Motion Control of a Flock of 1-Trailer Robots with Swarm Avoidance

Raj, Jai; Raghuwaiya, Krishna; Sharma, Bibhya N.; Vanualailai, Jito

doi:10.1017/s0263574721000060

Cited by 16 publications

(12 citation statements)

References 52 publications

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“… Wahid et al (2017) used artificial potential field method for motion planning and kinematics for the control to design vehicle collision avoidance assistance systems. Lyapunov based control scheme (LbCS) was developed by Sharma, Vanualailai & Singh (2014) and Sharma et al (2008) and has been used by many researchers in different scenarios for robot motion planning and control ( Devi et al, 2017 ; Kumar et al, 2021 ; Prasad, Sharma & Vanualailai, 2017 ; Prasad et al, 2021 ; Raj et al, 2021 ; Raj et al, 2018 ). LbCS is a time-invariant nonlinear method that is used to create velocity or acceleration-based controls enabling a robot to move safely in the workspace while avoiding obstacles.…”

Section: Related Workmentioning

confidence: 99%

“…LbCS is a time-invariant nonlinear method that is used to create velocity or acceleration-based controls enabling a robot to move safely in the workspace while avoiding obstacles. Raj et al used LbCS to control a system of 1-trailer robots in a cluttered environment including a swarm of boids ( Raj et al, 2021 ) and navigate car-like robots in 3-dimensional space ( Raj et al, 2018 ). Prasad et al used LbCS to derive the acceleration-based controllers for the mobile manipulator in 3-dimensional ( Prasad et al, 2021 ) and to motion control a pair of cylindrical manipulators in a constrained 3-dimensional workspace ( Prasad, Sharma & Vanualailai, 2017 ).…”

Section: Related Workmentioning

confidence: 99%

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ACO-Kinematic: a hybrid first off the starting block

Chaudhary

Prasad

Chand

et al. 2022

PeerJ Computer Science

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The use of robots in carrying out various tasks is popular in many industries. In order to carry out a task, a robot has to move from one location to another using shorter, safer and smoother route. For movement, a robot has to know its destination, its previous location, a plan on the path it should take, a method for moving to the new location and a good understanding of its environment. Ultimately, the movement of the robot depends on motion planning and control algorithm. This paper considers a novel solution to the robot navigation problem by proposing a new hybrid algorithm. The hybrid algorithm is designed by combining the ant colony optimization algorithm and kinematic equations of the robot. The planning phase in the algorithm will find a route to the next step which is collision free and the control phase will move the robot to this new step. Ant colony optimization is used to plan a step for a robot and kinematic equations to control and move the robot to a location. By planning and controlling different steps, the hybrid algorithm will enable a robot to reach its destination. The proposed algorithm will be applied to multiple point-mass robot navigation in a multiple obstacle and line segment cluttered environment. In this paper, we are considering a priori known environments with static obstacles. The proposed motion planning and control algorithm is applied to the tractor-trailer robotic system. The results show a collision and obstacle free navigation to the target. This paper also measures the performance of the proposed algorithm using path length and convergence time, comparing it to a classical motion planning and control algorithm, Lyapunov based control scheme (LbCS). The results show that the proposed algorithm performs significantly better than LbCS including the avoidance of local minima.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

ACO-Kinematic: a hybrid first off the starting block

Chaudhary

Prasad

Chand

et al. 2022

PeerJ Computer Science

Self Cite

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“…Raj J.A. [16] proposed a set of nonlinear continuous control laws via the Lyapunov-based control scheme for collision, obstacle, and swarm avoidances. Additionally, a leader-follower strategy is utilized to allow the flock to split and rejoin when approaching obstacles.…”

Section: Related Workmentioning

confidence: 99%

Research on Motion Planning Based on Flocking Control and Reinforcement Learning for Multi-Robot Systems

2021

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Robots have poor adaptive ability in terms of formation control and obstacle avoidance control in unknown complex environments. To address this problem, in this paper, we propose a new motion planning method based on flocking control and reinforcement learning. It uses flocking control to implement a multi-robot orderly motion. To avoid the trap of potential fields faced during flocking control, the flocking control is optimized, and the strategy of wall-following behavior control is designed. In this paper, reinforcement learning is adopted to implement the robotic behavioral decision and to enhance the analytical and predictive abilities of the robot during motion planning in an unknown environment. A visual simulation platform is developed in this paper, on which researchers can test algorithms for multi-robot motion control, such as obstacle avoidance control, formation control, path planning and reinforcement learning strategy. As shown by the simulation experiments, the motion planning method presented in this paper can enhance the abilities of multi-robot systems to self-learn and self-adapt under a fully unknown environment with complex obstacles.

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“…Compared to existing BC schemes, the proposed MBC scheme shows superior convergence characteristics in task accomplishment and deployment efficiency. Recent research in this area focused on the motion and control planning of cooperative mobile ground robots and also aerial systems such as quadrotors [7,8,9,10], which may provide services such as coverage, search and rescue, delivery, tracking, and inspection [11,12,13,14,15].Coverage control [16,17,18] is a benchmark problem and is the topic of interest in this paper. In general, the coverage control task aims to optimally deploy a group of mobile agents over an area of interest or environment.…”

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confidence: 99%

mentioning

confidence: 99%

Coverage control of mobile agents using multi-step broadcast control

et al. 2022

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This paper proposes a novel multi-step broadcast control (MBC) scheme to deploy a group of autonomous mobile agents for accomplishing coverage tasks in a bounded region. Traditional broadcast control (BC) schemes use a one-to-all communication framework to transmit a uniform signal to all agents, making it cost-effective compared with any all-to-all communication-based scheme for a multi-agent system. However, as BC schemes are based on a single-step view of the environment for decision-making, the environment’s varying distribution density is not known immediately to the agents, resulting in suboptimal performance. To overcome this drawback, this paper proposes an MBC scheme, where agents use a predictive multi-step view and are able to detect the varying densities in the environment ahead of time. The local controller output is estimated using a weighted averaging technique which assigns a higher weight to immediate steps; this feature compensates for any decrease in prediction accuracy as the number of steps increases. We demonstrate the effectiveness of the proposed MBC scheme using a coverage task over a region with uneven population density. Compared to existing BC schemes, the proposed MBC scheme shows superior convergence characteristics in task accomplishment and deployment efficiency.

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Motion Control of a Flock of 1-Trailer Robots with Swarm Avoidance

Cited by 16 publications

References 52 publications

ACO-Kinematic: a hybrid first off the starting block

ACO-Kinematic: a hybrid first off the starting block

Research on Motion Planning Based on Flocking Control and Reinforcement Learning for Multi-Robot Systems

Coverage control of mobile agents using multi-step broadcast control

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