# Multiple-Hypothesis Path Planning with Uncertain Object Detections

arXiv: https://arxiv.org/abs/2308.07420 · HTML: https://mlsanity.com/paper/2308.07420/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Aug 2023.

## Built on by (most cited first, 1 of 1)

- [Solomonoff-Inspired Hypothesis Ranking with LLMs for Prediction Under Uncertainty](https://mlsanity.com/paper/2512.17145/) (2025)

## Abstract (arXiv)

Path planning in obstacle-dense environments is a key challenge in robotics, and depends on inferring scene attributes and associated uncertainties. We present a multiple-hypothesis path planner designed to navigate complex environments using obstacle detections. Path hypotheses are generated by reasoning about uncertainty and range, as initial detections are typically at far ranges with high uncertainty, before subsequent detections reduce this uncertainty. Given estimated obstacles, we build a graph of pairwise connections between objects based on the probability that the robot can safely pass between the pair. The graph is updated in real time and pruned of unsafe paths, providing probabilistic safety guarantees. The planner generates path hypotheses over this graph, then trades between safety and path length to intelligently optimize the best route. We evaluate our planner on randomly generated simulated forests, and find that in the most challenging environments, it increases the navigation success rate over an A* baseline from 20% to 75%. Results indicate that the use of evolving, range-based uncertainty and multiple hypotheses are critical for navigating dense environments.