Robotic Brain Storm Optimization: A Multi-target Collaborative Searching
Paradigm for Swarm Robotics
- URL: http://arxiv.org/abs/2105.13108v1
- Date: Thu, 27 May 2021 13:05:48 GMT
- Title: Robotic Brain Storm Optimization: A Multi-target Collaborative Searching
Paradigm for Swarm Robotics
- Authors: Jian Yang and Yuhui Shi
- Abstract summary: This paper proposes a BSO-based collaborative searching framework for swarm robotics called Robotic BSO.
The proposed method can simulate the BSO's guided search characteristics and has an excellent prospect for multi-target searching problems for swarm robotics.
- Score: 24.38312890501329
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Swarm intelligence optimization algorithms can be adopted in swarm robotics
for target searching tasks in a 2-D or 3-D space by treating the target signal
strength as fitness values. Many current works in the literature have achieved
good performance in single-target search problems. However, when there are
multiple targets in an environment to be searched, many swarm
intelligence-based methods may converge to specific locations prematurely,
making it impossible to explore the environment further. The Brain Storm
Optimization (BSO) algorithm imitates a group of humans in solving problems
collectively. A series of guided searches can finally obtain a relatively
optimal solution for particular optimization problems. Furthermore, with a
suitable clustering operation, it has better multi-modal optimization
performance, i.e., it can find multiple optima in the objective space. By
matching the members in a robotic swarm to the individuals in the algorithm
under both environments and robots constraints, this paper proposes a BSO-based
collaborative searching framework for swarm robotics called Robotic BSO. The
simulation results show that the proposed method can simulate the BSO's guided
search characteristics and has an excellent prospect for multi-target searching
problems for swarm robotics.
Related papers
- CURE: Simulation-Augmented Auto-Tuning in Robotics [16.894274271669175]
This paper proposes CURE -- a method that identifies causally relevant configuration options.
CURE abstracts the causal relationships between various configuration options and robot performance objectives.
We demonstrate the effectiveness and transferability of CURE by conducting experiments in both physical robots and simulation.
arXiv Detail & Related papers (2024-02-08T04:27:14Z) - From Simulations to Reality: Enhancing Multi-Robot Exploration for Urban
Search and Rescue [46.377510400989536]
We present a novel hybrid algorithm for efficient multi-robot exploration in unknown environments with limited communication and no global positioning information.
We redefine the local best and global best positions to suit scenarios without continuous target information.
The presented work holds promise for enhancing multi-robot exploration in scenarios with limited information and communication capabilities.
arXiv Detail & Related papers (2023-11-28T17:05:25Z) - SEGO: Sequential Subgoal Optimization for Mathematical Problem-Solving [64.38649623473626]
Large Language Models (LLMs) have driven substantial progress in artificial intelligence.
We propose a novel framework called textbfSEquential subtextbfGoal textbfOptimization (SEGO) to enhance LLMs' ability to solve mathematical problems.
arXiv Detail & Related papers (2023-10-19T17:56:40Z) - Contribution \`a l'Optimisation d'un Comportement Collectif pour un
Groupe de Robots Autonomes [0.0]
This thesis studies the domain of collective robotics, and more particularly the optimization problems of multirobot systems.
The first contribution is the use of the Butterfly Algorithm Optimization (BOA) to solve the Unknown Area Exploration problem.
The second contribution is the development of a new simulation framework for benchmarking dynamic incremental problems in robotics.
arXiv Detail & Related papers (2023-06-10T21:49:08Z) - Efficient Non-Parametric Optimizer Search for Diverse Tasks [93.64739408827604]
We present the first efficient scalable and general framework that can directly search on the tasks of interest.
Inspired by the innate tree structure of the underlying math expressions, we re-arrange the spaces into a super-tree.
We adopt an adaptation of the Monte Carlo method to tree search, equipped with rejection sampling and equivalent- form detection.
arXiv Detail & Related papers (2022-09-27T17:51:31Z) - DC-MRTA: Decentralized Multi-Robot Task Allocation and Navigation in
Complex Environments [55.204450019073036]
We present a novel reinforcement learning based task allocation and decentralized navigation algorithm for mobile robots in warehouse environments.
We consider the problem of joint decentralized task allocation and navigation and present a two level approach to solve it.
We observe improvement up to 14% in terms of task completion time and up-to 40% improvement in terms of computing collision-free trajectories for the robots.
arXiv Detail & Related papers (2022-09-07T00:35:27Z) - Intelligent Trajectory Design for RIS-NOMA aided Multi-robot
Communications [59.34642007625687]
The goal is to maximize the sum-rate of whole trajectories for multi-robot system by jointly optimizing trajectories and NOMA decoding orders of robots.
An integrated machine learning (ML) scheme is proposed, which combines long short-term memory (LSTM)-autoregressive integrated moving average (ARIMA) model and dueling double deep Q-network (D$3$QN) algorithm.
arXiv Detail & Related papers (2022-05-03T17:14:47Z) - A distributed, plug-n-play algorithm for multi-robot applications with a
priori non-computable objective functions [2.2452191187045383]
In multi-robot applications, the user-defined objectives of the mission can be cast as a general optimization problem.
Standard gradient-descent-like algorithms are not applicable to these problems.
We introduce a new algorithm that carefully designs each robot's subcost function, the optimization of which can accomplish the overall team objective.
arXiv Detail & Related papers (2021-11-14T20:40:00Z) - AutoSpace: Neural Architecture Search with Less Human Interference [84.42680793945007]
Current neural architecture search (NAS) algorithms still require expert knowledge and effort to design a search space for network construction.
We propose a novel differentiable evolutionary framework named AutoSpace, which evolves the search space to an optimal one.
With the learned search space, the performance of recent NAS algorithms can be improved significantly compared with using previously manually designed spaces.
arXiv Detail & Related papers (2021-03-22T13:28:56Z) - Rapidly adapting robot swarms with Swarm Map-based Bayesian Optimisation [1.0152838128195467]
Rapid performance recovery from unforeseen environmental perturbations remains a grand challenge in swarm robotics.
To solve this challenge, we investigate a behaviour adaptation approach, where one searches an archive of controllers for potential recovery solutions.
We propose two algorithms: (i) Swarm Map-based optimisation (SMBO), which selects and evaluates one controller at a time, for a homogeneous swarm, in a centralised fashion; and (ii) Swarm Map-based optimisation Decentralised (SMBO-Dec), which performs an asynchronous batch-based Bayesian optimisation.
arXiv Detail & Related papers (2020-12-21T15:54:37Z) - Bioinspired Bipedal Locomotion Control for Humanoid Robotics Based on
EACO [1.0152838128195467]
This work presents promoting global search capability and convergence rate of the EACO applied to humanoid robots in real-time.
We put a special focus on the EACO algorithm on a wide range of problems, from ACO, real-coded GAs, GAs with neural networks(NNs), particle swarm optimization(PSO) to complex robotics systems.
arXiv Detail & Related papers (2020-10-09T09:43:48Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.