Search

Informative path planning algorithms are of paramount importance in applications like disaster management to efficiently gather information through a priori unknown environments. This is, however, a complex problem that involves finding a globally optimal path that gathers the maximum amount of information (e.g., the largest map with a minimum travelling distance) while using partial and uncertain local measurements. This paper addresses this problem by proposing a novel heuristic algorithm that continuously estimates the potential mapping gain for different sub-areas across the partially created map, and then uses these estimations to locally navigate the robot. Furthermore, this paper presents a novel algorithm to calculate a benchmark solution, where the map is a priori known to the planar, to evaluate the efficacy of the developed heuristic algorithm over different test scenarios. The findings indicate that the efficiency of the proposed algorithm, measured in terms of the mapped area per unit of travelling distance, ranges from 70% to 80% of the benchmark solution in various test scenarios. In essence, the algorithm demonstrates the capability to generate paths that come close to the globally optimal path provided by the benchmark solution., Comment: 11 Pages, 11 Figures

Unlike conventional cars, connected and autonomous vehicles (CAVs) can cross intersections in a lane-free order and utilise the whole area of intersections. This paper presents a minimum-time optimal control problem to centrally control the CAVs to simultaneously cross an intersection in the shortest possible time. Dual problem theory is employed to convexify the constraints of CAVs to avoid collision with each other and with road boundaries. The developed formulation is smooth and solvable by gradient-based algorithms. Simulation results show that the proposed strategy reduces the crossing time of intersections by an average of 52% and 54% as compared to, respectively, the state-of-the-art reservation-based and lane-free methods. Furthermore, the crossing time by the proposed strategy is fixed to a constant value for an intersection regardless of the number of CAVs., Comment: 6 pages, 4 figures, conference paper. arXiv admin note: substantial text overlap with arXiv:2204.03550, arXiv:2204.04156

Connected and autonomous vehicles (CAVs), unlike conventional cars, will utilise the whole space of intersections and cross in a lane-free order. This paper formulates such a lane-free crossing of intersections as a multi-objective optimal control problem (OCP) that minimises the overall crossing time, as well as the energy consumption of CAVs. The proposed OCP is convexified by applying the dual problem theory to the constraints that avoid collision of vehicles with each other and with road boundaries. The resulting OCP is smooth and solvable by gradient-based algorithms. Simulation results show that the proposed algorithm reduces the crossing time by an average of 40% and 41% as compared to, respectively, the state-of-the-art reservation-based and lane-free methods, whilst consuming the same amount of energy. Furthermore, it is shown that the resulting crossing time of the proposed algorithm is i) fixed to a constant value regardless of the number of CAVs, and ii) very close to its theoretical limit., Comment: 11 Pages, 15 figures and journal. arXiv admin note: substantial text overlap with arXiv:2204.03550

Connected and autonomous vehicles (CAVs) improve the throughput of intersections by crossing in a lane-free order as compared to the signalised crossing of human drivers. However, it is challenging to quantify such an improvement because the available frameworks to analyse the capacity (i.e., the maximum throughput) of the conventional intersections does not apply to the lane-free ones. This paper proposes a novel theoretical framework to numerically simulate and compare the capacity of lane-free and conventional intersections. The results show that the maximum number of vehicles passing through a lane-free intersection is up to seven times more than a signalised intersection managed by the state-of-the-art max-pressure and Webster algorithms. A sensitivity analysis shows that, in contrast to the signalised intersections, the capacity of the lane-free intersections improves by an increase in initial speed, the maximum permissible speed and acceleration of vehicles., Comment: 11 pages, 13 figures, Journal paper