Efficient Motion Planning for Mobile Robots with Non-Trivial Dynamics using Learned Controllers and Roadmaps with Gaps

Core Concepts

This work proposes a decoupled strategy that first trains a goal-conditioned controller offline in an empty environment to deal with the robot's dynamics, and then constructs a "Roadmap with Gaps" to approximately learn how to solve planning queries in a target environment using the learned controller. The roadmap guidance is integrated with an asymptotically optimal tree sampling-based planner to achieve improved computational efficiency for motion planning.

Abstract

The paper introduces a "Roadmap with Gaps" data structure that captures the approximate reachability of local regions in a given environment given a learned controller. The roadmap is constructed offline and provides high-level guidance on how the robot can navigate the target environment.
During online planning, a wavefront is computed over the roadmap to express the cost-to-goal at each node. The proposed Roadmap-Guided Expansion (RoGuE) method integrates this roadmap guidance with an asymptotically optimal tree sampling-based planner. At each iteration, RoGuE selects an informed local goal from the roadmap wavefront and propagates the robot's state towards it using the learned controller. If the controller cannot reach the local goal, the planner resorts to random exploration to maintain probabilistic completeness and asymptotic optimality.
The experimental evaluation demonstrates the effectiveness of the proposed approach on various benchmarks, including physics-based vehicular models on uneven and varying friction terrains, as well as a quadrotor under air pressure effects. The RoGuE-based planners significantly outperform alternatives that do not leverage the roadmap guidance.

Stats

The solution trajectory cost for the RLG method is 79.01s.
The solution trajectory cost for the RoGuE method is 47.05s.

Quotes

"This work aims to improve the computational efficiency of motion planning for mobile robots with non-trivial dynamics through the use of learned controllers."
"The proposed method, however, is informed about the reachability properties of the learned controller and the overall framework retains Asymptotic Optimality (AO)."

Key Insights Distilled From

Roadmaps with Gaps over Controllers

by Aravind Siva... at arxiv.org 04-01-2024

https://arxiv.org/pdf/2310.03239.pdf

Deeper Inquiries

How can the roadmap construction process be further optimized to reduce the memory requirements for higher-dimensional systems

To reduce memory requirements for higher-dimensional systems, the roadmap construction process can be optimized by implementing sparse representations similar to geometric planning. Instead of densely populating the roadmap with configurations, a sparse representation can be used to capture the essential information needed for planning. This approach involves selecting a subset of critical configurations that effectively represent the environment's traversability and connectivity. By strategically choosing these milestones, the roadmap can maintain its effectiveness while significantly reducing memory overhead. Additionally, techniques like dimensionality reduction or clustering can be applied to further streamline the roadmap construction process for higher-dimensional systems.

How can the proposed framework be integrated with feedback control to track the planned trajectory accurately on real robotic systems

Integrating the proposed framework with feedback control to track the planned trajectory accurately on real robotic systems involves combining the strengths of both approaches. The learned controller from the framework can provide high-level guidance on how to navigate the environment efficiently. This guidance can be used as a reference trajectory for the feedback control system to follow. By incorporating feedback mechanisms that adjust the robot's actions based on real-time sensor feedback, the system can adapt to unforeseen obstacles or disturbances during execution. This integration ensures that the planned trajectory is closely followed while allowing for on-the-fly adjustments to ensure safe and accurate navigation in dynamic environments.

What are the potential limitations of the approach when the gaps in the roadmap due to the dynamics are too large, and how can related techniques like effort-biased planning help address this issue

When the gaps in the roadmap due to dynamics are too large, it can limit the effectiveness of the proposed approach. In such cases, related techniques like effort-biased planning can help address this issue. Effort-biased planning focuses on exploring alternative paths quickly by introducing biases in the motion planner's decision-making process. By prioritizing regions that are likely to lead to successful trajectories, the planner can efficiently navigate challenging areas with large gaps in the roadmap. This approach allows the system to explore diverse paths while maintaining a balance between exploration and exploitation, ultimately improving the chances of finding feasible solutions in complex environments with significant dynamics.