Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

An Optimal Task Planning and Agent-aware Allocation Algorithm in Collaborative Tasks Combining with PDDL and POPF

Qiguang Chen, Ya-Jun Pan

TL;DR

This work addresses the need for flexible, optimal task planning in Industry 4.0 by integrating PDDL-based planning with the POPF planner and a novel task allocation cost model. The approach encodes actions in PDDL, computes agent-aware costs across feasibility, reachability, safety, and cooperation, and uses POPF to derive an optimal plan with appropriate agent assignments. Key contributions include (i) an action library translated into PDDL, (ii) a four-factor cost function that guides task allocation, and (iii) real-time validation with two manipulators and a human worker demonstrating adaptive planning from initial to goal states. The framework enables autonomous orchestration of collaborative tasks, reducing reliance on rigid pre-programmed production lines and enabling responsive operation in mixed human-robot teams.

Abstract

Industry 4.0 proposes the integration of artificial intelligence (AI) into manufacturing and other industries to create smart collaborative systems which enhance efficiency. The aim of this paper is to develop a flexible and adaptive framework to generate optimal plans for collaborative robots and human workers to replace rigid, hard-coded production line plans in industrial scenarios. This will be achieved by integrating the Planning Domain Definition Language (PDDL), Partial Order Planning Forwards (POPF) task planner, and a task allocation algorithm. The task allocation algorithm proposed in this paper generates a cost function for general actions in the industrial scenario, such as PICK, PLACE, and MOVE, by considering practical factors such as feasibility, reachability, safety, and cooperation level for both robots and human agents. The actions and costs will then be translated into a language understandable by the planning system using PDDL and fed into POPF solver to generate an optimal action plan. In the end, experiments are conducted where assembly tasks are executed by a collaborative system with two manipulators and a human worker to test the feasibility of the theory proposed in this paper.

An Optimal Task Planning and Agent-aware Allocation Algorithm in Collaborative Tasks Combining with PDDL and POPF

TL;DR

This work addresses the need for flexible, optimal task planning in Industry 4.0 by integrating PDDL-based planning with the POPF planner and a novel task allocation cost model. The approach encodes actions in PDDL, computes agent-aware costs across feasibility, reachability, safety, and cooperation, and uses POPF to derive an optimal plan with appropriate agent assignments. Key contributions include (i) an action library translated into PDDL, (ii) a four-factor cost function that guides task allocation, and (iii) real-time validation with two manipulators and a human worker demonstrating adaptive planning from initial to goal states. The framework enables autonomous orchestration of collaborative tasks, reducing reliance on rigid pre-programmed production lines and enabling responsive operation in mixed human-robot teams.

Abstract

Industry 4.0 proposes the integration of artificial intelligence (AI) into manufacturing and other industries to create smart collaborative systems which enhance efficiency. The aim of this paper is to develop a flexible and adaptive framework to generate optimal plans for collaborative robots and human workers to replace rigid, hard-coded production line plans in industrial scenarios. This will be achieved by integrating the Planning Domain Definition Language (PDDL), Partial Order Planning Forwards (POPF) task planner, and a task allocation algorithm. The task allocation algorithm proposed in this paper generates a cost function for general actions in the industrial scenario, such as PICK, PLACE, and MOVE, by considering practical factors such as feasibility, reachability, safety, and cooperation level for both robots and human agents. The actions and costs will then be translated into a language understandable by the planning system using PDDL and fed into POPF solver to generate an optimal action plan. In the end, experiments are conducted where assembly tasks are executed by a collaborative system with two manipulators and a human worker to test the feasibility of the theory proposed in this paper.
Paper Structure (13 sections, 11 equations, 8 figures, 3 tables)

This paper contains 13 sections, 11 equations, 8 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Problem Formulation
  3. Task Planning Algorithm
  4. Task Allocation Algorithm
  5. Feasibility
  6. Agent Strength
  7. System's Information Level
  8. Agent Flexibility
  9. Reachability
  10. Safety
  11. Cooperation Level
  12. Experiment Results
  13. Conclusions and Future Work

Figures (8)

  • Figure 1: A block diagram of task planning algorithm
  • Figure 2: Illustration of the original Fitts list
  • Figure 3: Capability cost of a typical 7-DOF robot arm. The colors indicate the suitability of allowing a manipulator to operate in certain areas: blue represents the most suitable region, orange represents a suitable region, and red represents an unsuitable region.
  • Figure 4: Setup Concept Diagram
  • Figure 5: Experiment Setup
  • ...and 3 more figures