Probability Navigation Function for Stochastic Static Environments

Hacohen, Shlomi; Shoval, Shraga; Shvalb, Nir

doi:10.1007/s12555-018-0563-2

Cited by 16 publications

(21 citation statements)

References 35 publications

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“…To account for the location uncertainties of the i th and j th agents, their location distributions must be combined by a convolution (see [37] for further discussion). For a normal distribution, the convolution product distribution covariance is the sum of the distributions' covariance.…”

Section: A the State Modelmentioning

confidence: 99%

Robotic Swarm Motion Planning for Load Carrying and Manipulating

2020

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Certain species of ants can carry out tasks in dense work spaces while maintaining their ability to accurately manipulate heavy loads, and these advantages are of interest to the robotics community. We consider a robotic swarm of N ≥ 6 agents that assumes the task of moving a load through a cluttered space. This forces the swarm to carefully manipulate the orientation of the load, while transporting it to its destination point. We model this scenario as a 6-PPSS (Prismatic-Prismatic-Spherical-Spherical) redundant mobile platform, having six degrees of freedom. As with insects, the multitude of agents enables sharing the burden of the load in the case that one or more agents are blocked by an obstacle. We model this by a semi-algebraic set of constraints on the distances between the agents and the load. We apply an Extended Kalman Filter routine, in order to estimate their relative locations. We show how the estimation-error is reduced when position-information is shared among the agents. These estimations are then used to calculate the full configuration and investigate the effect of position estimation error on the platform heading error. We show how motion planning can then be calculated in the model's full configuration space and demonstrate this with a distributed control scheme. To reduce the search time, we introduce a variant of the crawling probabilistic road map motion planning algorithm under a set of kinematic constraints and work-space obstacles. Finally, we exemplify our algorithms on several simulated scenarios. INDEX TERMS Swarm, load, extended Kalman filter, parallel platform, crawling probabilistic road map.

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Section: A the State Modelmentioning

confidence: 99%

Robotic Swarm Motion Planning for Load Carrying and Manipulating

2020

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“…Considering the estimation-error covariance, at each time-step k , each agent i uses an EKF to calculate the best available prediction for this uncertainty denoted by

. Following [ 44 ], we convolve the location distribution of each of the two agents i and j ; we then take the confidence level to be the maximal eigenvalue of the prediction. Please note that the covariance matrix of a convolution of two normal distributions is simply the sum of the two covariance matrices.…”

Section: Improved Localization—the Case Of No Disruptionsmentioning

confidence: 99%

Improved GNSS Localization and Byzantine Detection in UAV Swarms

Hacohen

Medina

Grinshpoun

et al. 2020

Sensors

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Many tasks performed by swarms of unmanned aerial vehicles require localization. In many cases, the sensors that take part in the localization process suffer from inherent measurement errors. This problem is amplified when disruptions are added, either endogenously through Byzantine failures of agents within the swarm, or exogenously by some external source, such as a GNSS jammer. In this paper, we first introduce an improved localization method based on distance observation. Then, we devise schemes for detecting Byzantine agents, in scenarios of endogenous disruptions, and for detecting a disrupted area, in case the source of the problem is exogenous. Finally, we apply pool testing techniques to reduce the communication traffic and the computation time of our schemes. The optimal pool size should be chosen carefully, as very small or very large pools may impair the ability to identify the source/s of disruption. A set of simulated experiments demonstrates the effectiveness of our proposed methods, which enable reliable error estimation even amid disruptions. This work is the first, to the best of our knowledge, that embeds identification of endogenous and exogenous disruptions into the localization process.

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“…In this paper we provide an extension of the well-established method for swarm motion planning using the NF. More precisely, we propose an efficient motion planner for all swarm members, by extending both the classical NF and the Probabilistic Navigation Function (PNF) 15 . Our solution does not require decomposition of the problem into sub-missions, and it guarantees asymptotic convergence even for a very large number of agents and targets, as the complexity of the solution is linear to the number of agents plus the number of un-intercepted targets.…”

Section: Introductionmentioning

confidence: 99%

Navigation function for Multi-Agent Multi-Target Interception Missions

Hacohen

Shoval

Shvalb

2022

Preprint

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Missions across a variety of disciplines require the interception of multiple targets. In defence scenarios, targets may pose a threat to sites, while in agriculture the targets may be invasive pests or fruit ready to harvest. This paper focuses on the cooperative control of a robot swarm for interception missions of multiple static and dynamic targets while avoiding collisions. We formulate two modifications of the classical Navigation-Function which are suited for deterministic and stochastic scenarios. Both provide a simultaneous solution for the problems of target assignment and motion-planning as opposed to the classical approaches that solve each problem independently. We show analytically that by following the Swarm-Navigation-Function gradient, the swarm will intercept all static targets while avoiding agent-agent and agent-obstacle collisions and similarly following the gradient of the Probabilistic-Navigation-Function will almost surely converge to a target in finite time, while the probability for agent-agent and agent-obstacles collisions is limited to a predefined value. The complexity of both schemes is linear with the number of targets and robots, and therefore it is scalable. Although not optimal, these solutions are simple and efficient, making them suitable for an extended set of real-time and real-life applications. We compare the resulting Swarm-Navigation-Function trajectories to that of a human in a catch game and an interception virtual game, the comparison indicates that as the trajectories are similar, human decision-making performs better. We conclude the paper with a set of simulated experiments and real-world experiments demonstrating the efficiency of the proposed scheme for dynamic targets.

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Probability Navigation Function for Stochastic Static Environments

Cited by 16 publications

References 35 publications

Robotic Swarm Motion Planning for Load Carrying and Manipulating

Robotic Swarm Motion Planning for Load Carrying and Manipulating

Improved GNSS Localization and Byzantine Detection in UAV Swarms

Navigation function for Multi-Agent Multi-Target Interception Missions

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