Informative Path Planning for Active Field Mapping under Localization Uncertainty

Abstract

Information gathering algorithms play a key role in unlocking the potential of robots for efficient data collection in a wide range of applications. However, most existing strategies neglect the fundamental problem of the robot pose uncertainty, which is an implicit requirement for creating robust, high-quality maps. To address this issue, we introduce an informative planning framework for active mapping that explicitly accounts for the pose uncertainty in both the mapping and planning tasks. Our strategy exploits a Gaussian Process (GP) model to capture a target environmental field given the uncertainty on its inputs. For planning, we formulate a new utility function that couples the localization and field mapping objectives in GP-based mapping scenarios in a principled way, without relying on any manually tuned parameters. Extensive simulations show that our approach outperforms existing strategies, with reductions in mean pose uncertainty and map error. We also present a proof of concept in an indoor temperature mapping scenario.

Adaptive Informative Path Planning Using Deep Reinforcement Learning for UAV-based Active Sensing

“…hotspots or abnormal areas [9]. Some methods focus on discrete action spaces defined by sparse graphs of permissible actions [10,11]. However, these simplifications are not applicable as the distribution of target regions is a priori unknown.…”

“…evolutionary algorithms [1,2] or Bayesian Optimization [8], to enhance planning efficiency. Although these approaches deliver highquality paths [1,11], using them for online decision-making is still computationally expensive when reasoning about many spatially correlated candidate future measurements.…”

“…The permissible next actions of the MCTS were reduced to a radius of 11 m around the UAV position to be computable online, resulting in ∼ 115 next actions per move. For a fair comparison, we trained our approach offline on this restricted action space, which is still much larger than considered in previous work [10,11].…”

“…Previous studies have tackled this by discretizing the action state space, e.g. by using sparse graphs [10,11], for replanning; however, such simplifications sacrifice on the quality of predictive plans. A new alternative paradigm is to use reinforcement learning (RL) to learn data gathering actions directly.…”

Peacock Exploration: A Lightweight Exploration for UAV Using Control-Efficient Trajectory

“…Unmanned Aerial Vehicles (UAVs) have received much attention in recent years due to its wide range of applications, such as autonomous structural inspection [1,2] and exploration of an unknown environment [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18]. In particular, exploration with the UAV is one of the most popular applications to acquire 3D map of an unknown environment without prior knowledge of it.…”