The Pi-puck Ecosystem: Hardware and Software Support for the e-puck and e-puck2

Allen, Jacob; Joyce, Russell; Millard, Alan G.; Gray, Ian

doi:10.1007/978-3-030-60376-2_19

Cited by 8 publications

(4 citation statements)

References 15 publications

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“…However, in practical situations, the proposed methodology struggles to work efficiently due to the poor quality of the close-range sensors. Furthermore, by employing high-precision sensors, this issue can be rectified to virtually produce any kind of map, as shown in the experiment performed by the authors in [ 36 ]. Ideally, the robots in the swarm should be as simple as possible, and should employ simple algorithms with low overhead, due to practicality.…”

Section: Related Workmentioning

confidence: 99%

DCP-SLAM: Distributed Collaborative Partial Swarm SLAM for Efficient Navigation of Autonomous Robots

Mahboob

Jokinen

Haghbayan

et al. 2023

Sensors

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Collaborative robots represent an evolution in the field of swarm robotics that is pervasive in modern industrial undertakings from manufacturing to exploration. Though there has been much work on path planning for autonomous robots employing floor plans, energy-efficient navigation of autonomous robots in unknown environments is gaining traction. This work presents a novel methodology of low-overhead collaborative sensing, run-time mapping and localization, and navigation for robot swarms. The aim is to optimize energy consumption for the swarm as a whole rather than individual robots. An energy- and information-aware management algorithm is proposed to optimize the time and energy required for a swarm of autonomous robots to move from a launch area to the predefined destination. This is achieved by modifying the classical Partial Swarm SLAM technique, whereby sections of objects discovered by different members of the swarm are stitched together and broadcast to members of the swarm. Thus, a follower can find the shortest path to the destination while avoiding even far away obstacles in an efficient manner. The proposed algorithm reduces the energy consumption of the swarm as a whole due to the fact that the leading robots sense and discover respective optimal paths and share their discoveries with the followers. The simulation results show that the robots effectively re-optimized the previous solution while sharing necessary information within the swarm. Furthermore, the efficiency of the proposed scheme is shown via comparative results, i.e., reducing traveling distance by 13% for individual robots and up to 11% for the swarm as a whole in the performed experiments.

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Section: Related Workmentioning

confidence: 99%

DCP-SLAM: Distributed Collaborative Partial Swarm SLAM for Efficient Navigation of Autonomous Robots

Mahboob

Jokinen

Haghbayan

et al. 2023

Sensors

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“…According to [85], e-puck 2 is the hardware extension of the e-puck. E-puck 2 is a Raspberry Pi-suitable platform and has high features used by swarm robotics.…”

Section: Mobile Robots Hardware Used For Swarm Roboticsmentioning

confidence: 99%

A Survey on Swarm Robotics for Area Coverage Problem

Muhsen,

Sadiq,

Raheem

2023

Algorithms

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The area coverage problem solution is one of the vital research areas which can benefit from swarm robotics. The greatest challenge to the swarm robotics system is to complete the task of covering an area effectively. Many domains where area coverage is essential include exploration, surveillance, mapping, foraging, and several other applications. This paper introduces a survey of swarm robotics in area coverage research papers from 2015 to 2022 regarding the algorithms and methods used, hardware, and applications in this domain. Different types of algorithms and hardware were dealt with and analysed; according to the analysis, the characteristics and advantages of each of them were identified, and we determined their suitability for different applications in covering the area for many goals. This study demonstrates that naturally inspired algorithms have the most significant role in swarm robotics for area coverage compared to other techniques. In addition, modern hardware has more capabilities suitable for supporting swarm robotics to cover an area, even if the environment is complex and contains static or dynamic obstacles.

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“…Regarding the information to be gathered, the experiment of Kegeleirs et al (2019) has shown that a robot swarm can produce an occupancy grid of a closed indoor environment in simulation, but struggles in reality because of poor-quality close-range proximity sensors. This means that, provided with the right sensors, a robot swarm can potentially produce any kind of map, as shown by Allen et al (2020) . However, swarm SLAM methods would benefit from low-cost, simple robots that will likely have imprecize sensors.…”

Section: Challengesmentioning

confidence: 99%

Swarm SLAM: Challenges and Perspectives

2021

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A robot swarm is a decentralized system characterized by locality of sensing and communication, self-organization, and redundancy. These characteristics allow robot swarms to achieve scalability, flexibility and fault tolerance, properties that are especially valuable in the context of simultaneous localization and mapping (SLAM), specifically in unknown environments that evolve over time. So far, research in SLAM has mainly focused on single- and centralized multi-robot systems—i.e., non-swarm systems. While these systems can produce accurate maps, they are typically not scalable, cannot easily adapt to unexpected changes in the environment, and are prone to failure in hostile environments. Swarm SLAM is a promising approach to SLAM as it could leverage the decentralized nature of a robot swarm and achieve scalable, flexible and fault-tolerant exploration and mapping. However, at the moment of writing, swarm SLAM is a rather novel idea and the field lacks definitions, frameworks, and results. In this work, we present the concept of swarm SLAM and its constraints, both from a technical and an economical point of view. In particular, we highlight the main challenges of swarm SLAM for gathering, sharing, and retrieving information. We also discuss the strengths and weaknesses of this approach against traditional multi-robot SLAM. We believe that swarm SLAM will be particularly useful to produce abstract maps such as topological or simple semantic maps and to operate under time or cost constraints.

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The Pi-puck Ecosystem: Hardware and Software Support for the e-puck and e-puck2

Cited by 8 publications

References 15 publications

DCP-SLAM: Distributed Collaborative Partial Swarm SLAM for Efficient Navigation of Autonomous Robots

DCP-SLAM: Distributed Collaborative Partial Swarm SLAM for Efficient Navigation of Autonomous Robots

A Survey on Swarm Robotics for Area Coverage Problem

Swarm SLAM: Challenges and Perspectives

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