# Perception-Aware Motion Planning via Multiobjective Search on GPUs

## TL;DR

This paper introduces MPAP, a GPU-accelerated multiobjective search algorithm for perception-aware motion planning that balances path cost and localization quality, demonstrated on a quadrotor with improved safety.

## Contribution

The paper presents a novel GPU-based multiobjective search framework for perception-aware motion planning, incorporating new heuristics for perception performance prediction.

## Key findings

- MPAP finds well-localized, robust plans in about a second.
- Perception-aware plans significantly improve safety and localization.
- The framework accommodates complex perception heuristics.

## Abstract

In this paper we describe a framework towards computing well-localized, robust motion plans through the perception-aware motion planning problem, whereby we seek a low-cost motion plan subject to a separate constraint on perception localization quality. To solve this problem we introduce the Multiobjective Perception-Aware Planning (MPAP) algorithm which explores the state space via a multiobjective search, considering both cost and a perception heuristic. This framework can accommodate a large range of heuristics, allowing those that capture the history dependence of localization drift and represent complex modern perception methods. We present two such heuristics, one derived from a simplified model of robot perception and a second learned from ground-truth sensor error, which we show to be capable of predicting the performance of a state-of-the-art perception system. The solution trajectory from this heuristic-based search is then certified via Monte Carlo methods to be well-localized and robust. The additional computational burden of perception-aware planning is offset by GPU massive parallelization. Through numerical experiments the algorithm is shown to find well-localized, robust solutions in about a second. Finally, we demonstrate MPAP on a quadrotor flying perception-aware and perception-agnostic plans using Google Tango for localization, finding the quadrotor safely executes the perception-aware plan every time, while crashing in over 20% of the perception-agnostic runs due to loss of localization.