Person Finding: An Autonomous Robot Search Method for Finding Multiple Dynamic Users in Human-Centered Environments
Bibliographic record
Abstract
Robot search for multiple dynamic users within a multi-room environment is important for social robots to find and engage in various human-robot interaction scenarios with these users. In this paper, we present a novel autonomous person search technique for a robot finding a group of dynamic users before a deadline. The uniqueness of our approach is that unlike existing robot search methods, we consider activity information to predict where, when, and for how long a user will be in a specific room. This allows for the generation of search plans without any assumption on the frequency of user movements. We represent our search problem as an extension of the orienteering problem (OP), which we define herein as the robot person search OP (PSOP). User activity information is represented as spatial-temporal user activity probability density functions (APDFs). We solve the PSOP using APDFs to generate a search plan to maximize the expected number of users found before the deadline. The solution of the PSOP is obtained in two steps. First, by solving a variant of the multiperiod knapsack problem to determine which rooms should be searched and for how long these rooms should be searched. Then, we solve the traveling salesman problem to obtain the order in which to search these rooms. Experiments were conducted to validate the performance of our robot search method in finding different numbers of multiple dynamic users for varying environment sizes and search durations. We also compared our method with two coverage planners and a Markov decision process planner. On average, our planner found more users than the other planners for a variety of scenarios. Finally, we performed experiments that introduced uncertainty into both the APDFs as well as during the search to validate the robustness of our overall approach.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 0.001 |
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".