Robotic Swarm Motion Planning for Load Carrying and Manipulating

Medina, Oded; Hacohen, Shlomi; Shvalb, Nir

doi:10.1109/access.2020.2979929

Cited by 14 publications

(14 citation statements)

References 38 publications

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“…The information regarding the rod's orientation, as well as the fellow agent position, was assumed to be known. In [28] the authors of this paper recently introduced a spatial-load-carrying swarm where a decentralized control scheme with information sharing was applied. The swarm was given the task of maneuvering through a planar-cluttered space with additional obstacles in the load's path.…”

Section: Decentralized With Information-sharingmentioning

confidence: 99%

“…In our precursory research [28] we assumed that all agents are fully aware of the (estimated) position of all other agents (i.e., they share only their estimated position, not their planned velocities). However, in the current research, each time step an agent proceeds towards its own goal while no information regarding the other agents' position is available.…”

Section: B Communicationmentioning

confidence: 99%

“…We use the kinematic model provided in [28]: Figure 2 depicts the i th agent position vector x i , the load center of gravity position p, the i th rod vector l i and the i th load's node b i , which are given in global coordinates, with vector b i written in the load's coordinate system. The 3 × 3 load's rotation matrix R sets:…”

Section: Kinematicsmentioning

confidence: 99%

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Decentralized Motion Planning for Load Carrying and Manipulating by a Robotic Pack

et al. 2023

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In many cases, a pack of robots holds an advantage over a single robot such when an oversized or over-weighted load is to be carried. In such cases, a single robot will not do. Nevertheless, this may not be an easy task for a pack of robots as well, especially when the load needs to be lifted off the ground making the cooperative task less tolerant of errors. The limited research on such a load can be attributed to the mechanical complexity of the problem. Notably, previous studies have not considered the spatial, decentralized, communication-free scenario. We, therefore, consider a robotic pack of six agents that assumes the task of spatially moving a load through a cluttered space. As it transports the load, the pack carefully avoids planar obstacles while maintaining its orientation. To do so, we model the whole system as a six Prismatic-Prismatic-Spherical-Spherical (6-PPSS) redundant mobile platform, having twelve degrees of freedom. This paper focuses on a decentralized control scheme where no mutual communication is needed. Each agent calculates its ego movements according to the height of its corresponding load-node; the surrounding obstacles, and; the goal's relative position. To avoid numerical errors appearing in the vicinity of singular configurations, we calculate the platform's forward kinematics in the model's full configuration space. We then show how this rationale can be further extended to formulate a distributed control scheme for the motion planner. We demonstrate our algorithms in several simulated scenarios and in a set of real-world experiments using specially designed omnidirectional robot agents. We test the ability of the pack to maintain the load's orientation just by measuring the load's height at the holding node of each agent. Lastly, we measured the time required for the pack to assume a desired load orientation. Results indicated that even in the presence of a 10-degree tilt error, the load was able to be restabilized within a maximum of 15 seconds in simulated conditions and 20 seconds in real-life experiments.INDEX TERMS Swarm robotics, parallel robots, motion planning, decentralized control. I. INTRODUCTION RELATED WORKEarly research mainly considered centralized control for the load carrying task. For example, in [33], where the researchers considered a swarm of communicating agents that manipulate planar loads via cooperative pushing. Since

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Section: Decentralized With Information-sharingmentioning

confidence: 99%

Section: B Communicationmentioning

confidence: 99%

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Decentralized Motion Planning for Load Carrying and Manipulating by a Robotic Pack

et al. 2023

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“…Researchers investigated MRS with every individual playing the same role, such that the MRS can be more flexible in their tasks. A swarm of simulated mobile robots were introduced in a task of cooperatively transporting a payload around obstacles [21], however, kinematics modeling and path planning were required. A two-stage motion planning strategy was proposed in [22], [23] to help the MRS safely transport an object around dynamic obstacles.…”

Section: Related Workmentioning

confidence: 99%

Decentralized Control of Multi-Robot System in Cooperative Object Transportation Using Deep Reinforcement Learning

et al. 2020

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Object transportation could be a challenging problem for a single robot due to the oversize and/or overweight issues. A multi-robot system can take the advantage of increased driving power and more flexible configuration to solve such a problem. However, an increased number of individuals also changed the dynamics of the system which makes control of a multi-robot system more complicated. Even worse, if the whole system is sitting on a centralized decision making unit, the data flow could be easily overloaded due to the upscaling of the system. In this research, we propose a decentralized control scheme on a multi-robot system with each individual equipped with a deep Q-network (DQN) controller to perform an oversized object transportation task. DQN is a deep reinforcement learning algorithm, thus does not require the knowledge of system dynamics, instead, it enables the robots to learn appropriate control strategies through trial-and-error style interactions within the task environment. Since analogous controllers are distributed on the individuals, the computational bottleneck is avoided systematically. We demonstrate such a system in a scenario of carrying an oversized rod through a doorway by a two-robot team. The presented multi-robot system learns abstract features of the task and cooperative behaviors are observed. The decentralized DQN-style controller is showing strong robustness against uncertainties. In addition, We propose a universal metric to assess the cooperation quantitatively.

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“…In prior work [ 41 ] the authors introduced an algorithm for estimating a swarm distribution. The algorithm is based on a simple sensing capability of measuring an angular location of other agents (relative to a global x -axis).…”

Section: Introductionmentioning

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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Robotic Swarm Motion Planning for Load Carrying and Manipulating

Cited by 14 publications

References 38 publications

Decentralized Motion Planning for Load Carrying and Manipulating by a Robotic Pack

Decentralized Motion Planning for Load Carrying and Manipulating by a Robotic Pack

Decentralized Control of Multi-Robot System in Cooperative Object Transportation Using Deep Reinforcement Learning

Improved GNSS Localization and Byzantine Detection in UAV Swarms

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