scholarly journals On Reasonable and Forced Goal Orderings and their Use in an Agenda-Driven Planning Algorithm

2000 ◽  
Vol 12 ◽  
pp. 338-386 ◽  
Author(s):  
J. Koehler ◽  
J. Hoffmann
Keyword(s):  
Ordered Set ◽  
Empirical Evaluation ◽  
Planning Problem ◽  
Practical Relevance ◽  
Ai Planning ◽  
Conditional Effects ◽  
Planning Algorithm ◽  
Speed Up ◽  
Exponential Complexity ◽  
Complex Planning

The paper addresses the problem of computing goal orderings, which is one of the longstanding issues in AI planning. It makes two new contributions. First, it formally defines and discusses two different goal orderings, which are called the reasonable and the forced ordering. Both orderings are defined for simple STRIPS operators as well as for more complex ADL operators supporting negation and conditional effects. The complexity of these orderings is investigated and their practical relevance is discussed. Secondly, two different methods to compute reasonable goal orderings are developed. One of them is based on planning graphs, while the other investigates the set of actions directly. Finally, it is shown how the ordering relations, which have been derived for a given set of goals G, can be used to compute a so-called goal agenda that divides G into an ordered set of subgoals. Any planner can then, in principle, use the goal agenda to plan for increasing sets of subgoals. This can lead to an exponential complexity reduction, as the solution to a complex planning problem is found by solving easier subproblems. Since only a polynomial overhead is caused by the goal agenda computation, a potential exists to dramatically speed up planning algorithms as we demonstrate in the empirical evaluation, where we use this method in the IPP planner.

Research on pull-type multi-AGV system dynamic path optimization based on time window

2021 ◽  
pp. 095440702098271
Author(s):  
Hongying Shan ◽  
Chuang Wang ◽  
Cungang Zou ◽  
Mengyao Qin
Keyword(s):  
Path Planning ◽  
Time Window ◽  
Planning Problem ◽  
Dijkstra Algorithm ◽  
Before And After ◽  
Common Planning ◽  
Planning Algorithm ◽  
Dynamic Path ◽  
Set Up ◽  
Path Planning Algorithm

This paper is a study of the dynamic path planning problem of the pull-type multiple Automated Guided Vehicle (multi-AGV) complex system. First, based on research status at home and abroad, the conflict types, common planning algorithms, and task scheduling methods of different AGV complex systems are compared and analyzed. After comparing the different algorithms, the Dijkstra algorithm was selected as the path planning algorithm. Secondly, a mathematical model is set up for the shortest path of the total driving path, and a general algorithm for multi-AGV collision-free path planning based on a time window is proposed. After a thorough study of the shortcomings of traditional single-car planning and conflict resolution algorithms, a time window improvement algorithm for the planning path and the solution of the path conflict covariance is established. Experiments on VC++ software showed that the improved algorithm reduces the time of path planning and improves the punctual delivery rate of tasks. Finally, the algorithm is applied to material distribution in the OSIS workshop of a C enterprise company. It can be determined that the method is feasible in the actual production and has a certain application value by the improvement of the data before and after the comparison.

Collision-Free Motion Planning for an Aligned Multiple-turret System Operating in Extreme Environment

Robotica ◽  
2021 ◽  
pp. 1-30
Author(s):  
Ümit Yerlikaya ◽  
R.Tuna Balkan
Keyword(s):  
Path Planning ◽  
Extreme Environment ◽  
Configuration Spaces ◽  
Planning Problem ◽  
Different Types ◽  
Planning Algorithm ◽  
Line Path ◽  
Path Planning Algorithm ◽  
Path Planning Problem ◽  
System Operating

Abstract Instead of using the tedious process of manual positioning, an off-line path planning algorithm has been developed for military turrets to improve their accuracy and efficiency. In the scope of this research, an algorithm is proposed to search a path in three different types of configuration spaces which are rectangular-, circular-, and torus-shaped by providing three converging options named as fast, medium, and optimum depending on the application. With the help of the proposed algorithm, 4-dimensional (D) path planning problem was realized as 2-D + 2-D by using six sequences and their options. The results obtained were simulated and no collision was observed between any bodies in these three options.

scholarly journals Narrative Planning: Balancing Plot and Character

2010 ◽  
Vol 39 ◽  
pp. 217-268 ◽  
Author(s):  
M. O. Riedl ◽  
R. M. Young
Keyword(s):  
Partial Order ◽  
Empirical Evaluation ◽  
Causal Link ◽  
World State ◽  
Narrative Generation ◽  
Planning Algorithm ◽  
Suspension Of Disbelief ◽  
Narrative Planning ◽  
Intentional Agents ◽  
Computational Systems

Narrative, and in particular storytelling, is an important part of the human experience. Consequently, computational systems that can reason about narrative can be more effective communicators, entertainers, educators, and trainers. One of the central challenges in computational narrative reasoning is narrative generation, the automated creation of meaningful event sequences. There are many factors -- logical and aesthetic -- that contribute to the success of a narrative artifact. Central to this success is its understandability. We argue that the following two attributes of narratives are universal: (a) the logical causal progression of plot, and (b) character believability. Character believability is the perception by the audience that the actions performed by characters do not negatively impact the audience's suspension of disbelief. Specifically, characters must be perceived by the audience to be intentional agents. In this article, we explore the use of refinement search as a technique for solving the narrative generation problem -- to find a sound and believable sequence of character actions that transforms an initial world state into a world state in which goal propositions hold. We describe a novel refinement search planning algorithm -- the Intent-based Partial Order Causal Link (IPOCL) planner -- that, in addition to creating causally sound plot progression, reasons about character intentionality by identifying possible character goals that explain their actions and creating plan structures that explain why those characters commit to their goals. We present the results of an empirical evaluation that demonstrates that narrative plans generated by the IPOCL algorithm support audience comprehension of character intentions better than plans generated by conventional partial-order planners.

Multi-Car Convex Feasible Set Algorithm in Trajectory Planning

2020 ◽  
Author(s):  
Jing Huang ◽  
Changliu Liu
Keyword(s):  
Convex Optimization ◽  
Quadratic Programming ◽  
Real Time ◽  
Trajectory Planning ◽  
Autonomous Driving ◽  
Curse Of Dimensionality ◽  
Planning Problem ◽  
Feasible Set ◽  
Planning Algorithm ◽  
Vehicle Trajectory

Abstract Trajectory planning is an essential module for autonomous driving. To deal with multi-vehicle interactions, existing methods follow the prediction-then-plan approaches which first predict the trajectories of others then plan the trajectory for the ego vehicle given the predictions. However, since the true trajectories of others may deviate from the predictions, frequent re-planning for the ego vehicle is needed, which may cause many issues such as instability or deadlock. These issues can be overcome if all vehicles can form a consensus by solving the same multi-vehicle trajectory planning problem. Then the major challenge is how to efficiently solve the multi-vehicle trajectory planning problem in real time under the curse of dimensionality. We introduce a novel planner for multi-vehicle trajectory planning based on the convex feasible set (CFS) algorithm. The planning problem is formulated as a non-convex optimization. A novel convexification method to obtain the maximal convex feasible set is proposed, which transforms the problem into a quadratic programming. Simulations in multiple typical on-road driving situations are conducted to demonstrate the effectiveness of the proposed planning algorithm in terms of completeness and optimality.

Extended Automata for Temporal Planning of Interacting Agents

2017 ◽  
Vol 5 (1) ◽  
pp. 30-48
Author(s):  
Christine Largouët ◽  
Omar Krichen ◽  
Yulong Zhao
Keyword(s):  
Timed Automata ◽  
Marine Ecosystem ◽  
Planning Problem ◽  
Management Problem ◽  
Timing Constraints ◽  
Goal State ◽  
Interacting Agents ◽  
Planning Algorithm ◽  
Definition Of ◽  
Classical Transport

In this paper, the authors consider the planning problem for a system represented as a set of interacting agents evolving along time according to explicit timing constraints. Given a goal, the planning problem is to find the sequence of actions such that the system reaches the goal state in a limited time and in an optimal manner, assuming actions have a cost. In their approach, the planning problem is based on model-checking and controller synthesis techniques while the goal is defined using temporal logic. Each agent of the system is represented using the formalism of Priced Timed Game Automata (PTGA). PTGA is an extension of Timed Automata that allows the representation of cost on actions and the definition of uncontrollable actions. The authors define a planning algorithm that computes the best strategy to achieve a goal. To experiment their approach, they extend the classical Transport Domain with timing constraints, cost on actions and uncontrollable actions. The planning algorithm is finally presented on a marine ecosystem management problem.

scholarly journals A sampling-based optimized algorithm for task-constrained motion planning

2019 ◽  
Vol 16 (3) ◽  
pp. 172988141984737 ◽  
Author(s):  
Kai Mi ◽  
Haojian Zhang ◽  
Jun Zheng ◽  
Jianhua Hu ◽  
Dengxiang Zhuang ◽  
...  
Keyword(s):  
Motion Planning ◽  
Sampling Methods ◽  
Tracking Error ◽  
Joint Space ◽  
Planning Problem ◽  
Redundant Manipulators ◽  
Task Space ◽  
Constrained Motion ◽  
Smooth Path ◽  
Planning Algorithm

We consider a motion planning problem with task space constraints in a complex environment for redundant manipulators. For this problem, we propose a motion planning algorithm that combines kinematics control with rapidly exploring random sampling methods. Meanwhile, we introduce an optimization structure similar to dynamic programming into the algorithm. The proposed algorithm can generate an asymptotically optimized smooth path in joint space, which continuously satisfies task space constraints and avoids obstacles. We have confirmed that the proposed algorithm is probabilistically complete and asymptotically optimized. Finally, we conduct multiple experiments with path length and tracking error as optimization targets and the planning results reflect the optimization effect of the algorithm.

Cooperative Trajectory Planning for Multi-UCAV Performing Air-to-Ground Target Attack Missions

2013 ◽  
Vol 718-720 ◽  
pp. 1329-1334 ◽  
Author(s):  
Xue Qiang Gu ◽  
Yu Zhang ◽  
Shao Fei Chen ◽  
Jing Chen
Keyword(s):  
Trajectory Planning ◽  
Differential Flatness ◽  
Planning Problem ◽  
Temporal Constraints ◽  
Differential Flatness Theory ◽  
Spline Curves ◽  
Ground Target ◽  
Planning Algorithm ◽  
Virtual Time ◽  
Unmanned Combat Aerial Vehicles

The problem of planning flight trajectories is studied for multiple unmanned combat aerial vehicles (UCAVs) performing a cooperated air-to-ground target attack (CA/GTA) mission. Several constraints including individual and cooperative constraints are modeled, and an objective function is constructed. Then, the cooperative trajectory planning problem is formulated as a cooperative trajectory optimal control problem (CTP-OCP). Moreover, in order to handle the temporal constraints, a notion of the virtual time based strategy is introduced. Afterwards, a planning algorithm based on the differential flatness theory and B-spline curves is developed to solve the CTP-OCP. Finally, the proposed approach is demonstrated using a typical CA/GTA mission scenario, and the simulation results show that the proposed approach is feasible and effective.

scholarly journals Inverse kinematics-based motion planning for dual-arm robot with orientation constraints

2019 ◽  
Vol 16 (2) ◽  
pp. 172988141983685 ◽  
Author(s):  
Jiangping Wang ◽  
Shirong Liu ◽  
Botao Zhang ◽  
Changbin Yu
Keyword(s):  
Motion Planning ◽  
Joint Space ◽  
Inverse Kinematic ◽  
Planning Problem ◽  
Constraint Manifold ◽  
Wide Range ◽  
Planning Algorithm ◽  
Motion Paths ◽  
Dual Arm Robot ◽  
Dual Arm

This article proposes an efficient and probabilistic complete planning algorithm to address motion planning problem involving orientation constraints for decoupled dual-arm robots. The algorithm is to combine sampling-based planning method with analytical inverse kinematic calculation, which randomly samples constraint-satisfying configurations on the constraint manifold using the analytical inverse kinematic solver and incrementally connects them to the motion paths in joint space. As the analytical inverse kinematic solver is applied to calculate constraint-satisfying joint configurations, the proposed algorithm is characterized by its efficiency and accuracy. We have demonstrated the effectiveness of our approach on the Willow Garage’s PR2 simulation platform by generating trajectory across a wide range of orientation-constrained scenarios for dual-arm manipulation.

Software Development for Setup Planning of Rotational Part

2015 ◽  
Vol 4 (1) ◽  
Author(s):  
Sumit Dwivedi ◽  
Shahnawaz Alam
Keyword(s):  
Tolerance Analysis ◽  
Control Technique ◽  
Planning Problem ◽  
Setup Planning ◽  
Tolerance Chart ◽  
Efficiency And Effectiveness ◽  
Planning Algorithm ◽  
Rotational Part ◽  
Sound Foundation ◽  
Tolerance Control

An innovative approach was developed to solve the problem of setup planning, which is the most critical problem in process planning for discrete metal parts. Setup planning is the act of preparing detailed work instructions for setting up a part. The major objective of this research is to improve the performance of CAPP systems by developing a systematic approach to generate practical setup plans based on tolerance analysis. A comprehensive literature review on tolerance control in CAPP was conducted. It was found that tolerance chart analysis, a traditional tolerance control technique, is reactive in nature and can be greatly improved by solving the problem of setup planning. In order to develop a theoretically sound foundation for tolerance analysis-based setup planning, the problem of tolerance stack up in NC machining was analyzed in terms of manufacturing error analysis. Guidelines for setup planning were then developed based on the analysis. To systematically solve the setup planning problem, a graph theoretical setup planning algorithm for rotational parts was then developed for automated and integrated setup planning and fixture design. Its efficiency and effectiveness evaluated. The result is promising. The algorithms were then computerized. A setup planning program was developed under the Microsoft Windows environment using C.

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