Particle swarm optimization-based central patter generator for robotic fish locomotion

Jeong, In-Bae; Park, Chang-Soo; Na, Ki‐In; Han, Seung-Beom; Kim, Jong-Hwan

doi:10.1109/cec.2011.5949612

Cited by 19 publications

(20 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The proposed approach is more practical for underwater robots in contrast with the simulation-based optimization in [19] and [20] as mentioned in the introduction. We believe that the empirical approach is the only reliable way for the gait optimization of underwater robots because the unstructured water environment cannot be modeled accurately at present.…”

Section: Discussionmentioning

confidence: 99%

“…The position x i and velocity v i , respectively, represent the parameter values and the search direction of particle i. The iteration equation takes the form (19) where p i is the personal best-known position of particle i and p g is the global best-known position of all particles, t is the current generation of particles and t + 1 is the next generation. c 1 and c 2 are the cognitive factor and the social factor, respectively.…”

Section: Optimization Algorithmmentioning

confidence: 99%

“…The algorithm we chose is the PSO method. PSO a population-based evolutionary algorithm and can always converge rapidly in developing the better locomotive gaits of many kinds of robots, such as biped robots [39], swimming robots [19], [20], and modular robots [18].…”

Section: Optimization Algorithmmentioning

confidence: 99%

“…When an optimal gait is acquired in the simulation, it is later transferred to the physical robot. For example, particle swarm optimization (PSO) was adopted to search preferable parameters of the controller for the speed of the robotic fish in simulations [19], [20], which illustrates that PSO is a viable evolutionary algorithm. Learning in the simulation, however, is not always feasible because it is still difficult to precisely simulate the dynamics of water environment at present.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Autonomous Optimization of Swimming Gait in a Fish Robot With Multiple Onboard Sensors

Wang

Xie

2019

IEEE Trans. Syst. Man Cybern, Syst.

View full text Add to dashboard Cite

Abstract-Autonomous gait optimization is an essential survival ability for mobile robots. However, it remains a challenging task for underwater robots. This paper addresses this problem for the locomotion of a bio-inspired robotic fish and aims at identifying fast swimming gait autonomously by the robot. Our approach for learning locomotion controllers mainly uses three components: 1) a biological concept of central pattern generator to obtain specific gaits; 2) an onboard sensory processing center to discover the environment and to evaluate the swimming gait; and 3) an evolutionary algorithm referred to as particle swarm optimization. A key aspect of our approach is the swimming gait of the robot is optimized autonomously, equivalent to that the robot is able to navigate and evaluate its swimming gait in the environment by the onboard sensors, and simultaneously run a built-in evolutionary algorithm to optimize its locomotion all by itself. Forward speed optimization experiments conducted on the robotic fish demonstrate the effectiveness of the developed autonomous optimization system. The latest results show that our robotic fish attained a maximum swimming speed of 1.011 BL/s (40.42 cm/s) through autonomous gait optimization, faster than any of the robot's previously recorded speeds.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Optimization Algorithmmentioning

confidence: 99%

Section: Optimization Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Autonomous Optimization of Swimming Gait in a Fish Robot With Multiple Onboard Sensors

Wang

Xie

2019

IEEE Trans. Syst. Man Cybern, Syst.

View full text Add to dashboard Cite

show abstract

“…In contrast, closed-loop CPG controllers always integrate sensory feedback into the CPG network to regulate the CPG behaviours automatically, which is more like the real mechanism of animal CPG activities. According to the specific surroundings and tasks, closed-loop CPG controllers are able to realize automatic swimming gait transition [9], pattern synchronization [16], gait stability [17] and gait optimization [18].…”

Section: Introductionmentioning

confidence: 99%

CPG-based Locomotion Controller Design for a Boxfish-like Robot

Wang

Xie

2014

International Journal of Advanced Robotic Systems

View full text Add to dashboard Cite

This paper focuses on a Central Pattern Generator (CPG)-based locomotion controller design for a boxfish-like robot. The bio-inspired controller is aimed at flexible switching in multiple 3D swimming patterns and exact attitude control of yaw and roll such that the robot will swim more like a real boxfish. The CPG network comprises two layers, the lower layer is the network of coupled linear oscillators and the upper is the transition layer where the lower-dimensional locomotion stimuli are transformed into the higher-dimensional control parameters serving for all the oscillators. Based on such a two-layer framework, flexible switching between multiple three-dimensional swimming patterns, such as swimming forwards/backwards, turning left/right, swimming upwards/downwards and rolling clockwise/counter-clockwise, can be simply realized by inputting different stimuli. Moreover, the stability of the CPG network is strictly proved to guarantee the intrinsic stability of the swimming patterns. As to exact attitude control, based on this open-loop CPG network and the sensory feedback from the Inertial Measurement Unit (IMU), a closed-loop CPG controller is advanced for yaw and roll control of the robotic fish for the first time. This CPG-based online attitude control for a robotic fish will greatly facilitate high-level practical underwater applications. A series of relevant experiments with the robotic fish are conducted systematically to validate the effectiveness and stability of the open-loop and closed-loop CPG controllers.

show abstract

Amphibious Pattern Design of a Robotic Fish with Wheel‐propeller‐fin Mechanisms

Ding

Yang

et al. 2013

Journal of Field Robotics

View full text Add to dashboard Cite

This paper is devoted to the underwater and terrestrial locomotion aspects of an amphibious robotic fish propelled by modular fish‐like propelling units and a pair of hybrid wheel‐propeller‐fin mechanisms. According to the mechanical structure and locomotion characteristics of the robot, a central pattern generator (CPG) network comprising coupled oscillators is employed to produce signals for swimming, crawling, as well as transitions between them. Specifically, a set of four key parameters including a tonic input drive, a direction factor, and two pitch factors is introduced to serve as input to the CPG network. Meanwhile, a finite state machine is built to trigger locomotor pattern transitions. Field tests on the amphibious patterns and autonomous water‐land transition demonstrate the effectiveness of the adopted CPG‐based control architecture. The latest results show that the robot attained a maximum advancing speed of 1.16 m/s (corresponding to 1.66 body lengths per second), a minimal turning radius of approximately 0.55 m (corresponding to 0.79 body lengths) on land, as well as an average rolling speed of 204 degrees per second in an alligator‐like roll maneuver. It is also found that the dolphin‐like dorsoventral swimming could provide an increase of 10.3% in speed compared to the fish‐like carangiform swimming on the same propulsion platform.

show abstract

Particle swarm optimization-based central patter generator for robotic fish locomotion

Cited by 19 publications

References 17 publications

Autonomous Optimization of Swimming Gait in a Fish Robot With Multiple Onboard Sensors

Autonomous Optimization of Swimming Gait in a Fish Robot With Multiple Onboard Sensors

CPG-based Locomotion Controller Design for a Boxfish-like Robot

Amphibious Pattern Design of a Robotic Fish with Wheel‐propeller‐fin Mechanisms

Contact Info

Product

Resources

About