This paper presents a fault-tolerant PD tracking system for Multirotor Aerial Vehicles (MAV) based on a novel Recursive Least Squares (RLS) Fault Detection and Isolation (FDI) algorithm utilized to diagnose propulsion system faults. As a test platform we investigate an octorotor model, including rigid body dynamics, the gyroscopic effect and motor dynamics. A hover configuration control is extended into an adaptive, fault-tolerant PD tracking controller. The approach is validated within a simulation study that includes a severe triple rotor fault scenario.

A globally stabilizing nonlinear predictive control (NMPC) framework is developed by a simple design of the terminal cost used in relevant optimization setup.We first adapt the recent results on the finite horizon state-dependent Riccati equation (SDRE) based control for unconstrained nonlinear systems to prove that such a control can be globally stabilizing in case a sufficiently large optimization horizon is selected. Then, we use the resulting globally stabilizing Lyapunov function as a terminal cost within the optimization setup to get a novel NMPC. We also adapt the results on the stability of the NMPC, based on the control Lyapunov function approach to prove global stability of the proposed control framework. The results are validated through extensive simulation setups for some unconstrained nonlinear dynamical systems.

This paper proposes a receding horizon optimization framework (RHC) for finding an approximate solution to different constrained multi-vehicle coverage problems. The optimization is based on the algorithm we have already developed for unconstrained multi-vehicle coverage problem, which inherently possessed some nice properties for dealing with unconstrained coverage problem setups. Although it was shown that the algorithm had preferred to choose obstacle-free areas during the task execution, it was not possible to guarantee collision free paths. The proposed RHC is, however, capable of handling constraints that might be present within a coverage problem, such as those imposed by the presence of obstacles and/or by different time limitations imposed on the duration of the vehicles' missions.

Obtaining the optimal cost-to-go map for large scale rough terrains is computationally very expensive both in terms of duration and memory resources. A fast algorithm for approximation of the optimal cost-to-go map in terms of terrain traversability measures for path planning on known large scale rough terrains is developed. The results show that the majority of the cost-to-go map values, computed from every terrain location with respect to the goal location, are near-optimal. Unlike Dijkstra algorithm, the proposed algorithm has inherently parallel structure, and can be significantly speeded up depending on the number of used CPU cores.

The paper presents a new solution to the multi-vehicle coverage problem. The proposed algorithm guarantees complete coverage and provides collaborative behaviors of vehicles, despite the fact that it does not explicitly exploit any computationally intensive optimization technique. The algorithm can deal with any mission domain, including regions with irregular shapes, multi-connected and disjoint regions. It gives reasonably good solutions even for partially connected multi-vehicle systems. The coverage problem for regions the shape of which change in time regardless the vehicle movement is also solved by the proposed algorithm.

The Roughness based Navigation Function (RbNF) is a numerical map that estimates the mobility measure (cost-to-go) from each terrain location toward the goal position. This paper compares the RbNF and the optimal cost-to-go map in terms of computational burden and solution quality. When the terrain is very large and obtaining the optimal cost-to-go map is computationally too expensive, the RbNF is shown to be able to compute an approximate solution much more quickly. As an application example, in which the RbNF is shown to be a powerful tool, the paper considers a Mars rover mission that finds the possible landing site using a mobility cost-to-go map constructed from a Mars terrain data.

Planning and control for a wheeled mobile robot are challenging problems when poorly traversable terrains, including dynamic obstacles, are considered. To accomplish a mission, the control system should firstly guarantee the vehicle integrity, for example with respect to possible roll-over/tip-over phenomena. A fundamental contribution to achieve this goal, however, comes from the planner as well. In fact, computing a path that takes into account the terrain traversability, the kinematic and dynamic vehicle constraints, and the presence of dynamic obstacles, is a first and crucial step towards ensuring the vehicle integrity. The present paper addresses some of the aforementioned issues, describing the hardware/software architecture of the planning and control system of an autonomous All-Terrain Mobile Robot and the implementation of a real-time path planner.

Abstract This paper proposes a novel Rapidly exploring random tree algorithm on rough terrains (RRT-RT) for the purpose of outdoor mobile robot navigation. Differently from other RRTs adopted for rough terrains where finding a nearest neighbor from a new random state within the tree is based on Euclidian distance, the proposed algorithm uses a roughness based metric. The metric is defined by the help of the roughness based navigation function, RbNF, that represents an estimate of the roughness-to-go value from each terrain location to the goal position. Simulation study shows that the RRT-RT provides an effective way to explore more promising terrain regions in order to decrease the total roughness along the resulting path.

This technical note proposes a novel navigation planner for mobile robots based on an adapted version of passivity-based nonlinear model predictive control. The proposed framework extends the convergent dynamic window approach and can be considered a generalized navigation planning technique able to include the high complex models required to describe the dynamics of vehicles moving outdoor on rough terrains. Several case studies are discussed to illustrate the usage of the framework.

This paper presents a novel navigation and motion planning algorithm for mobile vehicles in rough terrains. The main purpose of the algorithm is to generate feasible trajectories while selecting smoother paths, in the sense of level of roughness, toward the goal position. The purpose is achieved by adapting the passivity-based model predictive control optimization setup (PB/MPC), recently proposed for flat terrains, to the case of an outdoor irregular terrain. The passivity-based concept is used to enhance MPC in order to stabilize the goal position guaranteeing the task completion. The framework which is obtained can exploit any vehicle model in order to carefully take into account the vehicle dynamics and terrain structure as well as the wheel-terrain interaction. The inherited property of the MPC optimization allows to impose any additional constraint into the PB/MPC navigation, such as those needed to prevent vehicle rollover and unnecessary sideslip. The cost function representing the level of roughness along a candidate path is used to select the appropriate terrain areas toward the goal position. The results have been verified by several simulation examples.

This paper presents a novel mobile vehicle navigation algorithm based on the stability analysis of the model predictive control approach. The energy-shaping technique is performed with the navigation function to obtain a new virtual vehicle model that generates candidate feasible trajectories for the motion planner. Stability of the nonlinear model predictive control system is obtained by the passivity concept providing a guaranteed task completion. The proposed approach is adapted for a unicycle mobile vehicle but the work suggests that the passivity-based nonlinear model predictive control concept can be adapted for the navigation purposes for a broad range of mobile vehicle models.