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Mobile robots are endeavoring toward full autonomy. To that end, wheeled mobile robots have to function under non-holonomic constraints and uncertainty derived by feedback sensors and/or internal dynamics. Speed control is one of the main and challenging objectives in the endeavor for efficient autonomous collision-free navigation. This paper proposes an intelligent technique for speed control of a wheeled mobile robot using a combination of fuzzy logic and supervised machine learning (SML). The technique is appropriate for flexible leader-follower formation control on straight paths where a follower robot maintains a safely varying distance from a leader robot. A fuzzy controller specifies the ultimate distance of the follower to the leader using the measurements obtained from two ultrasonic sensors. An SML algorithm estimates a proper speed for the follower based on the ultimate distance. Simulations demonstrated that the proposed technique appropriately adjusts the follower robot’s speed to maintain a flexible formation with the leader robot.

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This paper proposes a weighted double-heuristic search algorithm to find the shortest path between two points. It can be used in numerous fields such as graph theory, game theory, and network. This algorithm, called T*, uses a weighted and heuristic function as f(x) = × t(x) + × h1(x) + γ × h2(x). It selects the path which minimises f(x) where x is a current node on the path, t(x) is cost of the path from start to x, h1(x) is a heuristic to estimate the cost from x to the straight line passing through start and target, and h2(x) is a heuristic to estimate cost of the cheapest path from x to target. Furthermore, , , and γ indicate effective weights of each sub-function on f(x). T* algorithm is compared to the Greedy and A* algorithms in terms of hit rate and the number of processed nodes. Comparison results show that the proposed algorithm has a high efficiency compared to other algorithms.

Routing protocols are used in wireless sensor network (WSN) to transmit data to a centre (e.g. a base station). In this study, the authors propose a routing protocol called dynamic three-dimensional fuzzy routing based on traffic probability to enhance network lifetime and increase packet delivery ratio. It uses a fuzzy-based procedure to transmit packets by hop-to-hop delivery from source nodes toward destination nodes. The proposed fuzzy system uses two input parameters including 'distance' and 'number of neighbours' and one output parameter denoted by 'traffic probability'. When a node has a sensed data or buffered data packet, it selects one of its neighbours, called chosen node, from among a list of candidate nodes (CNs). Candidates are the neighbours which have power energy higher than average remaining energy and free buffer more than average available buffer size. Distance and number of neighbours for each CN are fed in the fuzzy system to calculate traffic probability. The CN having the lowest traffic probability is selected as an appropriate chosen node to transmit packets to the destination. Simulation results show that the proposed protocol surpasses the greedy and A* heuristic routing for wireless sensor networks in home automation, dynamic optimal progress routing, and A-star & Fuzzy methods in terms of network lifetime and packet delivery ratio.