A Novel Occupancy Mapping Framework for Risk-Aware Path Planning in Unstructured Environments

TL;DR

This paper introduces the Lambda Field, a new risk assessment map for autonomous robot navigation in unstructured environments, enabling risk-aware path planning that considers physical collision forces.

Contribution

The paper presents the Lambda Field, a novel risk map that improves continuous path risk assessment and enables physically-informed navigation in complex, unstructured environments.

Findings

Lambda Field effectively models risk for continuous paths.

Framework allows risk-aware path planning with physical collision metrics.

Enables navigation through unstructured environments like tall grass.

Abstract

In the context of autonomous robots, one of the most important tasks is to prevent potential damage to the robot during navigation. For this purpose, it is often assumed that one must deal with known probabilistic obstacles, then compute the probability of collision with each obstacle. However, in complex scenarios or unstructured environments, it might be difficult to detect such obstacles. In these cases, a metric map is used, where each position stores the information of occupancy. The most common type of metric map is the Bayesian occupancy map. However, this type of map is not well suited for computing risk assessments for continuous paths due to its discrete nature. Hence, we introduce a novel type of map called the Lambda Field, which is specially designed for risk assessment. We first propose a way to compute such a map and the expectation of a generic risk over a path. Then, we demonstrate the benefits of our generic formulation with a use case defining the risk as the expected collision force over a path. Using this risk definition and the Lambda Field, we show that our framework is capable of doing classical path planning while having a physical-based metric. Furthermore, the Lambda Field gives a natural way to deal with unstructured environments, such as tall grass. Where standard environment representations would always generate trajectories going around such obstacles, our framework allows the robot to go through the grass while being aware of the risk taken.