Scalable Conflict-Based Model Predictive Control for Multi-Robot Motion Planning

Core Concepts

A scalable multi-robot motion planning algorithm that combines an efficient high-level conflict resolution mechanism with Model Predictive Control (MPC) as the low-level planner to efficiently resolve inter-agent collision constraints through constraint splitting.

Abstract

The paper presents a Conflict-Based Model Predictive Control (CB-MPC) algorithm for scalable multi-robot motion planning. The key aspects are: CB-MPC leverages a modified high-level conflict tree inspired by Conflict-Based Search (CBS) to efficiently resolve robot-robot conflicts in the continuous space, while reasoning about each agent's kinematic and dynamic constraints and actuation limits using MPC as the low-level planner. Tracking high-level multi-robot plans with a vanilla MPC controller is shown to be insufficient, resulting in unexpected collisions in tight navigation scenarios under realistic execution. Compared to other variations of multi-robot MPC like joint, prioritized, and distributed, CB-MPC is demonstrated to improve the executability and success rate, allow for closer robot-robot interactions, and scale better with higher numbers of robots without compromising the solution quality across a variety of environments. The key innovation is the efficient constraint splitting mechanism in CB-MPC that resolves conflicts collaboratively, in contrast to adding constraints from all other agents at every timestep as done in other multi-robot MPC approaches. Extensive experiments in narrow, open, and randomized environments show that CB-MPC outperforms the baselines in terms of success rate and scalability, without compromising solution quality.

Stats

Makespan of 8.35 for CB-MPC vs 8.45 for Joint-MPC in the narrow environment. Average solve time per robot of 0.10 for CB-MPC vs 0.32 for Pr-MPC in the open environment with 6 robots. Maximum solve time per fleet of 3.50 for CB-MPC vs 4.47 for Pr-MPC in the open environment with 8 robots. Average number of constraints added per timestep per robot of 9.90 for CB-MPC vs 22.5 for Pr-MPC in the open environment with 4 robots.

Quotes

"Compared to other variations of multi-robot MPC like joint, prioritized, and distributed, CB-MPC improves the executability and success rate, allows for closer robot-robot interactions, and scales better with higher numbers of robots without compromising the solution quality across a variety of environments." "The key innovation is the efficient constraint splitting mechanism in CB-MPC that resolves conflicts collaboratively, in contrast to adding constraints from all other agents at every timestep as done in other multi-robot MPC approaches."

Key Insights Distilled From

Conflict-Based Model Predictive Control for Scalable Multi-Robot Motion Planning

by Ardalan Tajb... at arxiv.org 04-02-2024

https://arxiv.org/pdf/2303.01619.pdf

Deeper Inquiries

How can the CB-MPC algorithm be extended to handle dynamic obstacles or moving targets in the environment

To extend the Conflict-Based Model Predictive Control (CB-MPC) algorithm to handle dynamic obstacles or moving targets in the environment, several modifications and enhancements can be implemented. One approach could involve integrating a dynamic obstacle prediction module that forecasts the future positions of moving obstacles based on their current trajectories. This prediction information can then be used to proactively generate collision constraints for these dynamic obstacles in the CB-MPC algorithm. By continuously updating these constraints based on real-time sensor data or predictive models, the algorithm can adapt to the changing environment and avoid collisions with dynamic obstacles or moving targets. Another strategy could involve incorporating adaptive planning horizons that adjust based on the predicted motion of dynamic obstacles. By dynamically extending or contracting the planning horizon in response to the anticipated movements of obstacles, the CB-MPC algorithm can optimize trajectories that account for the changing environment. Additionally, introducing reactive collision avoidance techniques, such as velocity adjustments or trajectory replanning, can further enhance the algorithm's ability to navigate around dynamic obstacles effectively.

What are the potential limitations of the conflict resolution mechanism in CB-MPC, and how could it be further improved to handle more complex multi-robot scenarios

While the Conflict-Based Model Predictive Control (CB-MPC) algorithm offers significant advantages in terms of scalability and performance, there are potential limitations in its conflict resolution mechanism that could be further improved for handling more complex multi-robot scenarios. One limitation is the depth of the conflict tree, which may grow exponentially with the number of robots and conflicts, leading to increased computational complexity. To address this, advanced conflict pruning techniques, heuristic-based conflict prioritization, or parallel conflict resolution strategies can be implemented to streamline the conflict resolution process and reduce the computational burden. Another limitation lies in the handling of conflicting constraints, especially in scenarios with intricate interdependencies between multiple robots. Enhancements such as adaptive constraint relaxation, dynamic constraint weighting based on robot priorities or criticality, and intelligent constraint merging strategies can be integrated into the CB-MPC algorithm to improve the efficiency and effectiveness of conflict resolution. Additionally, incorporating learning-based approaches to predict potential conflicts or preemptively resolve conflicts before they escalate could further enhance the algorithm's performance in complex multi-robot scenarios.

Given the scalability and performance advantages of CB-MPC, how could it be integrated with high-level task planning to enable more complex multi-robot missions in real-world applications

Integrating Conflict-Based Model Predictive Control (CB-MPC) with high-level task planning can enable more complex multi-robot missions in real-world applications by combining the strengths of both approaches. One way to achieve this integration is to establish a hierarchical planning framework where the high-level task planner generates mission objectives and constraints, which are then translated into lower-level motion planning tasks for the CB-MPC algorithm to execute. By aligning the high-level mission goals with the low-level motion planning constraints, the system can ensure that the individual robot trajectories contribute to the overall mission success. Furthermore, incorporating a feedback loop between the high-level task planner and the CB-MPC algorithm allows for dynamic mission reconfiguration based on real-time feedback and environmental changes. This adaptive planning mechanism enables the system to respond to unforeseen events, optimize resource allocation, and adjust mission priorities on the fly. By leveraging the scalability and efficiency of CB-MPC for real-time motion planning while maintaining the strategic decision-making capabilities of the high-level task planner, the integrated system can tackle complex multi-robot missions with enhanced coordination, flexibility, and robustness.