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 Planning for Hybrid Systems via Satisfiability Modulo Theories

2020 ◽  
Vol 67 ◽  
pp. 235-283 ◽  
Author(s):  
Michael Cashmore ◽  
Daniele Magazzeni ◽  
Parisa Zehtabi
Keyword(s):  
Hybrid Systems ◽  
Search Space ◽  
Satisfiability Modulo Theories ◽  
Planning Problem ◽  
Control Parameters ◽  
Continuous Change ◽  
Infinite Domain ◽  
Non Linear ◽  
Planning Algorithm ◽  
Definition Of

Planning for hybrid systems is important for dealing with real-world applications, and PDDL+ supports this representation of domains with mixed discrete and continuous dynamics. In this paper we present a new approach for planning for hybrid systems, based on encoding the planning problem as a Satisfiability Modulo Theories (SMT) formula. This is the first SMT encoding that can handle the whole set of PDDL+ features (including processes and events), and is implemented in the planner SMTPlan. SMTPlan not only covers the full semantics of PDDL+, but can also deal with non-linear polynomial continuous change without discretization. This allows it to generate plans with non-linear dynamics that are correct-by-construction. The encoding is based on the notion of happenings, and can be applied on domains with nonlinear continuous change. We describe the encoding in detail and provide in-depth examples. We apply this encoding in an iterative deepening planning algorithm. Experimental results show that the approach dramatically outperforms existing work in finding plans for PDDL+ problems. We also present experiments which explore the performance of the proposed approach on temporal planning problems, showing that the scalability of the approach is limited by the size of the discrete search space. We further extend the encoding to include planning with control parameters. The extended encoding allows the definition of actions to include infinite domain parameters, called control parameters. We present experiments on a set of problems with control parameters to demonstrate the positive effect they provide to the approach of planning via SMT.

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 Model Checking via Reachability Testing for Timed Automata

1997 ◽  
Vol 4 (29) ◽  
Author(s):  
Luca Aceto ◽  
Augusto Burgueno ◽  
Kim G. Larsen
Keyword(s):  
Model Checking ◽  
Timed Automata ◽  
Reachability Problem ◽  
Liveness Properties ◽  
Time Logic ◽  
Definition Of ◽  
Reachability Testing ◽  
Time Systems ◽  
Timed Automaton ◽  
Theoretical Side

In this paper we develop an approach to model-checking for timed automata via reachability testing. As our specification formalism, we consider a dense-time logic with clocks. This logic may be used to express safety and bounded liveness properties of real-time systems. We show how to automatically synthesize, for every logical formula phi, a so-called test automaton T_phi in such a way that checking whether a system S satisfies the property phi can be reduced to a reachability question over the system obtained by making T_phi interact with S. <br />The testable logic we consider is both of practical and theoretical interest. On the practical side, we have used the logic, and the associated approach to model-checking via reachability testing it supports, in the specification and verification in Uppaal of a collision avoidance protocol. On the theoretical side, we show that the logic is powerful enough to permit the definition of characteristic properties, with respect to a timed version of<br />the ready simulation preorder, for nodes of deterministic, tau-free timed automata. This allows one to compute behavioural relations via our model-checking technique, therefore effectively reducing the problem of checking the existence of a behavioural relation among states of a timed automaton to a reachability problem.

scholarly journals Timed Automata Relaxation for Reachability

2021 ◽  
pp. 291-310
Author(s):  
Jaroslav Bendík ◽  
Ahmet Sencan ◽  
Ebru Aydin Gol ◽  
Ivana Černá
Keyword(s):  
Mixed Integer Linear Programming ◽  
Timed Automata ◽  
Mixed Integer ◽  
Timing Constraints ◽  
System Specification ◽  
Minimal Set ◽  
Novel Concept ◽  
Time Systems ◽  
Sufficient Reduction ◽  
Correct Structure

AbstractTimed automata (TA) have shown to be a suitable formalism for modeling real-time systems. Moreover, modern model-checking tools allow a designer to check whether a TA complies with the system specification. However, the exact timing constraints of the system are often uncertain during the design phase. Consequently, the designer is able to build a TA with a correct structure, however, the timing constraints need to be tuned to make the TA comply with the specification.In this work, we assume that we are given a TA together with an existential property, such as reachability, that is not satisfied by the TA. We propose a novel concept of a minimal sufficient reduction (MSR) that allows us to identify the minimal set S of timing constraints of the TA that needs to be tuned to meet the specification. Moreover, we employ mixed-integer linear programming to actually find a tuning of S that leads to meeting the specification.

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.

scholarly journals SC-M*: A Multi-Agent Path Planning Algorithm with Soft-Collision Constraint on Allocation of Common Resources

2019 ◽  
Vol 9 (19) ◽  
pp. 4037 ◽  
Author(s):  
Rongye Shi ◽  
Peter Steenkiste ◽  
Manuela Veloso
Keyword(s):  
Path Planning ◽  
Traffic Control ◽  
Autonomous Agents ◽  
Solution Space ◽  
Hard Constraints ◽  
Planning Algorithm ◽  
Multi Agent ◽  
Path Cost ◽  
Definition Of ◽  
Path Planning Algorithm

Multi-agent path planning (MAPP) is increasingly being used to address resource allocation problems in highly dynamic, distributed environments that involve autonomous agents. Example domains include surveillance automation, traffic control and others. Most MAPP approaches assume hard collisions, e.g., agents cannot share resources, or co-exist at the same node or edge. This assumption unnecessarily restricts the solution space and does not apply to many real-world scenarios. To mitigate this limitation, this paper introduces a more general class of MAPP problems—MAPP in a soft-collision context. In soft-collision MAPP problems, agents can share resources or co-exist in the same location at the expense of reducing the quality of the solution. Hard constraints can still be modeled by imposing a very high cost for sharing. This paper motivates and defines the soft-collision MAPP problem, and generalizes the widely-used M* MAPP algorithm to support the concept of soft-collisions. Soft-collision M* (SC-M*) extends M* by changing the definition of a collision, so paths with collisions that have a quality penalty below a given threshold are acceptable. For each candidate path, SC-M* keeps track of the reduction in satisfaction level of each agent using a collision score, and it places agents whose collision scores exceed its threshold into a soft-collision set for reducing the score. Our evaluation shows that SC-M* is more flexible and more scalable than M*. It can also handle complex environments that include agents requesting different types of resources. Furthermore, we show the benefits of SC-M* compared with several baseline algorithms in terms of path cost, success rate and run time.

scholarly journals Improving Motion Safety and Efficiency of Intelligent Autonomous Swarm of Drones

Drones ◽  
2020 ◽  
Vol 4 (3) ◽  
pp. 48
Author(s):  
Amin Majd ◽  
Mohammad Loni ◽  
Golnaz Sahebi ◽  
Masoud Daneshtalab
Keyword(s):  
State Of The Art ◽  
Holistic Approach ◽  
Integrated Approach ◽  
Mission Planning ◽  
Operating Environment ◽  
Intelligent Monitoring ◽  
Planning Algorithm ◽  
Definition Of ◽  
Formal Requirements ◽  
Swarm Mission

Interest is growing in the use of autonomous swarms of drones in various mission-physical applications such as surveillance, intelligent monitoring, and rescue operations. Swarm systems should fulfill safety and efficiency constraints in order to guarantee dependable operations. To maximize motion safety, we should design the swarm system in such a way that drones do not collide with each other and/or other objects in the operating environment. On other hand, to ensure that the drones have sufficient resources to complete the required task reliably, we should also achieve efficiency while implementing the mission, by minimizing the travelling distance of the drones. In this paper, we propose a novel integrated approach that maximizes motion safety and efficiency while planning and controlling the operation of the swarm of drones. To achieve this goal, we propose a novel parallel evolutionary-based swarm mission planning algorithm. The evolutionary computing allows us to plan and optimize the routes of the drones at the run-time to maximize safety while minimizing travelling distance as the efficiency objective. In order to fulfill the defined constraints efficiently, our solution promotes a holistic approach that considers the whole design process from the definition of formal requirements through the software development. The results of benchmarking demonstrate that our approach improves the route efficiency by up to 10% route efficiency without any crashes in controlling swarms compared to state-of-the-art solutions.

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.

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