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Beyond Shortsighted Navigation: Merging Best View Trajectory Planning with Robot Navigation

Srinath Tankasala, Roberto Martín-Martín, Mitch Pryor

TL;DR

The paper tackles long-horizon viewpoint planning for ground robots equipped with arm-mounted cameras to maximize scene coverage along a known base patrol path. It formulates the problem as a submodular maximization: $\max_{VP\subset Z} IG(VP)$, subject to $|VP|\le N$, and solves it with a horizon-aware greedy search that accounts for arm reachability via a time-parameterized velocity profile (TVP). A voxel-based information gain model with ray casting drives view selection, and a view-path graph is traversed to maximize marginal IG over the horizon. Experiments in simulation and on a real Spot robot show LHVP outperforms a nearest-object baseline, enabling robust long-term data collection, with ablations clarifying parameter sensitivity and suggesting directions like recursive greedy search and gait optimization for smoother data capture.

Abstract

Gathering visual information effectively to monitor known environments is a key challenge in robotics. To be as efficient as human surveyors, robotic systems must continuously collect observational data required to complete their survey task. Inspection personnel instinctively know to look at relevant equipment that happens to be ``along the way.'' In this paper, we introduce a novel framework for continuous long-horizon viewpoint planning, for ground robots, applied to tasks involving patrolling, monitoring or visual data gathering in known environments. Our approach to Long Horizon Viewpoint Planning (LHVP), enables the robot to autonomously navigate and collect environmental data optimizing for coverage over the horizon of the patrol. Leveraging a quadruped's mobility and sensory capabilities, our LHVP framework plans patrol paths that account for coupling the viewpoint planner for the arm camera with the mobile base's navigation planner. The viewpath optimization algorithm seeks a balance between comprehensive environmental coverage and dynamically feasible movements, thus ensuring prolonged and effective operation in scenarios including monitoring, security surveillance, and disaster response. We validate our approach through simulations and in the real world and show that our LHVP significantly outperforms naive patrolling methods in terms of area coverage generating information-gathering trajectories for the robot arm. Our results indicate a promising direction for the deployment of mobile robots in long-term, autonomous surveying, and environmental data collection tasks, highlighting the potential of intelligent robotic systems in challenging real-world applications.

Beyond Shortsighted Navigation: Merging Best View Trajectory Planning with Robot Navigation

TL;DR

The paper tackles long-horizon viewpoint planning for ground robots equipped with arm-mounted cameras to maximize scene coverage along a known base patrol path. It formulates the problem as a submodular maximization: , subject to , and solves it with a horizon-aware greedy search that accounts for arm reachability via a time-parameterized velocity profile (TVP). A voxel-based information gain model with ray casting drives view selection, and a view-path graph is traversed to maximize marginal IG over the horizon. Experiments in simulation and on a real Spot robot show LHVP outperforms a nearest-object baseline, enabling robust long-term data collection, with ablations clarifying parameter sensitivity and suggesting directions like recursive greedy search and gait optimization for smoother data capture.

Abstract

Gathering visual information effectively to monitor known environments is a key challenge in robotics. To be as efficient as human surveyors, robotic systems must continuously collect observational data required to complete their survey task. Inspection personnel instinctively know to look at relevant equipment that happens to be ``along the way.'' In this paper, we introduce a novel framework for continuous long-horizon viewpoint planning, for ground robots, applied to tasks involving patrolling, monitoring or visual data gathering in known environments. Our approach to Long Horizon Viewpoint Planning (LHVP), enables the robot to autonomously navigate and collect environmental data optimizing for coverage over the horizon of the patrol. Leveraging a quadruped's mobility and sensory capabilities, our LHVP framework plans patrol paths that account for coupling the viewpoint planner for the arm camera with the mobile base's navigation planner. The viewpath optimization algorithm seeks a balance between comprehensive environmental coverage and dynamically feasible movements, thus ensuring prolonged and effective operation in scenarios including monitoring, security surveillance, and disaster response. We validate our approach through simulations and in the real world and show that our LHVP significantly outperforms naive patrolling methods in terms of area coverage generating information-gathering trajectories for the robot arm. Our results indicate a promising direction for the deployment of mobile robots in long-term, autonomous surveying, and environmental data collection tasks, highlighting the potential of intelligent robotic systems in challenging real-world applications.
Paper Structure (9 sections, 1 theorem, 6 equations, 8 figures, 6 tables, 2 algorithms)

This paper contains 9 sections, 1 theorem, 6 equations, 8 figures, 6 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Camera motion generation for maximum scene coverage
  3. View candidates generation
  4. Information gain and view path computation
  5. Experiments
  6. Simulation
  7. Ablation studies
  8. Real world reconstruction
  9. Conclusion

Key Result

Theorem 1

For a non-negative monotone submodular function $f: 2^V \rightarrow \mathbb{R}_+$, let $\{VP^i\}_{i\geq 0}$ be the greedily selected sets defined in Eq. eq:greedy_max. Then for all positive integers $k$ and $\ell$, In particular, for $\ell = k$,

Figures (8)

  • Figure 1: Real-world execution of our long horizon view trajectory planner. The arm mounted camera moves in conditioned on the base navigation plan to effectively survey the environment without stopping.
  • Figure 2: Reachability map of robot arm end effector in a single time step. The entire reachable surface in one time step is depicted on the left and cross-section of it is shown on the right. Pose dimensions reduced from 6D to 3D for visualization purposes.
  • Figure 3: Greedy IG search along robot base path (dotted blue line) uniformly discretized as $P_1, P_2,..., P_N$. Reachable camera pose candidates,$\tau_{i+1}^1, \tau_{i+1}^2,..., \tau_{i+1}^M$, are sequentially sampled from the current end effector pose, $\tau_i^j$. Greedy search traverses viewpoint graph along nodes of maximum marginal IG (solid red arrow path)
  • Figure 4: Different environment configurations, triangle layout (top) and linear layout(bottom), and base paths (Straight, Loop, Zig-zag) used to evaluate our long horizon view trajectory planner
  • Figure 5: Sample assets used as survey targets of interest (to scale)
  • ...and 3 more figures

Theorems & Definitions (1)

  • Theorem 1: nemhauser1978analysis