Heuristics as a cognitive function

The study conceptualizes science fiction as heuristics. To implement this conceptualization, a hybrid definition of science fiction is proposed: science fiction is a kind of fiction whose works can be characterized by secondary artistic conventionality, cognitive estrangement, and test of an intellectual idea or fantastic assumption. As an operational characterization of heuristics, V. Spiridonov’s concept of heuristics is used. Science fiction can be considered as a kind of heuristics under specific conditions, for example, when science fiction work contains the reflected-out heuristics or when heuristics are brought as science fiction work to stimulate the intuitive flash of the thought or insight. However, science fiction can only be regarded as heuristics with certain reservations: science fiction primarily solves artistic problems while heuristics primarily solve cognitive problems: and they can function independently of each other. But it shows that a heuristic function can be attributed to science fiction to solute a problem or to gain a piece of new knowledge (to make a discovery) in an intellectually and creative way.

An Efficient and Robust Improved A* Algorithm for Path Planning

Path planning plays an essential role in mobile robot navigation, and the A* algorithm is one of the best-known path planning algorithms. However, the conventional A* algorithm and the subsequent improved algorithms still have some limitations in terms of robustness and efficiency. These limitations include slow algorithm efficiency, weak robustness, and collisions when robots are traversing. In this paper, we propose an improved A*-based algorithm called EBHSA* algorithm. The EBHSA* algorithm introduces the expansion distance, bidirectional search, heuristic function optimization and smoothing into path planning. The expansion distance extends a certain distance from obstacles to improve path robustness by avoiding collisions. Bidirectional search is a strategy that searches for a path from the start node and from the goal node at the same time. Heuristic function optimization designs a new heuristic function to replace the traditional heuristic function. Smoothing improves path robustness by reducing the number of right-angle turns. Moreover, we carry out simulation tests with the EBHSA* algorithm, and the test results show that the EBHSA* algorithm has excellent performance in terms of robustness and efficiency. In addition, we transplant the EBHSA* algorithm to a robot to verify its effectiveness in the real world.

Accelerating AutoDock VINA with GPUs

AutoDock VINA is one of the most-used docking tools in the early stage of modern drug discovery. It uses a Monte-Carlo based iterated search method and multithreading parallelism scheme on multicore machines to improve docking accuracy and speed. However, virtual screening from huge compound databases is common for modern drug discovery, which puts forward a great demand for higher docking speed of AutoDock VINA. Therefore, we propose a fast method VINA-GPU, which expands the Monte-Carlo based docking lanes into thousands of ones coupling with a largely reduced number of search steps in each lane. Furthermore, we develop a heterogeneous OpenCL implementation of VINA-GPU that leverages thousands of computational cores of a GPU, and obtains a maximum of 403-fold acceleration on docking runtime when compared with a quad-threaded AutoDock VINA implementation. In addition, a heuristic function was fitted to determine the proper size of search steps in each lane for a convenient usage. The VINA-GPU code can be freely available at https://github.com/DeltaGroupNJUPT/Vina-GPU for academic usage.

Accelerating AutoDock VINA with GPUs

AutoDock VINA is one of the most-used docking tools in the early stage of modern drug discovery. It uses a Monte-Carlo based iterated search method and multithreading parallelism scheme on multicore machines to improve docking accuracy and speed. However, virtual screening from huge compound databases is common for modern drug discovery, which puts forward a great demand for higher docking speed of AutoDock VINA. Therefore, we propose a fast method VINA-GPU, which expands the Monte-Carlo based docking lanes into thousands of ones coupling with a largely reduced number of search steps in each lane. Furthermore, we develop a heterogeneous OpenCL implementation of VINA-GPU that leverages thousands of computational cores of a GPU, and obtains a maximum of 403-fold acceleration on docking runtime when compared with a quad-threaded AutoDock VINA implementation. In addition, a heuristic function was fitted to determine the proper size of search steps in each lane for a convenient usage. The VINA-GPU code can be freely available at https://github.com/DeltaGroupNJUPT/VINA-GPU for academic usage.

Autonomous land vehicle path planning algorithm based on improved heuristic function of A-Star

The path planning of autonomous land vehicle has become a research hotspot in recent years. In this article, we present a novel path planning algorithm for an autonomous land vehicle. According to the characteristics of autonomous movement towards the autonomous land vehicle, an improved A-Star path planning algorithm is designed. The disadvantages of using the A-Star algorithm for path planning are that the path planned by the A-Star algorithm contains many unnecessary turning points and is not smooth enough. Autonomous land vehicle needs to adjust its posture at each turning point, which will greatly waste time and also will not be conducive to the motion control of autonomous land vehicle. In view of these shortcomings, this article proposes a new heuristic function combined with the artificial potential field method, which contains both distance information and obstacle information. Our proposed algorithm shows excellent performance in improving the execution efficiency and reducing the number of turning points. The simulation results show that the proposed algorithm, compared with the traditional A-Star algorithm, makes the path smoother and makes the autonomous land vehicle easier to control.

Online Foot Location Planning for Gait Transitioning Using Model Predictive Control

This paper proposes an online uniform foot location planning method (UPMPC) based on model predictive control (MPC) for solving the problem of large posture changes during gait transitioning. This method converts the foot location planning into a discrete-time MPC problem. The core part of the method is to complete the planning of the foot location based on the linear inverted pendulum (LIP) model and the simplified robot dynamics model. By unifying the input foot location at each time step, the solution time is shortened. The final simulation experiment compares the results of using the UPMPC and foot location planning method with heuristic function (HF) for gait transitioning, respectively. This result demonstrates that the UPMPC can complete the gait transitioning task and adapt to large changes in posture during gait transitioning. In addition, the results also show the good performance of UPMPC in fixed gait.

CNN based Heuristic Function for A* Pathfinding Algorithm: Using Spatial Vector Data to Reconstruct Smooth and Natural Looking Plant Roots

In this work we propose an extension to recent methods for the reconstruction of root architectures in 2-dimensions. Recent methods for the automatic root analysis have proposed deep learned segmentation of root images followed by path finding such as Dijkstra’s algorithm to reconstruct root topology. These approaches assume that roots are separate, and that a shortest path within the image foreground represents a reliable reconstruction of the underlying root structure. This approach is prone to error where roots grow in close proximity, with path finding algorithms prone to taking “short cuts” and overlapping much of the root material. Here we extend these methods to also consider root angle, allowing a more informed shortest path search that disambiguates roots growing close together. We adapt a CNN architecture to also predict the angle of root material at each foreground position, and utilise this additional information within shortest path searchers to improve root reconstruction. Our results show an improved ability to separate clustered roots.

Custom-Design of FDR Encodings: The Case of Red-Black Planning

Classical planning tasks are commonly described in PDDL, while most planning systems operate on a grounded finite-domain representation (FDR). The translation of PDDL into FDR is complex and has a lot of choice points---it involves identifying so called mutex groups---but most systems rely on the translator that comes with Fast Downward. Yet the translation choice points can strongly impact performance. Prior work has considered optimizing FDR encodings in terms of the number of variables produced. Here we go one step further by proposing to custom-design FDR encodings, optimizing the encoding to suit particular planning techniques. We develop such a custom design here for red-black planning, a partial delete relaxation technique. The FDR encoding affects the causal graph and the domain transition graph structures, which govern the tractable fragment of red-black planning and hence affects the respective heuristic function. We develop integer linear programming techniques optimizing the scope of that fragment in the resulting FDR encoding. We empirically show that the performance of red-black planning can be improved through such FDR custom design.

An ant colony algorithm model for UAV sense and avoid based on ADS-B

In the context of airspace fusion, in order to improve the safety performance of UAV and prevent the occurrence of air collision accidents, an ant colony algorithm model for UAV sense and avoid based on ADS-B monitoring technology is proposed. The model mainly consists of two parts: the deterministic conflict detection model makes the full use of ADS-B information to calculate the geometric distance from the horizontal and vertical planes to identify the conflict target, and the conflict resolution model is based on the ant colony algorithm which introduces the comprehensive heuristic function and sorting mechanism to plan the route again for achieving the collision avoidance. The simulation results show that the conflict detection model can effectively identify the possible threat targets, and the conflict resolution model is not only suitable for the typical two aircraft conflict scenarios, but also can provide a better resolution strategy for the complex multiple aircraft conflict scenarios.

Polynomial-Time in PDDL Input Size: Making the Delete Relaxation Feasible for Lifted Planning

Polynomial-time heuristic functions for planning are commonplace since 20 years. But polynomial-time in which input? Almost all existing approaches are based on a grounded task representation, not on the actual PDDL input which is exponentially smaller. This limits practical applicability to cases where the grounded representation is "small enough". Previous attempts to tackle this problem for the delete relaxation leveraged symmetries to reduce the blow-up. Here we take a more radical approach, applying an additional relaxation to obtain a heuristic function that runs in time polynomial in the size of the PDDL input. Our relaxation splits the predicates into smaller predicates of fixed arity K. We show that computing a relaxed plan is still NP-hard (in PDDL input size) for K>=2, but is polynomial-time for K=1. We implement a heuristic function for K=1 and show that it can improve the state of the art on benchmarks whose grounded representation is large.