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TopoNav: Topological Navigation for Efficient Exploration in Sparse Reward Environments

Jumman Hossain, Abu-Zaher Faridee, Nirmalya Roy, Jade Freeman, Timothy Gregory, Theron T. Trout

TL;DR

TopoNav is presented, a novel topological navigation framework that integrates active mapping, hierarchical reinforcement learning, and intrinsic motivation to enable efficient goal-oriented exploration and navigation in sparse-reward settings.

Abstract

Autonomous robots exploring unknown environments face a significant challenge: navigating effectively without prior maps and with limited external feedback. This challenge intensifies in sparse reward environments, where traditional exploration techniques often fail. In this paper, we present TopoNav, a novel topological navigation framework that integrates active mapping, hierarchical reinforcement learning, and intrinsic motivation to enable efficient goal-oriented exploration and navigation in sparse-reward settings. TopoNav dynamically constructs a topological map of the environment, capturing key locations and pathways. A two-level hierarchical policy architecture, comprising a high-level graph traversal policy and low-level motion control policies, enables effective navigation and obstacle avoidance while maintaining focus on the overall goal. Additionally, TopoNav incorporates intrinsic motivation to guide exploration toward relevant regions and frontier nodes in the topological map, addressing the challenges of sparse extrinsic rewards. We evaluate TopoNav both in the simulated and real-world off-road environments using a Clearpath Jackal robot, across three challenging navigation scenarios: goal-reaching, feature-based navigation, and navigation in complex terrains. We observe an increase in exploration coverage by 7- 20%, in success rates by 9-19%, and reductions in navigation times by 15-36% across various scenarios, compared to state-of-the-art methods

TopoNav: Topological Navigation for Efficient Exploration in Sparse Reward Environments

TL;DR

TopoNav is presented, a novel topological navigation framework that integrates active mapping, hierarchical reinforcement learning, and intrinsic motivation to enable efficient goal-oriented exploration and navigation in sparse-reward settings.

Abstract

Autonomous robots exploring unknown environments face a significant challenge: navigating effectively without prior maps and with limited external feedback. This challenge intensifies in sparse reward environments, where traditional exploration techniques often fail. In this paper, we present TopoNav, a novel topological navigation framework that integrates active mapping, hierarchical reinforcement learning, and intrinsic motivation to enable efficient goal-oriented exploration and navigation in sparse-reward settings. TopoNav dynamically constructs a topological map of the environment, capturing key locations and pathways. A two-level hierarchical policy architecture, comprising a high-level graph traversal policy and low-level motion control policies, enables effective navigation and obstacle avoidance while maintaining focus on the overall goal. Additionally, TopoNav incorporates intrinsic motivation to guide exploration toward relevant regions and frontier nodes in the topological map, addressing the challenges of sparse extrinsic rewards. We evaluate TopoNav both in the simulated and real-world off-road environments using a Clearpath Jackal robot, across three challenging navigation scenarios: goal-reaching, feature-based navigation, and navigation in complex terrains. We observe an increase in exploration coverage by 7- 20%, in success rates by 9-19%, and reductions in navigation times by 15-36% across various scenarios, compared to state-of-the-art methods
Paper Structure (22 sections, 14 equations, 5 figures, 2 tables, 2 algorithms)

This paper contains 22 sections, 14 equations, 5 figures, 2 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. Learning-Based Navigation
  4. Topological Mapping Navigation
  5. Background and Problem Formulation
  6. Topological Mapping
  7. Hierarchical Deep Q-Networks (H-DQN)
  8. Problem Formulation
  9. TopoNav: Topological Map Generation and Navigation
  10. Attention-based Feature Detection
  11. Subgoal Generation and Navigation
  12. Reward Structure
  13. Hierarchical Policy Learning
  14. Experiments and Results
  15. Evaluation Environments
  16. ...and 7 more sections

Figures (5)

  • Figure 1: TopoNav Navigation Strategies: The navigation begins at the Start node (green circle) and progresses through designated subgoals—Point A (initial decision point), Point B (complex navigation subgoal), and Point C (alternative challenging subgoal)—toward the Goal (red diamond). The routes illustrate TopoNav's strategy: solid lines represent direct paths to subgoals, a dashed line marks a complex detour around Obstacle1, and a dotted line indicates a potential route for challenging maneuvering near Obstacle2. This diagram shows the robot's strategic navigation from start to finish, highlighting its decision-making and adaptability in outdoor environments with diverse navigational challenges. A real-world scenario is presented in Fig. \ref{['fig:obstacle_experiment']}
  • Figure 2: Overview of TopoNav System Architecture.
  • Figure 3: An illustration of the topological navigation process using detected landmarks as sub-goals and strategic landmark selection.
  • Figure 4: (a-d) The robot navigates through various outdoor environments: (a) an open space without obstacles/vegetation (Scenario 1), (b) a natural setting with trees/vegetation (Scenario 2), (c) a cluttered environment with obstacles and landmarks (Scenario 3), and (d) a diverse terrain in simulation. (e-h) The corresponding topological maps generated by TopoNav for each scenario. The topological maps capture the connectivity and traversability of the environments, representing key locations and paths. The green dots in the maps represent the nodes or subgoals, which correspond to landmarks or distinct places. The edges (red lines) indicate the navigability between these nodes, enabling efficient path planning and navigation for the robot in each setting.
  • Figure 5: (a) A 3D point cloud representation of the outdoor environment obtained from the robot's LiDAR sensor, showcasing the terrain and obstacle details. (b) Robot navigating through a physical environment, guided by the TopoNav framework, executes an immediate obstacle avoidance maneuver to select an alternative route when faced with an obstruction (red cross, fallen trees).