Fuzzy Planner Research Articles

Multi-factor of path planning based on an ant colony optimization algorithm

ABSTRACT We propose an improved ant colony algorithm for avoiding obstacles in complex static environments that addresses the problems of a single evaluation factor and low path quality of the traditional ant colony algorithm in path planning. The improvements are: 1) a fuzzy planner is constructed according to the comprehensive evaluation method of fuzzy mathematics and the analytic hierarchy process to comprehensively evaluate and determine the impact of environmental factors, 2) the probability selection formula of the ant colony algorithm is optimized, 3) the pheromone update formula is optimized, and 4) the corner system mechanism is introduced as a post-processing method of path optimization to further smooth the path. Results from simulation experiments of the traditional ant colony algorithm were analysed and compared with those of the improved ant colony algorithm, showing that the latter has a stronger path planning ability and higher algorithm efficiency, resulting in a smoother path with a lower negative impact by environmental factors. Thus, the proposed algorithm is expected to provide a computational basis for effective multi-factor path planning in realistic environments, thereby saving human and material resources.

Formulation of FISPLAN: A Fuzzy Logic based Reactive Planner for AUVs towards Situation Aware Control

the paper presents a detailed discussion on the structural organisation of a Fuzzy Inference System Planner (FISPLAN) for Autonomous Underwater Vehicles (AUVs), including elaboration of membership functions for the inputs as well as outputs. The inference mechanism is detailed with discussions on the rule base, which in essence incorporates the planning logic. In order to assess the effectiveness of the planner as a means of reactive escape under critical situations, a case study is studied with reference to a state of the art AUV. An approximate subsea current model is developed from field observations, and residual energy is estimated by referring to a typical Lithium-polymer cell discharge characteristic together with data recorded in actual field trials. Situations are simulated by considering different combinations of sea-currents as well as status of resident energy. Results reveal that the simulated system, by virtue of the planner, is capable of perceiving situations, thereby realizing their imminence and making a decisive action thereupon. In concise, the fuzzy planner may be considered to provide human-like perception of situations on the basis of crisp observations. Furthermore dynamics of the system are modelled with actual parameters, and subsequently controller responses for pitching and velocity correction are illustrated. Choice of planning interval is also expressed as a function of the controllers' response.

Shape control of cyclic networks in multi-robot formations

The first part of this paper examines cycles at the network level of sensing and control architectures that are needed to maintain the shape of a multiagent formation in 2-dimensional space, while the formation moves as a cohesive whole. The key tools used in the paper are rigidity theory and graph theory. The second part of the paper focuses on the planner level and controller level of the architecture by designing the fuzzy planner and fuzzy controller of individual agents. This permits the decomposition of the complex coordination problem into a series of smaller ones. The fuzzy planner and the fuzzy controller on kinematic unicycles are simulated for an exemplary cyclic formation generated by the results obtained in the first half of the paper. The validity of the proposed approach in the shape control of multiagent formations is verified through simulation experiments.

Evolving robotic path with genetically optimised fuzzy planner

Path planning is one of the highly studied problems in the field of robotics. The problem has been solved using numerous statistical, soft computing and other approaches. In this paper, we evolve the robotic path using genetic algorithms (GA). The GA generates solutions for the static map which disobeys the non-holonomic constraints. Fuzzy inference system (FIS) works on the generated path and extends the problem for dynamic environment. The results of GA serve as a guide for FIS planner. The FIS system was initially generated using rules from common sense. Once this model was ready, the fuzzy parameters were optimised by another GA. The GA tried to optimise the distance from the closest obstacle, total path length and the sharpest turn at any time in the journey of the robot. The resulting FIS was easily able to execute the plan of the robot in a dynamic environment. We tested the algorithm on various complex and simple paths. All paths generated were optimal in terms of path length and smoothness. Hence, using a combination of GA along with FIS, we were able to solve the problem of robotic path planning.

Fuzzy Planner: Reasoning with Inexact Concepts in a Procedural Problem-Solving Language

Abstract All contemporary deductive problem-solving paradigms deal with a world in which assertions are true (false) and action-rules valid (invalid). This simplified situation is inadequate for realistic applications which include inexact information. This report describes a precise computationally specific method for coupling two different many-valued logics with a procedural problem-solving system (micro-PLANNER). Solutions to deductive problems can be found which meet specific criteria of validity. This particular scheme enables the system to dynamically compute the truth-value of a subgoal during the search process. Thus, the validity of a subgoal may be used to direct the heuristic search procedure. Fuzzy PLANNER is a promising medium for experimenting with different many-valued logics to find the ones most appropriate for different problem domains. However, the notions elaborated here are relevent to any procedural problem-solving language.