithm for Autonomous Vehicle

Path planning of autonomous mobile robots in a real-world environment presents several challenges which are usually not raised in other areas. The real world is inherently complex, uncertain and dynamic. Therefore, accurate models of path planning are difficult to obtain and quickly become outdated. Anytime planners are ideal for this type of problem as they can find an initial solution very quickly and then improve it as time allows. This paper proposes a new anytime incremental search algorithm named improved Anytime Dynamic A*(iADA*). The algorithm is based on the currently popular anytime heuristic search algorithm, which is Anytime Dynamic A*(ADA*). The iADA* algorithm improves the calculation of the path lengths and decreases the calculating frequency of the path throughout the search, making it significantly faster. The algorithm is designed to provide an efficient solution to a complex, dynamic search environment when the locally changes affected. Our study shows that the two-dimensional path-planning iADA* experiments were between 2.0 to 3.7 times faster than ADA*, both in partially known and fully unknown dynamic environments. Additionally, in this paper shows the experiment results of the comparison with other four existing algorithms based on computing time and path lengths. iADA* was an average 2.57 times reduced on the computational time for the environment which locally changes effected. For the path length is little increase, but it is not the worst case. According to the experiments, the more the environmental problems and complexity increases, the more iADA* provides a rapid in-search time and total time to obtain the final solution.

AD*-Cut: A Search-Tree Cutting Anytime Dynamic A* Algorithm

This paper presents a new anytime incremental search algorithm, AD*-Cut. AD*-Cut is based on two algorithms, namely, Anytime Repairing A* (ARA*) and the novel incremental search algorithm, D* Extra Lite. D* Extra Lite reinitializes (cuts) entire search-tree branches that have been affected by changes in an environment, and D* Extra Lite appears to be quicker than the reinitialization during the search utilized by the popular incremental search algorithm, D* Lite. The search-tree branch cutting is a simple and robust technique that can be easily applied to ARA*. Consequently, AD*-Cut extends D* Extra Lite in the same manner, as the state-of-the-art Anytime D* (AD*) algorithm extends D* Lite. The benchmark results suggest that AD*-Cut is quicker and achieves shorter paths than AD* when used for path planning on 3D state-lattices (a 2D position with rotation).

Detecting action tubes via spatial action estimation and temporal path inference

Abstract In this paper, we address the problem of action detection in unconstrained video clips. Our approach starts from action detection on object proposals at each frame, then aggregates the frame-level detection results belonging to the same actor across the whole video via linking, associating, and tracking to generate action tubes that are spatially compact and temporally continuous. To achieve the target, a novel action detection model with two-stream architecture is firstly proposed, which utilizes the fused feature from both appearance and motion cues and can be trained end-to-end. Then, the association of the action paths is formulated as a maximum set coverage problem with the results of action detection as a priori. We utilize an incremental search algorithm to obtain all the action proposals at one-pass operation with great efficiency, especially while dealing with the video of long duration or with multiple action instances. Finally, a tracking-by-detection scheme is designed to further refine the generated action paths. Extensive experiments on three validation datasets, UCF-Sports, UCF-101 and J-HMDB, show that the proposed approach advances state-of-the-art action detection performance in terms of both accuracy and proposal quality.

User-Qualified Group Search using Bidirectional Sweep Planes

In this paper, we propose a nearest user-qualified group (NUG) query that searches a group of objects to obtain a result. In detail, given a dataset P, query q, distance δ, and cardinality k, the NUG query returns the nearest group of objects from q, such that more than k objects within δ distance from the point, called a representative, are in the group. Although the NUG query has large spectrum of applications, an efficient processing algorithm for NUG queries has not been studied so far. Therefore, we propose the plane sweep-based incremental search algorithm and heuristic that stops the plane sweep early to reduce the search space. A performance study is conducted on both synthetic and real datasets and our experimental results show that the proposed algorithm can improve the query performance in a variety of conditions.

Improving MPGAA* for Extended Visibility Ranges

Multipath Generalized Adaptive A* (MPGAA*) is an A*-based incremental search algorithm for dynamic terrain that can outperform D* for the (realistic) case of limited visibility ranges. A first contribution of this paper is a brief analysis studying why MPGAA* has poor performance for extended visibility ranges, which concludes that MPGAA* carries out an excessive number of heuristic updates. Our second contribution is a method to reduce the number of heuristic updates that preserves optimality. Finally, a third contribution is a variant of MPGAA*, MPGAA*-back, which we show outperforms MPGAA* and D* on a wide range of dynamic grid pathfinding scenarios, and visibility ranges.

Gene Regulatory Network Inferences Using a Maximum-Relevance and Maximum-Significance Strategy.

Recovering gene regulatory networks from expression data is a challenging problem in systems biology that provides valuable information on the regulatory mechanisms of cells. A number of algorithms based on computational models are currently used to recover network topology. However, most of these algorithms have limitations. For example, many models tend to be complicated because of the “large p, small n” problem. In this paper, we propose a novel regulatory network inference method called the maximum-relevance and maximum-significance network (MRMSn) method, which converts the problem of recovering networks into a problem of how to select the regulator genes for each gene. To solve the latter problem, we present an algorithm that is based on information theory and selects the regulator genes for a specific gene by maximizing the relevance and significance. A first-order incremental search algorithm is used to search for regulator genes. Eventually, a strict constraint is adopted to adjust all of the regulatory relationships according to the obtained regulator genes and thus obtain the complete network structure. We performed our method on five different datasets and compared our method to five state-of-the-art methods for network inference based on information theory. The results confirm the effectiveness of our method.

Truncated incremental search

Incremental heuristic search algorithms reuse their previous search efforts whenever these are available. As a result, they can often solve a sequence of similar planning problems faster than planning from scratch. State-of-the-art incremental heuristic searches (such as LPA*, D* and D* Lite) work by propagating cost changes to all the states in the search tree whose g values (the costs of computed paths from the start state) are no longer optimal. This work is based on the observation that while a complete propagation of cost changes is essential to ensure optimality, the propagations can be stopped earlier if we are looking close-to-optimal solutions instead of the optimal one. We develop a framework called Truncated Incremental Search that builds on this observation and uses a target suboptimality bound to efficiently restrict cost propagations. We present two truncation based algorithms, Truncated LPA* (TLPA*) and Truncated D* Lite (TD* Lite), for bounded suboptimal planning and navigation in dynamic graphs. We also develop an anytime replanning algorithm, Anytime Truncated D* (ATD*), that combines the inflated heuristic search with truncation, in an anytime manner. We discuss the theoretical properties of these algorithms proving their correctness and efficiency, and present experimental results on 2D and 3D (x, y, heading) path planning domains evaluating their performance. The empirical results show that the truncated incremental searches can provide significant improvement in runtime over existing incremental search algorithms, especially when searching for close-to-optimal solutions in large, dynamic graphs.

Reusing cost-minimal paths for goal-directed navigation in partially known terrains

Situated agents frequently need to solve search problems in partially known terrains in which the costs of the arcs of the search graphs can increase (but not decrease) when the agents observe new information. An example of such search problems is goal-directed navigation with the freespace assumption in partially known terrains, where agents repeatedly follow cost-minimal paths from their current locations to given goal locations. Incremental heuristic search is an approach for solving the resulting sequences of similar search problems potentially faster than with classical heuristic search, by reusing information from previous searches to speed up its current search. There are two classes of incremental heuristic search algorithms, namely those that make the $$h$$h-values of the current search more informed (such as Adaptive A*) and those that reuse parts of the A* search trees of previous searches during the current search (such as D* Lite). In this article, we introduce Path-Adaptive A* and its generalization Tree-Adaptive A*. Both incremental heuristic search algorithms terminate their searches before they expand the goal state, namely when they expand a state that is on a provably cost-minimal path to the goal. Path-Adaptive A* stores a single cost-minimal path to the goal state (the reusable path), while Tree-Adaptive A* stores a set of cost-minimal paths to the goal state (the reusable tree), and is thus potentially more efficient than Path-Adaptive A* since it uses information from all previous searches and not just the last one. Tree-Adaptive A* is the first incremental heuristic search algorithm that combines the principles of both classes of incremental heuristic search algorithms. We demonstrate experimentally that both Path-Adaptive A* and Tree-Adaptive A* can be faster than Adaptive A* and D* Lite, two state-of-the-art incremental heuristic search algorithms for goal-directed navigation with the freespace assumption.

Selection based inter-relay power allocation for multiple AF single-antenna relays

This article deals with inter-relay power allocation for multiple-input multiple-output systems with multiple single-antenna relays. It is difficult to derive an optimal solution in a closed form for the case of multiple multi-antenna relays as well as the case of multiple single-antenna relays. In this article, we propose an approximate solution which is effective only for the case of multiple single-antenna relays. A key contribution of this article is a low complexity inter-relay power allocation method which is based on relay selection. This approach also reduces the feedback information of the gain factors of all the relays. An incremental greedy search algorithm is also proposed to further reduce the complexity of the relay selection process with negligible performance degradation. Simulations indicate that the performance is comparable to the optimal exhaustive search algorithm.

Space D*

Abstract This paper describes a new method of path-planning for multiple robots in unknown environments. The method, called Space D*, is based on two algorithms: the D*, which is an incremental graph search algorithm, and the Space Colonization algorithm, previously used to simulate crowd behaviors. The path-planning is achieved through the exchange of information between the robots. So decentralized, each robot performs its path-planning, which provides an obstacle-free path with the least number of robots around. The major contribution of the proposed method is that it generates paths in spacious environments facilitating the control of robots and thus presenting itself in a viable way for using in areas populated with multiple robots. The results obtained validate the approach and show the advantages in comparison with using only the D* method.