No abstract

A more suitable motion planning method for an omni-directional mobile robot (OMR), an improved APF method (iAPF), is proposed in this paper by introducing the revolving factor into the artificial potential field (APF). Accordingly, the motion direction derived from traditional artificial potential field (tAPF) is regulated. The maximum velocity, maximum acceleration and energy consumption of the OMR moving in different directions are analyzed, based on the kinematic and dynamic constraints of an OMR, and the anisotropy of OMR is presented in this paper. Then the novel concept of an Anisotropic-Function is proposed to indicate the quality of motion in different directions, which can make a very favorable trade-off between time-optimality, stability and efficacy-optimality. In order to obtain the optimal motion, the path that the robot can take in order to avoid the obstacle safely and reach the goal in a shorter path is deduced. Finally, simulations and experiments are carried out to demonstrate that the motion resulting from the iAPF is high-speed, highly stable and highly efficient when compared to the tAPF.

PurposeThis paper aims to propose a suitable motion control method for omni‐directional mobile robots (OMRs). In RoboCup competition, the robot moves in a dynamic and oppositional environment, which occurs with high acceleration and deceleration motion frequently, especially for our OMR that slipping is almost inherently encountered in motion. Therefore, the purpose of this paper is to present one improved dynamical model with slip, and then to propose one suitable path‐tracking controller based on it, which gives more accurate control result.Design/methodology/approachA dynamic modeling method for OMRs based on the theory of vehicle dynamics is proposed. By analyzing the wheel contact friction forces both in the wheel hub rolling direction and in the roller rolling direction, an amendatory dynamics model is presented. This model is introduced into the computed‐torque‐like‐controller (CTLC) system to solve the path‐tracking problem.FindingsAn amendatory dynamics model with slip is analyzed and introduced into the CTLC system to solve the path tracking problem for OMR in this paper. The anti‐disturbance ability and the trajectory tracking effect of the proposed motion control method are proven through simulations and experiments.Practical implicationsThe proposed path tracking control method based on one improved dynamic model with slip is applied successfully to achieve effective motion control for one four‐wheel OMR, which is suitable for any kind of OMR.Originality/valueOne amendatory dynamics model including slipping between the wheels and ground is presented. Based on the above‐slipping model, one CTLC is implemented to solve the path‐tracking problem for one four‐wheel OMR.

Due to the challenge of increasing data volume, the traditional trust model is unable to manage data with high efficiency and effectively extract useful information hidden in big data. To fully utilize big data and combine machine learning with trust evaluation, a trust evaluation model based on Long short‐term memory (LSTM) is presented. The powerful learning ability, expressive ability and dynamic timing of LSTM can be applied to study data while avoiding the vanishing and exploding gradient phenomena of traditional Recurrent neural networks (RNNs) to ensure that the model can learn sequences of random length and provide accurate trust evaluation. Targeting the performance instability caused by the LSTM model's random initialization of weights and thresholds, Particle swarm optimization (PSO), one of the intelligent algorithms, is introduced to find global optimal initial weights and thresholds. Experiments proved that the trust model proposed in this paper has high accuracy and contributes a new idea for trust evaluation in big data environments.

No abstract