Wilderness Search for Lost Persons Using a Multimodal Aerial-Terrestrial Robot Team
Bibliographic record
Abstract
Mobile robots that are capable of multiple modes of locomotion may have tangible advantages over unimodal robots in unstructured and non-homogeneous environments due to their ability to better adapt to local conditions. This paper specifically considers the use of a team of multimodal robots capable of switching between aerial and terrestrial modes of locomotion for wilderness search and rescue (WiSAR) scenarios. It presents a novel search planning method that coordinates the members of the robotic team to maximize the probability of locating a mobile target in the wilderness, potentially, last seen on an a priori known trail. It is assumed that the search area expands over time and, thus, an exhaustive search is not feasible. Earlier research on search planning methods for heterogeneous though unimodal search teams have exploited synergies between robots with different locomotive abilities through coordination and/or cooperation. Work on multimodal robots, on the other hand, has primarily focused on their mechanical design and low-level control. In contrast, our recent work, presented herein, has two major components: (i) target-motion prediction in the presence of a priori known trails in the wilderness, and (ii) probability-guided multimodal robot search-trajectory generation. For the former sub-problem, the novelty of our work lies in the formulation and use of 3D probability curves to capture target distributions under the influence of a priori known walking/hiking trails. For the latter, the novelty lies in the use of a tree structure to represent the decisions involved in multimodal probability-curve-guided search planning, which enables trajectory generation and mode selection to be optimized simultaneously, for example, via a Monte Carlo tree search technique. Extensive simulations, some of which are included herein, have shown that multimodal robotic search teams, coordinated via the trajectory planning method proposed in this paper, clearly outperform their unimodal counterparts in terms of search success rates.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".