Stable path planning for reconfigurable robots over uneven terrains

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Autonomous mobile robots are required to find safe and feasible routes in the environment when operating over challenging terrains. The most influential tip-over stability measures are based on two criteria; the robot’s centre of mass (CM) and the support polygon defined by the convex area spanned between the ground contact points. The force angle (FA) stability margin is employed in this work given its widespread use and simple geometric interpretation. A method to compute the contact points between a tracked robot and rugged terrain and predict robot’s stability axes on 3D meshed maps reconstructed from 3D point clouds using the open dynamics engine (ODE) is presented. The validity and the need for stability computations based on the proposed contact points prediction algorithm is established through experiments over two common indoor obstacles i.e. ramps and stairs. An analytical strategy to generate stable paths for reconfigurable robots whilst also meeting additional navigational objectives is hereby proposed. The suggested solution looks at minimizing the length of the traversed path and the energy expenditure in changing postures, and also accounts for additional constraints in terms of sensor visibility and traction. A statistical analysis of stability prediction to account for the uncertainties associated with the actual robot’s dynamic model, its localisation in the ground, and the terrain models is introduced. Probability density function (PDF) of contact points, CM and the FA stability measure are numerically estimated, with simulation results performed on the ODE simulator based on uncertain parameters. Two techniques are presented: a conventional standard Monte Carlo (SMC) scheme, and a structured unscented transform (UT) which results in significant improvement in computational efficiency. A novel probabilistic stability criterion derived from the cumulative distribution of the FA margin is introduced that allows a safety constraint to be dynamically updated by available sensor data as it becomes available. The advantages of planning with probabilistic stability is demonstrated using a grid based A* algorithm as well as a sampling based RRT planner. The validity of the proposed approach is evaluated with a multi-tracked robot fitted with a manipulator arm and a range camera using two challenging 3D terrains data sets: one obtained whilst operating the robot in a mock-up urban search and rescue arena, and a second one from a publicly available on-line data from a quasi-outdoor rover testing facility.
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