A new methodology named Evolutionary Artificial Potential Field (EAPF) is proposed for real-time robot path planning. The artificial potential field method is combined with genetic algorithms, to derive optimal potential field functions. The proposed EAPF approach is capable of navigating robot(s) situated among moving obstacles. Potential field functions for obstacles and goal points are also defined. The potential field functions for obstacles contain tunable parameters. The multi-objective evolutionary algorithm (MOEA) is utilized to identify the optimal potential field functions. Fitness functions such as goal-factor, obstacle-factor, smoothness-factor and minimum-pathlength-factor are developed for the MOEA selection criteria. An algorithm named escape-force is introduced to avoid the local minima associated with EAPF. Moving obstacles and moving goal positions were considered to test the robust performance of the proposed methodology. Simulation results show that the proposed methodology is efficient and robust for robot path planning with non-stationary goals and obstacles.

Evolutionary artificial potential fields and their application in real time robot path planning

This paper reviews the major contributions to the Motion Planning (MP) field throughout a 35-year period, from classic approaches to heuristic algorithms Due to the NP-Hardness of the MP problem, heuristic methods have outperformed the classic approaches and have gained wide popularity After surveying around 1400 papers in the field, the amount of existing works for each method is identified and classified Especially, the history and applications of numerous heuristic methods in MP is investigated The paper concludes with comparative tables and graphs demonstrating the frequency of each MP method’s application, and so can be used as a guideline for MP researchers Keywords—Robot motion planning, Heuristic algorithms

Classic and Heuristic Approaches in Robot Motion Planning A Chronological Review

This paper presents a novel Behavior Fusion method for the navigation of Autonomous Mobile Vehicle in unknown environments. The proposed navigator consists of an Obstacle Avoider (OA), a Goal Seeker (GS) and a Navigation Supervisor (NS). The fuzzy actions inferred by the OA and the GS are weighted by the NS using the local and global environmental information and fused through fuzzy set operation to produce a command action, from which the final crisp action is determined by defuzzification. Simulation shows that the navigator is able to perform successful navigation task in various unknown environments, and it has smooth action and exceptionally good robustness to sensor noise.

A novel behavior fusion method for the navigation of mobile robots

This paper proposes a nonlinear model to characterize a 10/8 switched reluctance motor (SRM) in terms of Fourier series expression of phase self-inductance under the assumption of negligible mutual effect and various losses. It introduces a torque ripple minimization method by means of indirect dynamic torque control, then describes the corresponding control conception based on a torque controller (for regulating phase reference current) and a fuzzy logic controller (for adjusting turn-on and turn-off angles). Simulation results have shown the feasibility of the developed approach to minimize the torque ripple.

Modeling and nonlinear control of a switched reluctance motor to minimize torque ripple

This paper presents a novel navigation method for Autonomous Mobile Vehicle in unknown environments. The proposed navigator consists of an Obstacle Avoider (OA), a Goal Seeker (GS), a Navigation Supervisor (NS) and an Environment Evaluator (EE). The fuzzy actions inferred by the OA and the GS are weighted by the NS using the local and global environmental information and fused through fuzzy set operation to produce a command action, from which the final crisp action is determined by defuzzification. The EE tunes the supports of the fuzzy sets for the OA and the NS; therefore, the capability of the navigation method is enhanced. Simulation shows that the navigator is able to perform successful navigation task in various unknown or partially known environments, and it has satisfactory ability in tackling moving obstacles. More importantly, it has smooth action and exceptionally good robustness to sensor noise.

A Novel Navigation Method for Autonomous Mobile Vehicles

An advanced concept to solve the collision avoidance problem in multi-robot workcells is presented which holds realtime conditions for industrial robot controllers. The approach is based on a fuzzy set description of critical configurations between robots in a common workspace. The authors' strategy assumes a careful offline path planning to unburden the algorithm from online path planning but to handle extraordinary events, which can not be treated offline. Though the authors' collision avoidance strategy is local, in general it switches towards the preplanned trajectory with decreasing degree of conflict and finally reaches planned goal positions. To handle the collision avoidance problem in realtime, the complex kinematic chain of any industrial robot with six degrees of freedom is replaced by a drastically reduced actual robot (RAR). Fuzzification of the actual RAR position in a workspace provides a fuzzy membership vector to sectors, which subdivide the robots' cylindrical workspace. A practical fuzzy-rulebase is given to evaluate the degree of conflict between the actual fuzzy membership vectors of two RARs and to propose secure fuzzy-membership vectors. Defuzzification gives crisp locations of a collision-free trajectory which are restricted by the preplanned trajectory locations if the conflict is small. The efficiency of this strategy is illustrated by an example with two PUMA 562 robots in decoupled motion.

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H. Hoyer

Papers

Fuzzy collision avoidance for industrial robots