High-speed autonomous navigation of unknown environments using learned probabilities of collision

Richter, Charles; Ware, John; Roy, Nicholas

doi:10.1109/icra.2014.6907760

Cited by 24 publications

(17 citation statements)

References 23 publications

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“…The constant tracking of the distance to the nearest vision-occluding objects is the main requirement to ensure that robot can fully stop before the sharp turn and avoid collision with the unseen dynamic object that may cross the movement trajectory. By keeping further away from the sharp corners, the robot can choose a safer route, leaving enough time for collision avoidance manoeuvres and emergency brake function [31,32].…”

Section: Criteria Set For Sustainable Environment Explorationmentioning

confidence: 99%

Application of Sustainability Principles for Harsh Environment Exploration by Autonomous Robot

2019

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Currently, the European Union (EU) is focusing on a large-scale campaign dedicated to developing a competitive circular economy and expanding the single digital market. One of the main goals of this campaign is the implementation of the sustainability principles in the development and deployment cycle of the new generation technologies. This paper focuses on the fast-growing field of autonomous mobile robots and the harsh environment exploration problem. Currently, most state-of-the-art navigation methods are utilising the idea of evaluating candidate observation locations by combining different task-related criteria. However, these map building solutions are often designed for operating in near-perfect environments, neglecting such factors as the danger to the robot. In this paper, a new strategy that aims to address the safety and re-usability of the autonomous mobile agent by implementing the economic sustainability principles is proposed. A novel multi-criteria decision-making method of Weighted Aggregated Sum Product Assessment—Single-Valued Neutrosophic Sets, namely WASPAS-SVNS, and the weight selection method of Step-Wise Weights Assessment Ratio Analysis (SWARA) are applied to model a dynamic decision-making system. The experimental evaluation of the proposed strategy shows that increased survivability of the autonomous agent can be observed. Compared to the greedy baseline strategy, the proposed method forms the movement path which orients the autonomous agent away from dangerous obstacles.

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Section: Criteria Set For Sustainable Environment Explorationmentioning

confidence: 99%

Application of Sustainability Principles for Harsh Environment Exploration by Autonomous Robot

2019

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“…Fig. 5a, is pre-computed based on the Dubins car model [13] (line 1). The main loop is repeated until the vehicle reaches the goal (line 2).…”

Section: Motion Planning In Dynamic Environmentsmentioning

confidence: 99%

“…Yet, a good costto-go function can be valuable for purposes beyond just finding a feasible path. For instance, a cost-to-go function can help guide a safe local exploration strategy [13] in an unknown environment. More generally, motion planning in the vicinity (i.e.…”

Section: Introductionmentioning

confidence: 99%

Motion planning with diffusion maps

Chen

Liu

et al. 2016

2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Many robotic applications require repeated, ondemand motion planning in mapped environments. In addition, the presence of other dynamic agents, such as people, often induces frequent, dynamic changes in the environment. Having a potential function that encodes pairwise cost-to-go can be useful for improving the computational speed of finding feasible paths, and for guiding local searches around dynamic obstacles. However, since storing pairwise potential can be impractical given the O(|V | 2) memory requirement, existing work often needs to compute a potential function for each query to a new goal, which would require a substantial online computation. This work addresses the problem by using diffusion maps, a machine learning algorithm, to learn the map's geometry and develop a memory-efficient parametrization (O(|V |)) of pairwise potentials. Specially, each state in the map is transformed to a diffusion coordinate, in which pairwise Euclidean distance is shown to be a meaningful similarity metric. We develop diffusion-based motion planning algorithms and, through extensive numerical evaluation, show that the proposed algorithms find feasible paths of similar quality with orders of magnitude improvement in computational speed compared with singlequery methods. The proposed algorithms are implemented on hardware to enable real-time autonomous navigation in an indoor environment with frequent interactions with pedestrians.

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“…To date, most motion planning and decision-making approaches for autonomous driving assume good estimates from a dynamic object detection and tracking system [1][2][3][4][5][6]. A notable exception is the approach of Richter et al [10], which considers occluded space by commanding a vehicle based on learned probabilities of collision, although their approach does not reason about the current state of dynamic objects based on prior observations. However, most trackers from prior work do not explicitly handle prolonged sensor occlusions such as that shown in Fig.…”

Section: Related Workmentioning

confidence: 99%

Augmented vehicle tracking under occlusions for decision-making in autonomous driving

Galceran

Olson

Eustice

2015

2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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This paper reports on an algorithm to support autonomous vehicles in reasoning about occluded regions of their environment to make safe, reliable decisions. In autonomous driving scenarios, other traffic participants are often occluded from sensor measurements by buildings or large vehicles like buses or trucks, which makes tracking dynamic objects challenging. We present a method to augment standard dynamic object trackers with means to 1) estimate the occluded state of other traffic agents and 2) robustly associate the occluded estimates with new observations after the tracked object reenters the visible region of the sensor horizon. We perform occluded state estimation using a dynamics model that accounts for the driving behavior of traffic agents and a hybrid Gaussian mixture model (hGMM) to capture multiple hypotheses over discrete behavior, such as driving along different lanes or turning left or right at an intersection. Upon new observations, we associate them to existing estimates in terms of the Kullback-Leibler divergence (KLD). We evaluate the proposed method in simulation and using a real-world traffic-tracking dataset from an autonomous vehicle platform. Results show that our method can handle significantly prolonged occlusions when compared to a standard dynamic object tracking system.

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High-speed autonomous navigation of unknown environments using learned probabilities of collision

Cited by 24 publications

References 23 publications

Application of Sustainability Principles for Harsh Environment Exploration by Autonomous Robot

Application of Sustainability Principles for Harsh Environment Exploration by Autonomous Robot

Motion planning with diffusion maps

Augmented vehicle tracking under occlusions for decision-making in autonomous driving

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