High-Traversability and Precise Navigation for Mobile Robots in Constrained Environments
Bibliographic record
Abstract
This paper presents an integrated sensing-planning navigation system for mobile robots used in industrial inspection. The system can be directly integrated with real robots. It enables the robot to pass through tight spaces, circumvent unexpected obstacles, and accurately reach the goal pose. This improves the robot’s adaptability to inspection tasks. For obstacle sensing, the system includes an obstacle parameterizer. Static obstacles in global grid map occupancy clusters are convexified. To improve the representation accuracy of the tight space, continuous and angled obstacles are automatically segmented into multiple convex polygons. This avoids inappropriate convex shapes. Unexpected obstacles in local grid maps are first represented by minimum bounding circles (MBCs) to simplify data association. They are then converted into inscribed convex polygons to ensure consistency with static obstacle descriptions. For motion planning, the system utilizes a two-stage planner operating on obstacle convex sets. When the robot is distant from the goal, the nonlinear model predictive control (NMPC) is constrained by inter-convex set discrete-time control barrier functions (DCBF-Convex), termed NMPC-DCBF-Convex, enabling effective navigation in tight spaces and around unexpected obstacles. As the robot nears the goal pose, DCBF-Convex conditions prune the sampling space of dynamic window approach (DWA), termed DWA-DCBF-Convex. It ensures high-frequency control and enables safe and precise arrival at the goal in constrained environments. Experiments in constrained scenarios validate the system’s real-world effectiveness. An inspection robot weighing over 70kg completes challenging navigation tasks at a maximum velocity of 0.5m/s, achieving an empirical terminal positional error of ±0.017m.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".