Bibliographic record
Abstract
An autonomous or semi-autonomous powered wheelchair would bring the benefits of increased mobility and independence to a large population of cognitively impaired older adults who are not currently able to operate traditional powered wheelchairs. Algorithms for navigation of such wheelchairs are particularly challenging due to the unstructured, dynamic environments older adults navigate in their daily lives. Another set of challenges is found in the strict requirements for safety and comfort of such platforms. We aim to address the requirements of safe, smooth, and fast control with a version of the gradient sampling optimization algorithm of [Burke, Lewis & Overton, 2005]. We suggest that the uncertainty arising from such complex environments be tracked using a particle filter, and we propose the Gradient Sampling with Particle Filter (GSPF) algorithm, which uses the particles as the locations in which to sample the gradient. At each step, the GSPF efficiently finds a consensus direction suitable for all particles or identifies the type of stationary point on which it is stuck. If the stationary point is a minimum, the system has reached its goal (to within the limits of the state uncertainty) and the algorithm naturally terminates; otherwise, we propose two approaches to find a suitable descent direction. We illustrate the effectiveness of the GSPF on several examples with a holonomic robot, using the Robot Operating System (ROS) and Gazebo robot simulation environment, and also briefly demonstrate its extension to use a version of the RRT* planner instead of a value function.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".