A Combination of Theta*, ORCA and Push and Rotate for Multi-agent Navigation (2008.01227v1)

Abstract: We study the problem of multi-agent navigation in static environments when no centralized controller is present. Each agent is controlled individually and relies on three algorithmic components to achieve its goal while avoiding collisions with the other agents and the obstacles: i) individual path planning which is done by Theta* algorithm; ii) collision avoidance while path following which is performed by ORCA* algorithm; iii) locally-confined multi-agent path planning done by Push and Rotate algorithm. The latter component is crucial to avoid deadlocks in confined areas, such as narrow passages or doors. We describe how the suggested components interact and form a coherent navigation pipeline. We carry out an extensive empirical evaluation of this pipeline in simulation. The obtained results clearly demonstrate that the number of occurring deadlocks significantly decreases enabling more agents to reach their goals compared to techniques that rely on collision-avoidance only and do not include multi-agent path planning component

• The paper presents a decentralized pipeline that integrates Theta* for path planning, ORCA for collision avoidance, and Push and Rotate for resolving deadlocks.
• It shows significant performance gains by achieving a 95% success rate in narrow passages where ORCA-only methods fail.
• The approach provides a scalable solution for multi-agent navigation in static, obstacle-laden environments by enabling temporary agent collaboration.

A Combination of Theta*, ORCA, and Push and Rotate for Multi-agent Navigation

The discussed paper presents a robust algorithmic pipeline for decentralized multi-agent navigation in static environments devoid of a centralized controller. The core approach synergizes three algorithmic components: Theta* for individual path planning, Optimal Reciprocal Collision Avoidance (ORCA) for collision avoidance during path following, and Push and Rotate for locally-confined multi-agent path planning. This integration aims to effectively address deadlock situations, often encountered in confined spaces such as narrow passages or doorways, by enabling collaborative behavior among agents temporarily.

Methodology Overview

The paper introduces a decentralized navigation methodology that combines elements from both independent path planning and temporary collaborative pathfinding. Each agent independently plans its route using the Theta* algorithm, known for providing near-optimal, any-angle paths on grid-based representations of the workspace. Individual agents proceed towards their waypoints using ORCA to ensure collision-free movements by dynamically adjusting their velocities based on local observations.

Deadlocks, which occur when agents pursuing their goals independently cause a standstill, are mitigated using a locally coordinated strategy. Here, the Push and Rotate algorithm is employed in specific scenarios where patterns could lead to potential deadlocks. This component is activated when certain conditions in the agent's environment are met, involving a subset of agents in locally-confined multi-agent pathfinding. This ensures that all involved agents transition from potential deadlock states back to their decentralized path-following behaviors once the coordinated segment of the plan is executed successfully.

Evaluation and Results

The proposed method, ORCA*+PR, was subjected to rigorous empirical evaluations through simulations. Various grid-based scenarios with increasing agent densities and complex layouts were used to challenge the method's effectiveness compared to a baseline approach that only used ORCA for collision avoidance. The simulations indicated that ORCA*+PR notably improved the success rate of navigation tasks, particularly in congested environments with narrow passages. For instance, ORCA*+PR achieved a 95% success rate in environments with a single passage, a scenario where the ORCA-only approach completely failed. Across all tested scenarios, ORCA*+PR consistently outperformed the decentralized baseline, emphasizing the importance of incorporating local multi-agent pathfinding mechanisms in reducing deadlocks and enhancing task success rates.

Implications and Future Directions

The paper's integration of decentralized and centralized elements into a cohesive multi-agent navigation strategy signifies a pivotal step towards scalable, cooperative autonomous systems in structured environments. The hybrid methodology presented offers a balance between the decentralized flexibility and the coordinated assurance necessary for effective multi-agent deployment in complex, obstacle-laden spaces.

Future work may focus on refining deadlock detection mechanisms, potentially employing machine learning techniques to predict and avert deadlock scenarios more efficiently. Additionally, exploring alternative multi-agent pathfinding algorithms that strike a better compromise between computational efficiency and execution speed could further enhance the system's overall performance. Practical deployment on physical robot systems and validation in real-world scenarios would also be valuable for establishing the utility and robustness of the proposed navigation pipeline.