Investigating Human-Robot Teams for Learning-Based Semi-autonomous Control in Urban Search and Rescue Environments

Hong, Alexander; Igharoro, O.; Liu, Yugang; Niroui, Farzad; Nejat, Goldie; Benhabib, B.

doi:10.1007/s10846-018-0899-0

Cited by 28 publications

(18 citation statements)

References 49 publications

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“…Overall, the experimental results (Figs. 7, 8,9,10,11,12,13,14,15,16,17) shows that our approach works well for real-time knowledge optimization for high-level control in cognitive robotics.…”

Section: Performancementioning

confidence: 85%

“…As described in the introduction, the two key factors of cognitive robotics, are knowledge representation and cognitive reasoning [3]. Although the problem of improving autonomy is non-trivial, it is relevant to a variety of robotic applications, for example, in humanoid robotics [4,5], human-robot interaction [6][7][8][9], Search and Rescue (SAR) [10] and multi-robot systems [11].…”

Section: Related Workmentioning

confidence: 99%

“…However, cognitive robotics is concerned with cognitive reasoning, using current knowledge. In [9], a semi-autonomous high-level controller is proposed for the semi-autonomous control of robot teams in urban search and rescue missions. The objective of the controller is to reduce the workload of the robot operator.…”

Section: Cognitive Reasoningmentioning

confidence: 99%

“…(1) and (2) for a continuous search space, Eqs. (8) and (9) govern the movement of particles (sets) through a discrete set-based search space.…”

Section: Overview Of Set-based Particle Swarm Optimizationmentioning

confidence: 99%

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A particle swarm optimization approach using adaptive entropy-based fitness quantification of expert knowledge for high-level, real-time cognitive robotic control

2019

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High-level, real-time mission control of semi-autonomous robots, deployed in remote and dynamic environments, remains a challenge. Control models, learnt from a knowledgebase, quickly become obsolete when the environment or the knowledgebase changes. This research study introduces a cognitive reasoning process, to select the optimal action, using the most relevant knowledge from the knowledgebase, subject to observed evidence. The approach in this study introduces an adaptive entropy-based set-based particle swarm algorithm (AE-SPSO) and a novel, adaptive entropybased fitness quantification (AEFQ) algorithm for evidence-based optimization of the knowledge. The performance of the AE-SPSO and AEFQ algorithms are experimentally evaluated with two unmanned aerial vehicle (UAV) benchmark missions: (1) relocating the UAV to a charging station and (2) collecting and delivering a package. Performance is measured by inspecting the success and completeness of the mission and the accuracy of autonomous flight control. The results show that the AE-SPSO/AEFQ approach successfully finds the optimal state-transition for each mission task and that autonomous flight control is successfully achieved.

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Section: Performancementioning

confidence: 85%

Section: Related Workmentioning

confidence: 99%

Section: Cognitive Reasoningmentioning

confidence: 99%

“…(1) and (2) for a continuous search space, Eqs. (8) and (9) govern the movement of particles (sets) through a discrete set-based search space.…”

Section: Overview Of Set-based Particle Swarm Optimizationmentioning

confidence: 99%

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A particle swarm optimization approach using adaptive entropy-based fitness quantification of expert knowledge for high-level, real-time cognitive robotic control

2019

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“…The search and planning strategy is also generalizable to any other performance measure. Examples of such problems include: urban search and rescue [50][51][52][53][54], target pursuit [55,56], wildlife search [57], and surveillance [58].…”

Section: Introductionmentioning

confidence: 99%

A Sensor-Network-Supported Mobile-Agent-Search Strategy for Wilderness Rescue

et al. 2019

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Mobile target search is a problem pertinent to a variety of applications, including wilderness search and rescue. This paper proposes a hybrid approach for target search utilizing a team of mobile agents supported by a network of static sensors. The approach is novel in that the mobile agents deploy the sensors at optimized times and locations while they themselves travel along their respective optimized search trajectories. In the proposed approach, mobile-agent trajectories are first planned to maximize the likelihood of target detection. The deployment of the static-sensor network is subsequently planned. Namely, deployment locations and times are optimized while being constrained by the already planned mobile-agent trajectories. The latter optimization problem, as formulated and solved herein, aims to minimize an overall network-deployment error. This overall error comprises three main components, each quantifying a deviation from one of three main objectives the network aims to achieve: (i) maintaining directional unbiasedness in target-motion consideration, (ii) maintaining unbiasedness in temporal search-effort distribution, and, (iii) maximizing the likelihood of target detection. We solve this unique optimization problem using an iterative heuristic-based algorithm with random starts. The proposed hybrid search strategy was validated through the extensive simulations presented in this paper. Furthermore, its performance was evaluated with respect to an alternative hybrid search strategy, where it either outperformed or performed comparably depending on the search resources available.

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