Action-Aware Pro-Active Safe Exploration for Mobile Robot Mapping (2503.09515v1)

Abstract: Safe autonomous exploration of unknown environments is an essential skill for mobile robots to effectively and adaptively perform environmental mapping for diverse critical tasks. Due to its simplicity, most existing exploration methods rely on the standard frontier-based exploration strategy, which directs a robot to the boundary between the known safe and the unknown unexplored spaces to acquire new information about the environment. This typically follows a recurrent persistent planning strategy, first selecting an informative frontier viewpoint, then moving the robot toward the selected viewpoint until reaching it, and repeating these steps until termination. However, exploration with persistent planning may lack adaptivity to continuously updated maps, whereas highly adaptive exploration with online planning often suffers from high computational costs and potential issues with livelocks. In this paper, as an alternative to less-adaptive persistent planning and costly online planning, we introduce a new proactive preventive replanning strategy for effective exploration using the immediately available actionable information at a viewpoint to avoid redundant, uninformative last-mile exploration motion. We also use the actionable information of a viewpoint as a systematic termination criterion for exploration. To close the gap between perception and action, we perform safe and informative path planning that minimizes the risk of collision with detected obstacles and the distance to unexplored regions, and we apply action-aware viewpoint selection with maximal information utility per total navigation cost. We demonstrate the effectiveness of our action-aware proactive exploration method in numerical simulations and hardware experiments.

Essay on "Action-Aware Pro-Active Safe Exploration for Mobile Robot Mapping"

The paper "Action-Aware Pro-Active Safe Exploration for Mobile Robot Mapping" addresses the challenge of efficient and safe exploration in unknown environments essential for mobile robot mapping tasks. Mapping in uncertain domains such as search and rescue, surveillance, and environmental detection requires robust methods, not just for navigation but also for determining exploration strategies that minimize computational demands and potential motion risks.

Central to the work is the proposition of a novel approach: proactive preventive replanning. This is framed as an alternative to the conventional persistent planning strategies and complex online planning methods historically used in frontier-based exploration of environments. Frontier-based exploration, which leverages the boundary between known safe spaces and unexplored regions, is an efficient heuristic, routing robots to novel information zones while generally lacking adaptability due to its reliance on fixed planning policies. The authors improve upon this by proposing an algorithm that dynamically selects frontier viewpoints, accounting for both immediate actionable information and navigational costs.

Key contributions of the paper include:

1. Safe and Informative Path Planning: The authors formulated a method that constructs paths which minimize distance to unexplored regions while ensuring avoidance of obstacles. This optimization of path planning is key to efficient exploration.
2. Action-Aware Viewpoint Selection: Introduced a strategy for selecting viewpoints based on maximal information utility relative to total navigation cost, countering simplistic perception-driven approaches that fail to account for the intricacies of system dynamics and navigational safety.
3. Preventive Replanning Strategy: They introduced a criterion of 'immediately available actionable information' to determine the termination of exploration, allowing the system to adaptively replan based on real-time data available from the environment.

The proactive exploration method was demonstrated through simulations and hardware experiments, showing enhanced efficiency by improving the rate and extent of environmental exploration—a key metric for robotic systems intended for dynamic and real-world environments. The results indicate that action-aware exploration with preventive strategies significantly mitigates redundant motion, with reduced computational overhead compared to purely online planning techniques.

In terms of numerical results, action-aware strategies using geodesic distances rather than simplistic Euclidean measures improved map completion rates relative to distance traveled. The paper argues convincingly that navigation cost accuracy (with geodesic distances modeled from occupancy maps) is more pertinent than information utility measures in ensuring efficient completions due to the necessity of visiting all informative regions.

Future work as highlighted in the paper involves exploring multi-viewpoint planning using approximate traveling salesman problem methods, addressing the simultaneous objectives of mapping, localization, and efficient exploration especially in dynamic environments with variable constraints.

In conclusion, the authors have presented a comprehensive framework for exploration that challenges existing paradigms and presents evidence that careful integration of perception and action layers can significantly enhance robotic exploration efficiency. This work stands as a substantial contribution to the domain of robotic exploration and mapping in the technical complexity it addresses and the practical implications of its findings. The research opens new avenues for optimal exploration strategies and frontier-based mapping in robotics, both theoretical and applied.