Prioritized Planning Algorithms for Trajectory Coordination of Multiple Mobile Robots (1409.2399v1)

Abstract: An important capability of autonomous multi-robot systems is to prevent collision among the individual robots. One approach to this problem is to plan conflict-free trajectories and let each of the robots follow its pre-planned trajectory. A widely used practical method for multi-robot trajectory planning is prioritized planning, which has been shown to be effective in practice, but is in general incomplete. Formal analysis of instances that are provably solvable by prioritized planning is still missing. Moreover, prioritized planning is a centralized algorithm, which may be in many situations undesirable. In this paper we a) propose a revised version of prioritized planning and characterize the class of instances that are provably solvable by the algorithm and b) propose an asynchronous decentralized variant of prioritized planning, which maintains the desirable properties of the centralized version and in the same time exploits the distributed computational power of the individual robots, which in most situations allows to find the joint trajectories faster. The experimental evaluation performed on real-world indoor maps shows that a) the revised version of prioritized planning reliably solves a wide class of instances on which both classical prioritized planning and popular reactive technique ORCA fail and b) the asynchronous decentralized algorithm provides solution faster than the previously proposed synchronized decentralized algorithm.

• The paper presents a revised prioritized planning algorithm that ensures conflict-free trajectories by avoiding crucial start and goal positions.
• It introduces an asynchronous decentralized variant that leverages parallel processing to resolve trajectory conflicts faster.
• Empirical tests in real-world settings such as offices and warehouses confirm improved efficiency over traditional planning methods.

Insights into Trajectory Coordination for Multi-Robot Systems

The paper "Prioritized Planning Algorithms for Trajectory Coordination of Multiple Mobile Robots" explores the intricacies of autonomous multi-robot systems and their ability to coordinate trajectories to avert collisions. Traditional methodologies include reactive collision avoidance and multi-robot planning, with prioritized planning being a prevalent approach due to its practical efficacy despite inherent incompleteness. The authors propose advancements in trajectory planning with the introduction of two significant contributions: a revised prioritized planning algorithm, and its decentralized asynchronous variant.

Revised Prioritized Planning

The revised algorithm (RPP) refines the classical prioritized planning by ensuring that each robot’s trajectory avoids both lower-priority robots’ start positions and higher-priority robots’ goal positions. This enhancement identifies conditions under which trajectory planning becomes provably solvable, notably when robots navigate a structured transport infrastructure akin to human-designed environments. The RPP has shown improved coverage in instances where traditional algorithms fail, particularly in environments with predefined endpoints. The algorithm is theoretically sound, ensuring termination and soundness, thereby providing conflict-free trajectories for a particular class of problem instances.

Asynchronous Decentralized Implementation

Addressing a significant limitation of centralized algorithms—reliance on a single computational unit—the paper introduces an asynchronous decentralized version of the RPP (AD-RPP). This variant leverages the distributed computational capabilities of individual robots, allowing for quicker convergence to a solution by enabling parallel processing among robots. Asynchronous processing improves computational efficiency, resolving trajectory conflicts in less time compared to synchronized decentralized implementations (SD-RPP), especially as the number of robots increases. The decentralized approach is applicable without a central solver and is robust enough to handle dynamic task changes and communication uncertainties.

Empirical Evidence and Practical Implications

Empirical results gathered from extensive testing in various real-world environments—office corridors, warehouses—underscore the performance improvements granted by both RPP and AD-RPP. Notably, AD-RPP demonstrates superior speed-up ratios in complex scenarios, while RPP exhibits reliable problem-solving in environments designed with transport infrastructures. The practical applicability of these algorithms extends to multi-UAV systems, where dynamic adaptation to task changes and robust collision avoidance are crucial.

Future Research Directions

The research sets a foundation for further exploration into adaptive decentralized solutions, especially emphasizing open systems with local communication constraints. The paper’s contributions hold substantial potential for expanding the capabilities of autonomous robotic systems in navigating complex environments while ensuring safety and operational efficiency.

In summary, the advancements presented in this paper offer significant strides in solving trajectory coordination challenges efficiently, with RPP ensuring provably complete solutions under specific conditions and AD-RPP facilitating rapid adaptive resolution in decentralized settings. These contributions are poised to have far-reaching implications for the design and deployment of autonomous robotic systems across diverse applications.