Effect of random walk methods on searching efficiency in swarm robots for area exploration

Pang, Baojun; Song, Yong; Zhang, Chengjin

doi:10.1007/s10489-020-02060-0

Cited by 26 publications

(15 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…is experiment has been conducted on the confided grid map introduced in Figure 7(c), and for each exploration algorithm, the experiment has been repeated 20 times for each value of n t . As demonstrated in Figure 10, the random walk multiagent exploration algorithm [41] performed ineffectual compared to other algorithms. is is mainly because the random walk search algorithm neglects the distance to the frontiers and disregards the locations of other hunters in the environment.…”

Section: Functionality Validation Of the Hunter-and-gatherermentioning

confidence: 99%

Exploration and Coordination of Complementary Multirobot Teams in a Hunter-and-Gatherer Scenario

et al. 2021

View full text Add to dashboard Cite

The hunter-and-gatherer approach copes with the problem of dynamic multirobot task allocation, where tasks are unknowingly distributed over an environment. This approach employs two complementary teams of agents: one agile in exploring (hunters) and another dexterous in completing (gatherers) the tasks. Although this approach has been studied from the task planning point of view in our previous works, the multirobot exploration and coordination aspects of the problem remain uninvestigated. This paper proposes a multirobot exploration algorithm for hunters based on innovative notions of “expected information gain” to minimize the collective cost of task accomplishments in a distributed manner. Besides, we present a coordination solution between hunters and gatherers by integrating the novel notion of profit margins into the concept of expected information gain. Statistical analysis of extensive simulation results confirms the efficacy of the proposed algorithms compared in different environments with varying levels of obstacle complexities. We also demonstrate that the lack of effective coordination between hunters and gatherers significantly distorts the total effectiveness of the planning, especially in environments containing dense obstacles and confined corridors. Finally, it is statistically proven that the overall workload is distributed equally for each type of agent which ensures that the proposed solution is not biased to a particular agent and all agents behave analogously under similar characteristics.

show abstract

Section: Functionality Validation Of the Hunter-and-gatherermentioning

confidence: 99%

Exploration and Coordination of Complementary Multirobot Teams in a Hunter-and-Gatherer Scenario

et al. 2021

View full text Add to dashboard Cite

show abstract

“…We consider multiple autonomous robots that must complete tasks across an agricultural field, with an intention to optimise field coverage by determining a movement trajectory. The movements of these robots are governed by a random walk approach aiming to cover the field [10] in minimal time, whilst also completing the required tasks to a high accuracy. The use of a truncated random walk approach, which generates step lengths that follow a predetermined distribution and fall within a specific range, improves the efficiency of the search process [10].…”

Section: Robot Mobilitymentioning

confidence: 99%

Adaptive Edge-Cloud Environments for Rural AI

Almurshed

Patros

Huang

et al. 2022

2022 IEEE International Conference on Services Computing (SCC)

View full text Add to dashboard Cite

Cloud computing provides on-demand access to computational resources while outsourcing infrastructure and service maintenance. Edge computing could extend cloud computing capability to areas with limited computing resources, such as rural areas, by utilizing low-cost hardware, such as singleboard computers. Cloud data centre hosted machine learning algorithms may violate user privacy and data confidentiality requirements. Federated learning (FL) trains models without sending data to a central server and ensures data privacy. Using FL, multiple actors can collaborate on a single machine learning model without sharing data. However, rural network outages can happen at any time, and the quality of a wireless network varies depending on location, which can affect the performance of the Federated Learning application. Therefore there is a need to have a platform that maintains service quality independent of infrastructure status. We propose a self-adaptive system for rural FL, which employs the Greedy Nominator Heuristic (GNH) based optimisation to orchestrate application workflows across multiple resources that make up a rural computing environment. GNH provides distributed optimization for workflow placement. GNH utilises resource status to reduce failure risks and costs while still completing tasks on time. Our approach is validated using a simulated rural environment -composed of multiple decentralized controllers sharing the same infrastructure and running a shared FL application. Results show that GNH outperforms three algorithms for deployment of FL tasks: random placement, round-robin load balancer and simple greedy algorithm.

show abstract

“…which allows us to formulate the measurement model in a vectorized form y(X) = Φ(X, Π)w + η(X), (7) with η(X) [η(x 1 ), . .…”

Section: Model Definitionmentioning

confidence: 99%

“…Let us consider a modification of the model ( 7) as a function of the location x. The incorporation of x into (7) would imply that the design matrix Φ would be extended as…”

Section: D-optimalitymentioning

confidence: 99%

Experimental Validation of Entropy-Driven Swarm Exploration under Sparsity Constraints with Sparse Bayesian Learning

Manss

Kuehner

Shutin

2022

Entropy

View full text Add to dashboard Cite

Increasing the autonomy of multi-agent systems or swarms for exploration missions requires tools for efficient information gathering. This work studies this problem from theoretical and experimental perspectives and evaluates an exploration system for multiple ground robots that cooperatively explore a stationary spatial process. For the distributed model, two conceptually different distribution paradigms are considered. The exploration is based on fusing distributively gathered information using Sparse Bayesian Learning (SBL), which permits representing the spatial process in a compressed manner and thus reduces the model complexity and communication load required for the exploration. An entropy-based exploration criterion is formulated to guide the agents. This criterion uses an estimation of a covariance matrix of the model parameters, which is then quantitatively characterized using a D-optimality criterion. The new sampling locations for the agents are then selected to minimize this criterion. To this end, a distributed optimization of the D-optimality criterion is derived. The proposed entropy-driven exploration is then presented from a system perspective and validated in laboratory experiments with two ground robots. The experiments show that SBL together with the distributed entropy-driven exploration is real-time capable and leads to a better performance with respect to time and accuracy compared with similar state-of-the-art algorithms.

show abstract

Effect of random walk methods on searching efficiency in swarm robots for area exploration

Cited by 26 publications

References 36 publications

Exploration and Coordination of Complementary Multirobot Teams in a Hunter-and-Gatherer Scenario

Exploration and Coordination of Complementary Multirobot Teams in a Hunter-and-Gatherer Scenario

Adaptive Edge-Cloud Environments for Rural AI

Experimental Validation of Entropy-Driven Swarm Exploration under Sparsity Constraints with Sparse Bayesian Learning

Contact Info

Product

Resources

About