A Deformable Configuration Planning Framework for a Parallel Wheel-Legged Robot Equipped with Lidar

Abstract

The wheel-legged hybrid robot (WLHR) is capable of adapting height and wheelbase configuration to traverse obstacles or rolling in confined space. Compared with legged and wheeled machines, it can be applied for more challenging mobile robotic exercises using the enhanced environment adapting performance. To make full use of the deformability and traversability of WHLR with parallel Stewart mechanism, this paper presents an optimization-driven planning framework for WHLR with parallel Stewart mechanism by abstracting the robot as a deformable bounding box. It will improve the obstacle negotiation ability of the high degree-of-freedoms robot, resulting in a shorter path through adjusting wheelbase of support polygon or trunk height instead of using a fixed configuration for wheeled robots. In the planning framework, we firstly proposed a pre-calculated signed distance field (SDF) mapping method based on point cloud data collected from a lidar sensor and a KD -tree-based point cloud fusion approach. Then, a covariant gradient optimization method is presented, which generates smooth, deformable-configuration, as well as collision-free trajectories in confined narrow spaces. Finally, with the user-defined driving velocity and position as motion inputs, obstacle-avoidancing actions including expanding or shrinking foothold polygon and lifting trunk were effectively testified in realistic conditions, demonstrating the practicability of our methodology. We analyzed the success rate of proposed framework in four different terrain scenarios through deforming configuration rather than bypassing obstacles.

Reconfigurability in Automobiles—Structure, Manufacturing and Algorithm for Automobiles

“…When switched to the legged form, it had a high terrain adaptability. The amphibious automobile of similar structure may move on uneven mountain roads or soft swamps smoothly [20][21][22], and therefore, it can assist humans in field exploration [23], rescue and disaster relief [24,25]. However, the legged form of automobile moves slowly [20,23].…”

“…The amphibious automobile of similar structure may move on uneven mountain roads or soft swamps smoothly [20][21][22], and therefore, it can assist humans in field exploration [23], rescue and disaster relief [24,25]. However, the legged form of automobile moves slowly [20,23]. Furthermore, it is difficult for the users to manipulate [21,24], and the overall motion stability is poor [20,25].…”

“…So far, there are sensing [19,23,55,[81][82][83], interactive [20][21][22]84,85], autonomous [25,83,86,87] and other types of reconfigurable automobiles and robots. However, there is still much room for improvement of intelligent reconfigurable factory to be capable of interacting with humans, "thinking" by themselves, and being completely unmanned.…”

A Dynamic Motion Analysis of a Six-Wheel Ground Vehicle for Emergency Intervention Actions

“…The global method needs a completely known environment and certain simplifying methods; for the local method, the algorithm allows some real-time adjustments to the path to be followed. Planning implies optimization procedures of the time (and velocities) to select the geometrical paths in real-time to avoid obstacles [ 18 , 19 , 20 , 21 ]. Since every obstacle creates a risk level for the UGV, introducing proportional integrative derivative (PID) controllers and fuzzy logic methods, which classify the objects around the vehicle based on their level of risk, allows generating some predictions regarding the capacity to avoid fixed or moving obstacles (the velocity obstacle (VO) approach), which means that, virtually, a space that defines the respective object should be generated [ 22 , 23 , 24 ].…”

Suboptimal Omnidirectional Wheel Design and Implementation

“…On the one hand, the automatic development of such cooperative tasks requires the use of human compatible devices in order to avoid collisions or actions that can harm people, mainly using laser range sensors [ 1 , 2 ], depth cameras [ 3 , 4 ], or surveillance systems [ 5 ]. On the other hand, the automatic development of cooperative tasks requires the use of human compatible devices moving like people in common spaces designed for people, mainly using legs [ 6 ], omnidirectional wheels [ 7 ], or hybrid legs [ 8 , 9 ].…”