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In the context of autonomous robots, one of the most important tasks is toprevent potential damage to the robot during navigation. For this purpose, itis often assumed that one must deal with known probabilistic obstacles, thencompute the probability of collision with each obstacle. However, in complexscenarios or unstructured environments, it might be difficult to detect suchobstacles. In these cases, a metric map is used, where each position stores theinformation of occupancy. The most common type of metric map is the Bayesianoccupancy map. However, this type of map is not well suited for computing riskassessments for continuous paths due to its discrete nature. Hence, weintroduce a novel type of map called the Lambda Field, which is speciallydesigned for risk assessment. We first propose a way to compute such a map andthe expectation of a generic risk over a path. Then, we demonstrate thebenefits of our generic formulation with a use case defining the risk as theexpected collision force over a path. Using this risk definition and the LambdaField, we show that our framework is capable of doing classical path planningwhile having a physical-based metric. Furthermore, the Lambda Field gives anatural way to deal with unstructured environments, such as tall grass. Wherestandard environment representations would always generate trajectories goingaround such obstacles, our framework allows the robot to go through the grasswhile being aware of the risk taken.

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