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In this paper, a dynamic model of a wheeled mobile robotic (WMR) system with coupled control input is developed, which will increase the complexity of its tracking control with time-varying advance angle. To deal with this problem, a partial reinforcement learning neural network (PRLNN)-based tracking algorithm is proposed for the WMR systems. The main contributions of the PRLNN adaptive tracking control method is that it is the first control method to introduce the PRLNN adaptive control to the WMR system, which determines to solve the WMR tracking control with the time-varying advance angle. The critic neural network (NN) and action NN adaptive laws for the decoupled controllers are designed using the standard gradient-based adaptation method. According to the Lyapunov stability analysis theorem, the uniform ultimate boundedness of all signals in the WMR system can be guaranteed with the design parameters chose properly, and the tracking error converge to a small compact set nearby zero. A numerical simulation is presented to verify the effectiveness of the proposed control algorithm. [ABSTRACT FROM AUTHOR]

A group of mobile robots (beachcombers) have to search collectively every point of a given domain. At any given moment, each robot can be in walking mode or in searching mode. It is assumed that each robot's maximum allowed searching speed is strictly smaller than its maximum allowed walking speed. A point of the domain is searched if at least one of the robots visits it in searching mode. The Beachcombers' Problem consists in developing efficient schedules (algorithms) for the robots which collectively search all the points of the given domain as fast as possible. We consider searching schedules in the following one-dimensional geometric domains: the cycle of a known circumference L , the finite straight line segment of a known length L , and the semi-infinite line [ 0 , + ∞). We first consider the online Beachcombers' Problem (i.e. the scenario when the robots do not know in advance the length of the segment to be searched), where the robots are initially collocated at the origin of a semi-infinite line. It is sought to design a schedule A with maximum speed S , defined as S = inf ℓ ⁡ ℓ t A (ℓ) , where t A (ℓ) denotes the time when the search of the segment [ 0 , ℓ ] is completed under A. We consider a discrete and a continuous version of the problem, depending on whether the infimum is taken over ℓ ∈ N ⁎ or ℓ ≥ 1. We prove that the LeapFrog algorithm, which was proposed in Czyzowicz et al. (2015) [12] , is in fact optimal in the discrete case. This settles in the affirmative a conjecture from that paper. We also show how to extend this result to the more general continuous online setting. For the offline version of the Beachcombers' Problem (i.e. the scenario when the robots know in advance the length of the segment to be searched), we consider the t-source Beachcombers' Problem (i.e. all robots start from a fixed number t ≥ 1 of starting positions) on the cycle and on the finite segment. For the t-source Beachcombers' Problem on the cycle, we show that the structure of the optimal solutions is identical to the structure of the optimal solutions to the 2 t -source Beachcombers' Problem on a finite segment. In consequence, by using results from Czyzowicz et al. (2014) [13] , we prove that the 1-source Beachcombers' Problem on the cycle is NP-hard, and we derive approximation algorithms for the problem. For the t-source variant of the Beachcombers' Problem on the cycle and on the finite segment, we also derive efficient approximation algorithms. One important contribution of our work is that, in all variants of the offline Beachcombers' Problem that we discuss, we allow the robots to change direction of movement and search points of the domain on both sides of their respective starting positions. This represents a significant generalization compared to the model considered in Czyzowicz et al. (2014) [13] , in which each robot had a fixed direction of movement that was specified as part of the solution to the problem. We manage to prove that changes of direction do not help the robots achieve optimality. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of trajectory planning of a mobile robot for pasture maintenance comprising mulching weeds, reseeding patches without vegetation and spreading cowpats. Based on the sensor-based acquired data (points of interest), the proposed approach is to first use an approximation algorithm for data clustering in the form of non-convex and convex hulls. These hulls are then delimited by stair-shaped limits with respect to the working width of the robot, and their centres of gravity calculated. To minimise the travelled distance between the centres of gravity of the defined areas, the Travelling Salesman Problem is addressed via an evolutionary algorithm. Finally, kinematic and dynamic properties of the robot are considered in order to generate the final trajectory. The capabilities of the proposed approaches are highlighted through the processing of several datasets. Graphical abstract (a)-Terestrial information about the field (b, c, d, e)-Sensor-based data about the field considering weed map, areas without vegetation and areas with cowpats (f)-Detail of the calculated path for the pasture robot based on convex hulls, centre of gravity, travelling salesman and evolutionary algorithm. Image 1 Highlights • Method development for selectively pasture maintenance with robotics. • Path planning for a mobile robot considering selectively pasture maintenance. • Data clustering based on approximation algorithm using non-convex and convex hulls. • Path optimization using evolutionary algorithm for Traveling Salesman Problem. • Trajectory calculation in respect of kinematic and dynamic properties of the robot. [ABSTRACT FROM AUTHOR]

In several multirobot applications in which communication is limited, the mission could require the robots to iteratively take coordinated joint decisions on how to spread out in the environment and on how to reconnect with each other to share data and compute plans. Exploration and surveillance are examples of these applications. In this paper, we consider the problem of computing robots’ paths on a graph-represented environment for restoring connections at minimum traveling cost. We call it the multirobot reconnection problem, we show its NP-hardness and hardness of approximation on some important classes of graphs, and we provide optimal and heuristic algorithms to solve it in practical settings. The techniques we propose are then exploited to derive a new efficient planning algorithm for a relevant connectivity-constrained multirobot planning problem addressed in the literature, the multirobot informative path planning with periodic connectivity problem. [ABSTRACT FROM AUTHOR]