Motion Planning Algorithm of a Multi-Joint Snake-Like Robot Based on Improved Serpenoid Curve

Li, Dongfang; Wang, Chao; Deng, Hongbin; Wei, Yiran

doi:10.1109/access.2020.2964486

Cited by 21 publications

(14 citation statements)

References 34 publications

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“…Dehghani et al [11] developed a modified serpentine equation to reduce slipping while varying the parameters. Since then, several studies on MRs used the serpentine equation to attain a faster speed for locomotion [12]- [15].…”

Section: Introductionmentioning

confidence: 99%

Energy-Efficient Gait Optimization of Snake-Like Modular Robots by Using Multiobjective Reinforcement Learning and a Fuzzy Inference System

et al. 2022

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Snake-like modular robots (MRs) are highly flexible, but, to traverse a challenging terrain or explore a region of interest, MR needs to attain efficient locomotion depending on a tradeoff between objectives like forward velocity and power consumption of the robot. The objectives can vary with different weights depending upon the situation, reflecting relative objective importance. This study developed a multiobjective reinforcement learning algorithm based on a fuzzy inference system (FI-MORL) to select the most appropriate gait parameters of snake-like MRs according to the objective weights. The developed algorithm employs a fuzzy inference system to reduce the number of states in an environment, which results in faster learning. The proposed approach uses the previously learned experience to rapidly achieve the best objective values in response to a change in weights. While setting equal importance to the objectives, FI-MORL delivers superior performance than single-objective reinforcement learning algorithms by consuming 2% less power and gaining 2.5% higher velocity since it mitigates the effect of weight change, similar performance found comparing Actor-Critic algorithm. Likewise, the proposed method outperforms by consuming 14% less power and achieving 11% higher velocity than traditional methods like Proximal Policy Optimization, Deep Q-Network, and Vanilla Policy Gradient. Even after weight change, FI-MORL achieved a 14% higher reward than the above methods. The proposed FI-MORL framework can effectively converge quicker and efficiently handle the changes in objective weights.

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Section: Introductionmentioning

confidence: 99%

Energy-Efficient Gait Optimization of Snake-Like Modular Robots by Using Multiobjective Reinforcement Learning and a Fuzzy Inference System

et al. 2022

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“…Firstly, we optimize the dynamical model of the multi-joint snake robot with non-holonomic constraints by coordinate transformation, and the new dynamical model of the robot is obtained. The new dynamical model of the multi-joint snake robot can make the motion path converge to the desired trajectory [23]. Secondly, we develop the control objectives of the adaptive trajectory tracking controller of the multi-joint snake robot.…”

Section: Introductionmentioning

confidence: 99%

“…The non-holonomic constraints only restrict the motion of the multi-joint snake robot, and the geographical situation of the robot is not restricted, which solves the problem that the environment limits the four-link model proposed in [7]. Therefore, under the non-holonomic constraints, the robot can reduce the complexity of the system, improve the computing speed of the system, reduce the computing cost, and make up for the shortcoming of the Serpenoid control curve in [23].…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Trajectory Tracking Controller of a Multi-joint Snake Robot Considering Non-holonomic Constraints

Li¹,

Wang²,

Deng³

et al. 2021

Preprint

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Multi-joint snake robot is a vital reconnaissance, surveillance and attack weapon in national defence and military in the future. To study the trajectory tracking problem of a multi-joint snake robot with high redundancy and multi-degree of freedom in the plane, an adaptive trajectory tracking controller of a multi-joint snake robot considering non-holonomic constraints is proposed in this paper. The adaptive trajectory tracking controller replaces unknown parameters in the environment wi t h estimated values, which effectively solves the negative effects caused by uncertain and time-varying environmental parameters in the process of the robot movement and realizes the stability of the controller. Firstly, a new dynamical model of a multi-joint snake robot is established through coordinate transformation. Secondly, the control objective of the controller of the multi-joint snake robot is established. Thirdly, the proposed controller of the multi-joint snake robot is designed by the Backsteppi n g method to realize the control of the joint angle tracking error, link angle tracking error, actuator torque error and motion speed error of the robot. Then, a suitable Lyapunov function is found to verify the stability of the controller. Finally, through the MATLAB simulation and prototype experiment, the motion process of the multi-joint snake robot is observed, the trajectory tracking performance of the robot is analyzed, and the effectiveness of the adaptive trajectory tracking controller is verified.

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“…Especially in the case of a steep slope, a snake robot needs an additional sensor to receive information about the frictional force of the slope terrain, and the optimal gait type for the slope should be considered to prevent the snake robot from rolling down the steep slope, like Figure 1a. As a result, to overcome a steep slope, snake robots need to consider additional sensors and sophisticated algorithms to find the optimal gait type [23][24][25]. In 2006, Shugen Ma et al created a snake-like robot that was able to overcome a 20 degrees steep slope at a maximum forward speed of 0.2 m/s by applying the optimum winding angle to each joint [23].…”

Section: Introductionmentioning

confidence: 99%

“…In this simulation, the gait type for the snake robot only considered rolling motion [24]. In addition, in 2020, DONGFANG LI et al applied a motion planning algorithm and drive wheels to a multi-joint snake-like robot to overcome a slope of 7 degrees at a maximum speed of 0.4 m/s [25]. In conclusion, many researchers have been actively researching to find the optimal gait type for snake robots by using sensors and driving algorithms to overcome steep slopes.…”

Section: Introductionmentioning

confidence: 99%

Snake Robot with Driving Assistant Mechanism

et al. 2020

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Snake robots are composed of multiple links and joints and have a high degree of freedom. They can perform various motions and can overcome various terrains. Snake robots need additional driving algorithms and sensors that acquire terrain data in order to overcome rough terrains such as grasslands and slopes. In this study, we propose a driving assistant mechanism (DAM), which assists locomotion without additional driving algorithms and sensors. In this paper, we confirmed that the DAM prevents a roll down on a slope and increases the locomotion speed through dynamic simulation and experiments. It was possible to overcome grasslands and a 27 degrees slope without using additional driving controllers. In conclusion, we expect that a snake robot can conduct a wide range of missions well, such as exploring disaster sites and rough terrain, by using the proposed mechanism.

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Motion Planning Algorithm of a Multi-Joint Snake-Like Robot Based on Improved Serpenoid Curve

Cited by 21 publications

References 34 publications

Energy-Efficient Gait Optimization of Snake-Like Modular Robots by Using Multiobjective Reinforcement Learning and a Fuzzy Inference System

Energy-Efficient Gait Optimization of Snake-Like Modular Robots by Using Multiobjective Reinforcement Learning and a Fuzzy Inference System

An Adaptive Trajectory Tracking Controller of a Multi-joint Snake Robot Considering Non-holonomic Constraints

Snake Robot with Driving Assistant Mechanism

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