Robot Path Finding Research Articles

Improved decentralized cooperative multi-agent path finding for robots with limited communication

Improved decentralized cooperative multi-agent path finding for robots with limited communication

An enhanced adaptive 3D path planning algorithm for mobile robots with obstacle buffering and improved Theta* using minimum snap trajectory smoothing

This study proposes an adaptive robot pathfinding algorithm (MS-W-Theta*) based on fused trajectory smoothing with 3D maps. Firstly, we introduce an obstacle buffer in the 3D map, which includes the height and volume of the AGV when carrying shelves. This enhancement improves the robot's path traversal in narrow spaces. Then, we propose an improved adaptive heuristic function and a cost function to optimize the total cost function of the algorithm, based on the obstacle distribution information on the 3D map. This step increases the purposefulness of the planned path. Finally, the original path is smoothed using a Minimum Snap polynomial algorithm to avoid the unstable speed or jitter phenomenon that may occur during the actual operation of the mobile robot and to improve the stability of the robot operation. The experimental results show that MS-W-Theta* exhibits a significant advantage in terms of running time, and the generated path curves better align with the requirements of real-world mobile robot scenarios. The practicality and high efficiency of the algorithm in path planning for workshop logistics robots are demonstrated through experiments, indicating a reduction in running time by 20 % and an improvement in path alignment by 15 % compared to the baseline algorithm.

Multi-Objective PSO with Variable Number of Dimensions for Space Robot Path Optimization

This paper aims to solve the space robot pathfinding problem, formulated as a multi-objective (MO) optimization problem with a variable number of dimensions (VND). This formulation enables the search and comparison of potential solutions with different model complexities within a single optimization run. A novel VND MO algorithm based on the well-known particle swarm optimization (PSO) algorithm is introduced and thoroughly described in this paper. The novel VNDMOPSO algorithm is validated on a set of 21 benchmark problems with different dimensionality settings and compared with two other state-of-the-art VND MO algorithms. Then, it is applied to solve five different instances of the space robot pathfinding problem formulated as a VND MO problem where two objectives are considered: (1) the minimal distance of the selected path, and (2) the minimal energy cost (expressed as the number of turning points). VNDMOPSO shows at least comparable or better convergence on the benchmark problems and significantly better convergence properties on the VND pathfinding problems compared with other VND MO algorithms.

Robot-assisted mobile scanning for automated 3D reconstruction and point cloud semantic segmentation of building interiors

Scan-to-BIM technology can help construction stakeholders to obtain accurate BIM models for building information management and construction progress monitoring. The current practice of scan-to-BIM relies on laser scanning to capture a building's as-built condition, followed by processing scanned point clouds to generate BIM, which is laborious and time-consuming. This study presents a new robot-assisted mobile laser scanning approach using a legged robot and a solid-state LiDAR sensor, for automated 3D reconstruction and point cloud semantic segmentation. The proposed new approach involves integrating the Simultaneous Localisation and Mapping (SLAM) algorithm with robot motion control and pathfinding for 3D mapping to obtain the complete point cloud data. To ensure the completeness and density of scanned point clouds, new scanning fitness metrics and a grid-based direction-by-direction algorithm are designed for robot pathfinding. Then, an enhanced dynamic window approach is established to integrate the motion control of the legged robot with SLAM for reconstructing dense point clouds. Lastly, semantic segmentation is performed using ResPointNet++ to segment architectural and structural components. Experimental results show that the mobile scanning approach achieves a decent performance in point cloud completeness and density, with an overall mIoU of 81.75% for semantic segmentation. The ablation test demonstrates that the proposed approach is superior to the method that does not integrate the robot motion with SLAM. The proposed approach contributes to the efficient collection of high-quality point clouds for building interiors, with an aim to boost building information management.

Conflict-based search with D* lite algorithm for robot path planning in unknown dynamic environments

This study proposes a locally observable robot pathfinding algorithm, conflict-based search with D* lite (CBS-D*), to realize automatic and effective pathfinding in mixed environments with dynamic obstacles. This algorithm takes Manhattan distance as the heuristic function and extends the incremental search range from 1order to 3order neighbors. It presents a prejudgment mechanism of collision avoidance and investigates a wait and circuity strategy to promote pathfinding performance. Compared with the D* lite, the experimental results demonstrate that CBS-D* achieves a higher success rate and obstacle avoidance number, and a lower time step. By this collision avoidance mechanism, CBS-D* gives all successes in pathfinding in various dynamic environments, while D* lite may result in some failures. Specifically, CBS-D* has around 31% in the average success rate of pathfinding improved to D* lite in a 32 × 32 map. Furthermore, CBS-D* gives a superiority of self-adaptability and intelligence in unknown dynamic environments.

Pathfinding algorithm based on rotated block AOR technique in structured environment

<abstract> <p>Harmonic potential fields are commonly used as guidance in a global approach for self-directed robot pathfinding. These harmonic potentials are generated using Laplace's equation solutions. The computation of these harmonic potentials often requires the use of immense amounts of computing resources. This study introduces a numerical technique called Rotated Block Accelerated Over-Relaxation (AOR), also known as Explicit Decoupled Group AOR (EDGAOR), to deal with pathfinding problem. Several robot navigation simulations were performed in a static, structured, known indoor environment to validate the efficiency of the suggested approach. The paths generated by the simulations are shown using several different starting and target positions. The performance of the proposed approach in computing harmonic potentials for solving pathfinding problems is also discussed.</p> </abstract>

CL-MAPF: Multi-Agent Path Finding for Car-Like robots with kinematic and spatiotemporal constraints

Multi-Agent Path Finding has been widely studied in the past few years due to its broad application in the field of robotics and AI. However, previous solvers rely on several simplifying assumptions. This limits their applicability in numerous real-world domains that adopt nonholonomic car-like agents rather than holonomic ones. In this paper, we give a mathematical formalization of the Multi-Agent Path Finding for Car-Like robots (CL-MAPF) problem. We propose a novel hierarchical search-based solver called Car-Like Conflict-Based Search to address this problem. It applies a body conflict tree to address collisions considering the shapes of the agents. We introduce a new algorithm called Spatiotemporal Hybrid-State A* as the single-agent planner to generate agents’ paths satisfying both kinematic and spatiotemporal constraints. We also present a sequential planning version of our method, sacrificing a small amount of solution quality to achieve a significant reduction in runtime. We compare our method with two baseline algorithms on a dedicated benchmark and validate it in real-world scenarios. The experiment results show that the planning success rate of both baseline algorithms is below 50% for all six scenarios, while our algorithm maintains that of over 98%. It also gives clear evidence that our algorithm scales well to 100 agents in 300 m × 300 m scenario and is able to produce solutions that can be directly applied to Ackermann-steering robots in the real world. The benchmark and source code are released in https://github.com/APRIL-ZJU/CL-CBS. The video of the experiments can be found on YouTube.

Path finding and guidance algorithms for autonomous mobile robots in a tree structure network

Considering an autonomous mobile robot that has to navigate indoor and outdoor from a certain point towards another certain point in a tree structure, the paper is giving a solution for finding the path and navigation. The mathematical model is proposed for a number nDP of departure/destination points (DP), a number nIP of intersection points (IP) and connections between them in a tree structure. Based on mathematical model, the problems solved and presented in this paper are i) to find a path from any departure point to any destination point, ii) to generate an algorithm for guidance of an autonomous robot to follow the path. Both algorithms were tested on Matlab Simulink® environment. An implementation of algorithms in a pseudocode is presented. The algorithms were adapted to be implemented on a microcontroller based board. The advantage of algorithms and implementation is simplicity. The outcome is less memory consumption and increased computing and execution speed.

Robot pathfinding with obstacle avoidance capabilities in a static indoor environment via TOR iterative method using harmonic potentials

Mobile robots are always in a state where they have to find a collision-free path in their environment from start to the target point. This study tries to solve the problem of mobile robot iteratively by using a numerical technique. It is based on potential field technique that was modelled using the Laplace’s equation to restrain the creation of a potential functions across regions in the mobile robot’s configuration space. The gradient formed by the potential field is then used to generate a path for the robot to advance through. The present paper proposes a Two-Parameter Over-Relaxation (TOR) iterative method that is used to solve Laplace’s equation for obtaining the potential field that is then utilized for finding path of the robot, thus solving the robot pathfinding problem. The experiment indicates that it is capable of producing a smooth path between the starting and target points through the use of a finite-difference technique. Furthermore, the simulation results show that this numerical approach executes quicker and provides a smoother trail than to the previous works, that includes Successive Over-Relaxation (SOR) and Accelerated Over-Relaxation (AOR) methods.

A Dynamic Fusion Pathfinding Algorithm Using Delaunay Triangulation and Improved A-Star for Mobile Robots

Although many studies exist on mobile robot path planning, the disadvantages of complex algorithms and many path nodes in logistics warehouses and manufacturing workshops are obvious, mainly due to the inconsistency of map environment construction and pathfinding strategies. In this study, to improve the efficiency of mobile robot path planning, the Delaunay triangulation algorithm was used to process complex obstacles and generate Voronoi points as pathfinding priority nodes. The concept of the grid was used to extract obstacle edges to provide obstacle avoidance strategies for robot pathfinding. Subsequently, the search for priority and regular path nodes used the improved A-star (A*) algorithm. The dynamic fusion pathfinding algorithm (DFPA), based on Delaunay triangulation and improved A*, was designed, which realizes the path planning of mobile robots. MATLAB 2016a was used as the simulation software, to firstly verify the correctness of the DFPA, and then to compare the algorithm with other methods. The results show that under the experimental environment with the same start point, goal point, and number of obstacles, the map construction method and pathfinding strategy proposed in this paper reduce the planned path length of the mobile robot, the number of path nodes, and the cost of overall turn consumption, and increase the success rate of obtaining a path. The new dynamic map construction method and pathfinding strategy have important reference significance for processing chaotic maps, promoting intelligent navigation, and site selection planning.

PATH FINDING OF STATIC INDOOR MOBILE ROBOT VIA AOR ITERATIVE METHOD USING HARMONIC POTENTIALS

Mobile robots often have to discover a path of collision-free towards a specific goal point in their environment. We are trying to resolve the mobile robot problem iteratively by means of numerical technique. It is built on a method of potential field that count on the use of Laplace’s equation in the mobile robot’s configuration space to constrain/which reduces the generation of a potential function over regions. This paper proposed an iterative approach in solving robot path finding problem known as Accelerated Over-Relaxation (AOR). The experiment shows that these suggested approach can establish a smooth pat between the starting and goal points by engaging with a finite-difference technique. The simulation results also show that a more rapidly solution with smoother path than the previous work is achieved via this numerical approach.

Multiobjective model-free learning for robot pathfinding with environmental disturbances

This article addresses the robot pathfinding problem with environmental disturbances, where a solution to this problem must consider potential risks inherent in an uncertain and stochastic environment. For example, the movements of an underwater robot can be seriously disturbed by ocean currents, and thus any applied control actions to the robot cannot exactly lead to the desired locations. Reinforcement learning is a formal methodology that has been extensively studied in many sequential decision-making domains with uncertainty, but most reinforcement learning algorithms consider only a single objective encoded by a scalar reward. However, the robot pathfinding problem with environmental disturbances naturally promotes multiple conflicting objectives. Specifically, in this work, the robot has to minimise its moving distance so as to save energy, and, moreover, it has to keep away from unsafe regions as far as possible. To this end, we first propose a multiobjective model-free learning framework, and then proceed to investigate an appropriate action selection strategy by improving a baseline with respect to two dimensions. To demonstrate the effectiveness of the proposed learning framework and evaluate the performance of three action selection strategies, we also carry out an empirical study in a simulated environment.

An Eight-Direction Scanning Detection Algorithm for the Mapping Robot Pathfinding in Unknown Indoor Environment.

Aiming at the problem of how to enable the mobile robot to navigate and traverse efficiently and safely in the unknown indoor environment and map the environment, an eight-direction scanning detection (eDSD) algorithm is proposed as a new pathfinding algorithm. Firstly, we use a laser-based SLAM (Simultaneous Localization and Mapping) algorithm to perform simultaneous localization and mapping to acquire the environment information around the robot. Then, according to the proposed algorithm, the 8 certain areas around the 8 directions which are developed from the robot’s center point are analyzed in order to calculate the probabilistic path vector of each area. Considering the requirements of efficient traverse and obstacle avoidance in practical applications, the proposal can find the optimal local path in a short time. In addition to local pathfinding, the global pathfinding is also introduced for unknown environments of large-scale and complex structures to reduce the repeated traverse. The field experiments in three typical indoor environments demonstrate that deviation of the planned path from the ideal path can be kept to a low level in terms of the path length and total time consumption. It is confirmed that the proposed algorithm is highly adaptable and practical in various indoor environments.

Artificial Neural Fuzzy Logic Algorithm for Robot Path Finding

One of the major challenges of the autonomous navigation for mobile robots is the detection and avoidance of static and dynamic obstacles in the robot's movement. This problem can be solved by applying different methods or algorithms in order to achieve optimal results. Besides optimizing, attention should be paid also to the processing time of the signals from the sensors, as it relates to the time response of the system as a whole in order to detect and avoid obstacles without colliding them. In this paper we mixed, Fuzzy Logic Controller (FLC-Sugeno) and Artificial Neuronal Fuzzy Logic (ANFL). The proposed approaches are tested in simulation environment as well as on the real mobile robot with satisfying results in terms of paths and processing-time.

Design and Implementation of a Multi-Sensor Based Object Detecting and Removing Autonomous Robot Exploration System

Developing autonomous mobile robot system has been a hot topic in AI area. With recent advances in technology, autonomous robots are attracting more and more attention worldwide, and there are a lot of ongoing research and development activities in both industry and academia. In complex ground environment, obstacles positions are uncertain. Path finding for robots in such environment is very hot issues currently. In this paper, we present the design and implementation of a multi-sensor based object detecting and moving autonomous robot exploration system, 4RE, with the VEX robotics design system. With the goals of object detecting and removing in complex ground environment with different obstacles, a novel object detecting and removing algorithms is proposed and implemented. Experimental results indicate that our robot system with our object detecting and removing algorithm can effectively detect the obstacles on the path and remove them in complex ground environment and avoid collision with the obstacles.

Classifying the multi robot path finding problem into a quadratic competitive complexity class

We explore two motion planning problems where a group of mobile robots has to reach a target located in an a priori unknown environment while on-line planning the next step. In the first problem the target position is unknown and should be found by the robots, while in the second problem the target position is known and only a path to it should be found. We focus on optimizing the cost of the task in terms of motion time, which, under the assumption of uniform velocity of all the robots, correlates to the path length passed by the robot which reaches the target. The performance of an on-line algorithm is usually expressed in terms of Competitiveness, the constant ratio between the on-line and the optimal off-line solutions. Specifically, the ratio between the lengths of the actual path made by the robot which reached the target to the shortest path to the target. We use generalized competitiveness, i.e., the ratio is not necessarily constant, but could be any function. Classification of a motion planning task in the sense of performance is done by finding an upper and a lower bounds on the competitiveness of all algorithms solving that task. If the two bounds belong to the same functional class this is the Competitive Complexity Class of the task. We find the two bounds for the aforementioned common on-line motion planning problems, and classify them into competitive classes. It is shown that in general any on-line motion planning algorithm that tries to solve these problems must have at least a quadratic competitive performance. This is a lower bound of the problems. This paper describes two new on-line navigation algorithm which solve the problems under discussion. The first is called MRSAM, short for Multi-Robot Search Area Multiplication, and the second is called MRBUG, short for Multi-Robot BUG which extends Lumelsky famous BUG algorithm. Both algorithms have quadratic upper bounds, which prove that the problems they solve have quadratic upper bounds. Thus it is shown that navigation in an unknown environment by a group of robots belongs to a quadratic competitive class. MRSAM and MRBUG have a quadratic competitive performance and thus have optimal competitiveness. The algorithms' performance is simulated in office-like environments.

Quantitative perceptual representation of prepositional semantics

In this paper we concentrate on spatial prepositions, more specifically we are interested here in projective prepositions (eg. “in front of”, “to the left of”) which have in the past been treated as semantically uninteresting. We demonstrate that projective prepositions are in fact problematic and demand more attention than they have so far been afforded; after summarising the important components of their meaning, we review the deficiencies of past and current approaches to the decoding problem; that is, predicting what a locative expression used in a particular situation conveys. Finally we present our own approach. Motivated by the shortcomings of contemporary work, we integrate elements of Lang's conceptual representation of objects' perceptual and dimensional characteristics, and the potential field model of object proximity that originated in manipulator and mobile robot path-finding.

A reinforcement connectionist approach to robot path finding in non-maze-like environments

This paper presents a reinforcement connectionist system which finds and learns the suitable situation-action rules so as to generate feasible paths for a point robot in a 2D environment with circular obstacles. The basic reinforcement algorithm is extended with a strategy for discovering stable solution paths. Equipped with this strategy and a powerful codification scheme, the path-finder (i) learns quickly, (ii) deals with continuous-valued inputs and outputs, (iii) exhibits good noise-tolerance and generalization capabilities, (iv) copes with dynamic environments, and (v) solves an instance of the path finding problem with strong performance demands.