[論文レビュー] Cooperative Task and Motion Planning for Multi-Arm Assembly Systems

Jingkai Chen, Jiaoyang Li+8 more|arXiv (Cornell University)|Mar 4, 2022

Modular Robots and Swarm Intelligence被引用数 20

ひとこと要約

本論文は、MILPベースの高レベルタスク割り当てと衝突認識付き低レベルマルチエージェントパス探索を組み合わせ、複雑なマルチロボット構造を安全かつ効率的に組み立てる階層的タスクとモーションプランニングのフレームワークを提示し、さまざまなドメインで最大23個のオブジェクトを用いて実証した。

ABSTRACT

Multi-robot assembly systems are becoming increasingly appealing in manufacturing due to their ability to automatically, flexibly, and quickly construct desired structural designs. However, effectively planning for these systems in a manner that ensures each robot is simultaneously productive, and not idle, is challenging due to (1) the close proximity that the robots must operate in to manipulate the structure and (2) the inherent structural partial orderings on when each part can be installed. In this paper, we present a task and motion planning framework that jointly plans safe, low-makespan plans for a team of robots to assemble complex spatial structures. Our framework takes a hierarchical approach that, at the high level, uses Mixed-integer Linear Programs to compute an abstract plan comprised of an allocation of robots to tasks subject to precedence constraints and, at the low level, builds on a state-of-the-art algorithm for Multi-Agent Path Finding to plan collision-free robot motions that realize this abstract plan. Critical to our approach is the inclusion of certain collision constraints and movement durations during high-level planning, which better informs the search for abstract plans that are likely to be both feasible and low-makespan while keeping the search tractable. We demonstrate our planning system on several challenging assembly domains with several (sometimes heterogeneous) robots with grippers or suction plates for assembling structures with up to 23 objects involving Lego bricks, bars, plates, or irregularly shaped blocks.

研究の動機と目的

• ロボット組立の柔軟な自動化を促進し、手動の再プログラミングの必要性を低減する。
• 複数のロボットに対して、タスク割り当てと衝突のないモーションを共同最適化するフレームワークを開発する。
• 高レベル計画に重要な衝突情報と継続時間情報を組み込み、実現性と効率を向上させる。
• 近接動作の中で複雑な構造物を組み立てる異種ロボットチームへの適用性を示す。

提案手法

• モードグラフを用いてロボットの操作手順と前提制約されたタスクを記述する。
• 各ロボットについて、物体との実行可能な動きと相互作用を捉える多モーダルロードマップを生成する。
• 主要な衝突と前提制約を組み込みつつ、タスクをロボットに割り当てるためにリラックスした MILP を解く。
• ロードマップ間の潜在的な衝突を注釈付けし、PBS-AT のような優先度ベースのデカップリングMAPF風探索を用いて衝突のないモーションを生成する。
• MILP解からサブプラン列を抽出し、moving obstaclesとprecedenceを扱うための優先度付けされた反復的パスプランニング手法とともに rSIPP を適用する。

実験結果

リサーチクエスチョン

• RQ1 How can task allocation and motion planning be integrated for multi-arm assembly to minimize makespan?
• RQ2 What high-level constraints (e.g., collisions, precedence) are essential to ensure feasible, collision-free low-level plans?
• RQ3 Can a hierarchical approach with MILP-based task assignment and MAPF-inspired planning scale to complex assemblies with multiple robots and objects?
• RQ4 How does incorporating collision-aware, task-relevant information at the high level influence plan feasibility and performance?

主な発見

• The framework yields safe, low-makespan plans for teams of robots assembling structures with up to 23 objects across diverse domains.
• Collision constraints and movement durations included during high-level planning improve feasibility and guide the search toward low-makespan solutions.
• The MILP-based task assignment effectively maps tasks to robots while respecting precedence and roadmap connectivity.
• The PBS-AT algorithm extends priority-based search to handle assigned tasks and moving obstacles, producing collision-free, precedence-compliant plans.
• Roadmap generation and annotation steps enable scalable planning by reusing collision information and decomposing the problem into tractable subproblems.