LDP: A Local Diffusion Planner for Efficient Robot Navigation and Collision Avoidance (2407.01950v1)

Abstract: The conditional diffusion model has been demonstrated as an efficient tool for learning robot policies, owing to its advancement to accurately model the conditional distribution of policies. The intricate nature of real-world scenarios, characterized by dynamic obstacles and maze-like structures, underscores the complexity of robot local navigation decision-making as a conditional distribution problem. Nevertheless, leveraging the diffusion model for robot local navigation is not trivial and encounters several under-explored challenges: (1) Data Urgency. The complex conditional distribution in local navigation needs training data to include diverse policy in diverse real-world scenarios; (2) Myopic Observation. Due to the diversity of the perception scenarios, diffusion decisions based on the local perspective of robots may prove suboptimal for completing the entire task, as they often lack foresight. In certain scenarios requiring detours, the robot may become trapped. To address these issues, our approach begins with an exploration of a diverse data generation mechanism that encompasses multiple agents exhibiting distinct preferences through target selection informed by integrated global-local insights. Then, based on this diverse training data, a diffusion agent is obtained, capable of excellent collision avoidance in diverse scenarios. Subsequently, we augment our Local Diffusion Planner, also known as LDP by incorporating global observations in a lightweight manner. This enhancement broadens the observational scope of LDP, effectively mitigating the risk of becoming ensnared in local optima and promoting more robust navigational decisions.

• The paper introduces LDP, a novel local planner that leverages conditional diffusion models to significantly enhance robot navigation and collision avoidance.
• It employs multimodal expert policy data from dense static, dynamic, and maze-like scenarios, integrating global paths as key guiding conditions.
• Empirical evaluations reveal LDP outperforms baseline methods in success rate and robustness, with real-world deployments confirming its practical efficacy.

Overview of "LDP: A Local Diffusion Planner for Efficient Robot Navigation and Collision Avoidance"

Robotic navigation in real-world environments, often fraught with dynamic obstacles and complex structures, poses significant challenges to both the design and deployment of effective collision-avoidance systems. The paper "LDP: A Local Diffusion Planner for Efficient Robot Navigation and Collision Avoidance" addresses these challenges by introducing the Local Diffusion Planner (LDP), leveraging conditional diffusion models to enhance robotics path planning and collision avoidance capabilities.

Main Contributions

The paper makes several key contributions to the field of robotic navigation:

1. LDP Framework: The introduction of LDP, a novel local motion planning algorithm for robotic collision avoidance, which uses diffusion processes to effectively tackle diverse and complex navigation scenarios.
2. Multimodal Training Data: The provision of an expert policy dataset based on 2D laser sensing, encompassing expert data across three types of scenarios (dense static, dynamic pedestrian, maze-like) and two distinct preferences (original SAC policy and SAC policy guided by global paths).
3. Integration of Global Paths: The incorporation of global paths as additional guiding conditions in the diffusion model. This integration improves the planner’s ability to comprehend expert data distribution and make more forward-thinking decisions.
4. Empirical Validation: Comprehensive experiments demonstrate the superior performance of LDP over baseline algorithms in terms of navigation performance, robustness, and generalization capabilities. Additionally, the practical value of LDP is validated through deployment on physical robotic platforms in real-world scenarios.

Methodology

LDP is primarily developed through two major efforts: generating diverse expert data and utilizing a diffusion model for local planning.

Expert Policy Data

Expert policy data is collected by training reinforcement learning agents using SAC (Soft Actor-Critic) algorithms in three different scenarios:

• Dense Static: Scenarios with numerous static obstacles.
• Dynamic: Scenarios featuring moving pedestrians.
• Maze-like: Scenarios resembling labyrinthine structures.

Each scenario includes two types of expert preferences:

1. Original SAC Policy: Trained with a reward function optimized for rapid completion of tasks without global path guidance, often resulting in locally optimal but short-sighted decisions.
2. SAC Policy Guided by Global Paths: Ensures the robot follows a globally planned path, enabling better handling of maze-like structures and avoiding local minima but potentially creating longer paths.

Local Diffusion Planner (LDP)

LDP employs the Denoising Diffusion Probabilistic Models (DDPM), exploiting their strong distribution modeling capabilities:

1. Condition Representation: LDP uses three conditional elements: costmaps, goals, and global paths. The global paths act as additional conditions to guide the diffusion process, broadening the planner’s observation scope.
2. Training: The model is trained using the collected multimodal data, optimizing a loss function that measures the discrepancy between predicted and actual noise in the denoising process.
3. Inference: During inference, the model generates action sequences by gradually denoising initially sampled noise, guided by the observation conditions.

Experimental Evaluation

The LDP's performance was evaluated in both simulation and real-world scenarios:

• Simulation: Across dense static, dynamic pedestrian, and maze-like scenarios, LDP outperformed baseline models (LSTM-GMM, IBC, and DT) in key metrics such as success rate (SUCC), collision rate (COLL), average time (TIME), and SPL. Remarkably, LDP demonstrated strong zero-shot generalization capabilities in unseen zigzag scenarios.
• Ablation Studies: Removing global path conditions from LDP notably degraded its performance, especially in complex maze-like environments.

Practical Implications and Future Developments

The integration of multimodal expert data and global path guidance enables LDP to make more informed and forward-looking decisions, achieving higher performance and robustness than existing methods. The practical deployment on Ackermann steering robots further underlines its real-world applicability.

Speculation and Future Work

The paper opens several avenues for future research in AI and robotic navigation:

1. Data Quality and Diversity: Collecting richer and more varied expert policy data can potentially yield even more robust navigation models.
2. Real-Time Performance: Exploring alternative modeling techniques, such as flow-based models or consistency models, could significantly speed up the sampling process, enhancing the real-time applicability of LDP.

In conclusion, the introduction of LDP marks a significant step forward in efficient robot navigation and collision avoidance, offering a robust framework that can be further refined and expanded in the future.