Conv1D Energy-Aware Path Planner for Mobile Robots in Unstructured Environments

Deep Meta-Learning Energy-Aware Path Planner for Unmanned Ground Vehicles in Unknown Terrains

“…State lattice is a search graph where vertices representing kinematic states of the vehicle are connected by edges representing trajectories that satisfy its kinematic constraints. In this way, planning and cost estimation can be achieved directly over feasible trajectories, thereby considering the actual vehicle mobility constraints [17]. In our application, we define a set of 5 elementary trajectories 2.7 m long, and with curvature uniformly spaced in [−0.13, 0.13] m −1 , according to the mobility capability of our vehicle (see Fig.…”

“…We compare our method with alternative state-of-theart graph search heuristic methods used in driving energy optimization problems [15], [17] [18] (Section VII). In this way, we demonstrate the potential benefit of our approach, when navigating over unknown terrains, to provide more informed estimations and more energy-efficient paths than a non-adaptive energy predictor (Section VII-A) and to reduce the computational time of planning while retaining close-tooptimal solutions compared with a non-adaptive admissible heuristic function (Section VII-B).…”

“…Within this framework, terrain classifiers can be used to identify the type of terrain, while sensors such as LIDAR or stereo vision provide the geometric information. Hence, different energy models can be adopted, such as semi-empirical functions [19]- [21], look-up tables [22], or neural networks [15], [17] to link the terrain geometry to the energy consumption for each terrain type. The main drawback of these methods is that they cannot account for terrains with unknown properties.…”

“…However, their method does not address the problem of energy consumption prediction and its integration into a planning optimization framework. Some recent works have proposed the use of neural networks to model complex wheel-terrain interactions, their effect on energy consumption, as well as their integration into an energy-aware path planning algorithm [15], [17]. However, in those works prediction and planning are performed on a single terrain type with known terramechanical properties, while possible generalization and adaptation are not considered.…”

“…The A* graph search optimization is used in the state-lattice space [42]. This choice is motivated by the simplicity and effectiveness of A* to optimize paths according to complex cost functions, making it well-suited to the mobile robots energy-aware path planning [17], [19]. Moreover, A*-like graph search methods have the advantage to guarantee bounded path optimality, and to reduce the computational cost of planning by making use of heuristics [43].…”