Minimalist Multi-Robot Clustering of Square Objects: New Strategies, Experiments, and Analysis

Studies of minimalist multi-robot systems consider multiple robotic agents, each with limited individual capabilities, but with the capacity for self-organization in order to collectively perform coordinated tasks. Object clustering is a widely studied task in which self-organized robots form piles from dispersed objects. Our work considers a variation of an object clustering derived from the influential ant-inspired work of Beckers, Holland and Deneubourg which proposed stigmergy as a design principle for such multi-robot systems. Since puck mechanics contribute to cluster accrual dynamics, we studied a new scenario with square objects because these pucks into clusters differently from cylindrical ones. Although central clusters are usually desired, workspace boundaries can cause perimeter cluster formation to dominate. This research demonstrates successful clustering of square boxes - an especially challenging instance since flat edges exacerbate adhesion to boundaries - using simpler robots than previous published research. Our solution consists of two novel behaviours, Twisting and Digging, which exploit the objects’ geometry to pry boxes free from boundaries. Physical robot experiments illustrate that cooperation between twisters and diggers can succeed in forming a single central cluster. We empirically explored the significance of different divisions of labor by measuring the spatial distribution of robots and the system performance. Data from over 40 hours of physical robot experiments show that different divisions of labor have distinct features, e.g., one is reliable while another is especially efficient

New Models of Self-Organized Multi-Robot Clustering

For self-organized multi-robot systems, one of the widely studied task domains is object clustering, which involves gathering randomly scattered objects into a single pile. Earlier studies have pointed out that environment boundaries influence the cluster formation process, generally causing clusters to form around the perimeter rather than centrally within the workspace. Nevertheless, prior analytical models ignore boundary effects and employ the simplifying assumption that clusters pack into rotationally symmetric forms. In this study, we attempt to solve the problem of the boundary interference in object clustering. We propose new behaviors, twisting and digging, which exploit the geometry of the object to detach objects from the boundaries and cover different regions within the workplace. Also, we derive a set of conditions that is required to prevent boundaries causing perimeter clusters, developing a mathematical model to explain how multiple clusters evolve into a single cluster. Through analysis of the model, we show that the time-averaged spatial densities of the robots play a significant role in producing conditions which ensure that a single central cluster emerges and validate it with experiments. We further seek to understand the clustering process more broadly by investigating the problem of clustering in settings involving different object geometries. We initiate a study of this important area by considering a variety of rectangular objects that produce diverse shapes according to different packing arrangements. In addition, on the basis of the observation that cluster shape reflects object geometry, we develop cluster models that describe clustering dynamics across different object geometries. Also, we attempt to address the question of how to maximize the system performance by computing a policy for altering the robot division of labor as a function of time. We consider a sequencing strategy based on the hypothesis that since the clustering performance is influenced by the division of labor, it can be improved by sequencing different divisions of labor. We develop a stochastic model to predict clustering behavior and propose a method that uses the model's predictions to select a sequential change in labor distribution. We validate our proposed method that increases clustering performance on physical robot experiments

Swarm Synergy: A Silent Way of Forming Community

In this paper, we introduce a novel swarm application, swarm synergy, where robots in a swarm intend to form communities. Each robot is considered to make independent decisions without any communication capability (silent agent). The proposed algorithm is based on parameters local to individual robots. Engaging scenarios are studied where the silent robots form communities without the preset conditions on the number of communities, community size, goal location of each community, and specific members in the community. Our approach allows silent robots to achieve this self-organized swarm behavior using only sensory inputs from the environment. The algorithm facilitates the formation of multiple swarm communities at arbitrary locations with unspecified goal locations. We further infer the behavior of swarm synergy to ensure the anonymity/untraceability of both robots and communities. The robots intend to form a community by sensing the neighbors, creating synergy in a bounded environment. The time to achieve synergy depends on the environment boundary and the onboard sensor's field of view. Compared to the state-of-art with similar objectives, the proposed communication-free swarm synergy shows comparative time to synergize with untraceability features.Comment: 8 Pages, 8 figures, 6 tables, pre-print versio

A Multi-robot Coverage Path Planning Algorithm Based on Improved DARP Algorithm

The research on multi-robot coverage path planning (CPP) has been attracting more and more attention. In order to achieve efficient coverage, this paper proposes an improved DARP coverage algorithm. The improved DARP algorithm based on A* algorithm is used to assign tasks to robots and then combined with STC algorithm based on Up-First algorithm to achieve full coverage of the task area. Compared with the initial DARP algorithm, this algorithm has higher efficiency and higher coverage rate

On the Dynamic Evolution of Distributed Computational Aggregates

Engineering and programming approaches for collective adaptive systems often leverage ensemble-like abstractions to characterise a subset of devices as a domain for a given task or computation. In this paper, we address the problem of programming the dynamic evolution of distributed computational aggregates, through neighbour-based coordination. This is a problem of interest, since several situated activities (especially in large-scale settings) require decentralised collaboration, and need to be sustained by limited subsets of devices. These subsets may vary dynamically due to delegation, completion of local contributions, exhaustion of resources, failure, or change in the device set induced by the openness of system boundaries. In order to study and develop how distributed aggregates progressively take form by local coordination, we build on the field-based framework of aggregate processes, and extend it with techniques to support more expressive evolution dynamics. We propose novel algorithms for more effective propagation and closure of the boundaries of dynamic aggregates, based on statistics on the information speed and a notion of progressive closure through wave-like propagation. We verify the proposed techniques by simulation of a paradigmatic case study of multihop message delivery in mobile settings, and show increased performance and success rate with respect to previous work

Bayesian Clustering by Dynamics

This paper introduces a Bayesian method for clustering dynamic processes. The method models dynamics as Markov chains and then applies an agglomerative clustering procedure to discover the most probable set of clusters capturing different dynamics. To increase ef£ciency, the method uses an entropy-based heuristic search strategy. A controlled experiment suggests that the method is very accurate when applied to artificial time series in a broad range of conditions and, when applied to clustering sensor data from mobile robots, it produces clusters that are meaningful in the domain of application

A field-based computing approach to sensing-driven clustering in robot swarms

Swarm intelligence leverages collective behaviours emerging from interaction and activity of several “simple” agents to solve problems in various environments. One problem of interest in large swarms featuring a variety of sub-goals is swarm clustering, where the individuals of a swarm are assigned or choose to belong to zero or more groups, also called clusters. In this work, we address the sensing-based swarm clustering problem, where clusters are defined based on both the values sensed from the environment and the spatial distribution of the values and the agents. Moreover, we address it in a setting characterised by decentralisation of computation and interaction, and dynamicity of values and mobility of agents. For the solution, we propose to use the field-based computing paradigm, where computation and interaction are expressed in terms of a functional manipulation of fields, distributed and evolving data structures mapping each individual of the system to values over time. We devise a solution to sensing-based swarm clustering leveraging multiple concurrent field computations with limited domain and evaluate the approach experimentally by means of simulations, showing that the programmed swarms form clusters that well reflect the underlying environmental phenomena dynamics

Apple recognition and picking sequence planning for harvesting robot in the complex environment

In order to improve the efficiency of robots picking apples in challenging orchard environments, a method for precisely detecting apples and planning the picking sequence is proposed. Firstly, the EfficientFormer network serves as the foundation for YOLOV5, which uses the EF-YOLOV5s network to locate apples in difficult situations. Meanwhile, the Soft Non-Maximum Suppression (NMS) algorithm is adopted to achieve accurate identification of overlapping apples. Secondly, the adjacently identified apples are automatically divided into different picking clusters by the improved density-based spatial clustering of applications with noise (DBSCAN). Finally, the order of apple harvest is determined to guide the robot to complete the rapid picking, according to the weight of the Gauss distance weight combined with the significance level. In the experiment, the average precision of this method is 98.84%, which is 4.3% higher than that of YOLOV5s. Meanwhile, the average picking success rate and picking time are 94.8% and 2.86 seconds, respectively. Compared with sequential and random planning, the picking success rate of the proposed method is increased by 6.8% and 13.1%, respectively. The research proves that this method can accurately detect apples in complex environments and improve picking efficiency, which can provide technical support for harvesting robots