On the Optimality of Ergodic Trajectories for Information Gathering Tasks

Dressel, Louis; Kochenderfer, Mykel J.

doi:10.23919/acc.2018.8430857

Cited by 7 publications

(3 citation statements)

References 15 publications

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“…A trajectory is ergodic (i.e., optimizes the ergodic metric) if the time spent along the trajectory in each region is proportional to the measure of information distributed in that region. As a result, optimized ergodic trajectories are significantly more robust to external sensor disturbances [22] and have been shown to be an optimal strategy for information-gathering tasks [41,42] with real-world application [3]. However, prior work typically has planning horizons that are fixed and are not considered part of the optimization.…”

Section: A Coverage-based and Ergodic Search Methodsmentioning

confidence: 99%

Time Optimal Ergodic Search

Dong,

Berger,

Abraham

2023

Robotics: Science and Systems XIX

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Robots with the ability to balance time against the thoroughness of search have the potential to provide time-critical assistance in applications such as search and rescue. Current advances in ergodic coverage-based search methods have enabled robots to completely explore and search an area in a fixed amount of time. However, optimizing time against the quality of autonomous ergodic search has yet to be demonstrated. In this paper, we investigate solutions to the time-optimal ergodic search problem for fast and adaptive robotic search and exploration. We pose the problem as a minimum time problem with an ergodic inequality constraint whose upper bound regulates and balances the granularity of search against time. Solutions to the problem are presented analytically using Pontryagin's conditions of optimality and demonstrated numerically through a direct transcription optimization approach. We show the efficacy of the approach in generating time-optimal ergodic search trajectories in simulation and with drone experiments in a cluttered environment. Obstacle avoidance is shown to be readily integrated into our formulation, and we perform ablation studies that investigate parameter dependence on optimized time and trajectory sensitivity for search.

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Section: A Coverage-based and Ergodic Search Methodsmentioning

confidence: 99%

Time Optimal Ergodic Search

Dong,

Berger,

Abraham

2023

Robotics: Science and Systems XIX

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“…This is not possible for the ergodic trajectory, as they are per definition deterministic and thus when the chosen convergence criteria are met, the trajectory is finished. However, the ergodic trajectory can be considered optimal for covering a distribution [18] and the deterministic nature increases the predictability and repeatability of the algorithm.…”

Section: A Comparison Between Ergodic Control and Tshixmentioning

confidence: 99%

Imitating Human Search Strategies for Assembly

Ehlers

Suomalainen

Lundell

et al. 2019

2019 International Conference on Robotics and Automation (ICRA)

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We present a Learning from Demonstration method for teaching robots to perform search strategies imitated from humans in scenarios where alignment tasks fail due to position uncertainty. The method utilizes human demonstrations to learn both a state invariant dynamics model and an exploration distribution that captures the search area covered by the demonstrator. We present two alternative algorithms for computing a search trajectory from the exploration distribution, one based on sampling and another based on deterministic ergodic control. We augment the search trajectory with forces learnt through the dynamics model to enable searching both in force and position domains. An impedance controller with superposed forces is used for reproducing the learnt strategy. We experimentally evaluate the method on a KUKA LWR4+ performing a 2D peg-in-hole and a 3D electricity socket task. Results show that the proposed method can, with only few human demonstrations, learn to complete the search task.

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“…Ergodic trajectory generation is rooted in the intuition that a path should spend time in a region proportional to the amount of expected information in that region [14], [15]. Ergodic trajectory design was originally presented in Mathew and Mezić [16] where they introduced a norm on the statistical distance between a trajectory and a reference distribution allowing problem to be framed as an optimization problem with the goal of achieving the lowest ergodic score.…”

Section: Introductionmentioning

confidence: 99%

Fast and Scalable Signal Inference for Active Robotic Source Seeking

Denniston¹,

Peltzer²,

Ott³

et al. 2023

Preprint

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In active source seeking, a robot takes repeated measurements in order to locate a signal source in a cluttered and unknown environment. A key component of an active source seeking robot planner is a model that can produce estimates of the signal at unknown locations with uncertainty quantification. This model allows the robot to plan for future measurements in the environment. Traditionally, this model has been in the form of a Gaussian process, which has difficulty scaling and cannot represent obstacles. We propose a global and local factor graph model for active source seeking, which allows the model to scale to a large number of measurements and represent unknown obstacles in the environment. We combine this model with extensions to a highly scalable planner to form a system for large-scale active source seeking. We demonstrate that our approach outperforms baseline methods in both simulated and real robot experiments.

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On the Optimality of Ergodic Trajectories for Information Gathering Tasks

Cited by 7 publications

References 15 publications

Time Optimal Ergodic Search

Time Optimal Ergodic Search

Imitating Human Search Strategies for Assembly

Fast and Scalable Signal Inference for Active Robotic Source Seeking

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