Abstract
Hopping robots, called hoppers, are expected to move on rough terrains, such as disaster areas or planetary environments. The uncertainties of the hopping locomotion in such environments are high, making path planning algorithms essential to traverse these uncertain environments. Planetary surface exploration requires to generate a path which minimises the risk of failure and maximises the information around the hopper. This paper newly proposes a hopping path planning algorithm for rough terrains locomotion. The proposed algorithm takes into account the motion uncertainties using Markov decision processes (MDPs), and generates paths corresponding to the terrain conditions, or the mission requirements, or both. The simulation results show the effectiveness of the proposed route planning scheme in three cases as the rough terrain, sandy and hard ground environment, and non-smooth borders.
Highlights
In recent years, hopping robots have received a lot of attention, because they are expected to play an active role in disaster areas [1], celestial bodies [2,3,4,5], etc
The contribution of this paper is to propose a hopping path planning algorithm to traverse uncertain environments
If a robot fail to follow the path, you need to re-planning a path. This is because we think rapidly exploring random trees (RRTs)-based path planning algorithms are not suited for the uncertain environments
Summary
In recent years, hopping robots (hoppers) have received a lot of attention, because they are expected to play an active role in disaster areas [1], celestial bodies [2,3,4,5], etc. The contribution of this paper is to propose a hopping path planning algorithm to traverse uncertain environments. PRM uses a graph search algorithm to generate the path. If a robot fail to follow the path, you need to re-planning a path This is because we think RRT-based path planning algorithms are not suited for the uncertain environments. Model based algorithms Unlike urban environments, locomotion on natural terrains includes a lot of uncertainties because of the interactions with the terrain surface. Terramechanics is the field of the terrain-mobility interactions and the application for path planning of wheeled robots has been studied in [19]. Our approach can generate paths corresponding to the requirements in each mission sequence, and calculate the optimal action in all states (details in section ). The paths are not adaptive and do not include uncertainty
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