A Human-Like Approach to Footstep Planning

Humanoid Robots, Human-like Machines 296 possible stepping locations that lead to an equal number of descendants, a tree is spread out from the initial footstep position.Now, after pruning from the tree those branches that do not end up with a step in the destination area, a two-leveled polygon-polygon collision search is conducted in order to identify trajectories that result in collision with the obstacles in the environment.Once these are discarded too, a greedy search is conducted in order to hunt out the best among the paths generated.This strategy cannot distinguish between the paths without obstacles and with some obstacles that can be stepped over due to which the robot always needs to execute high stepping profiles resulting in larger overall energy consumption.In addition, because of generation of a large search tree of possible stepping locations, greedy search has to be applied instead of an exhaustive one.On the other hand, a human in such a situation would start by searching for an obstacle-free path directly connecting initial and goal locations.If found, it would simply be opted.Whereas some options would be considered once an obstacle-free direct path is not found.Again, an obstacle can be dodged from the right, or dodged from the left, or is crossed by stepping over it.(Ayaz et al., 2006) starts by checking if it is possible for the robot to go directly from the initial to final locations.If so, such a path is adopted.If not, trajectories are formed in order to dodge the hindering obstacle from the right and the left.In addition, keeping in view the dimensional constraints, obstacles are classified into three types.The smallest ones that can be stepped over: type-1.For these an additional trajectory considering stepping over is also formed.The larger ones that cannot be stepped over but the robot can pass right next to them: type-2.And the largest ones that might collide with the robot's arms (sticking out wider than its feet) if the robot passes right next to them: type-3.The robot keeps a larger distance from obstacles of type-3 as compared to those of type-2 in order to avoid collision.In this way, the algorithm starts by considering the shortest path and then expands its alternates on either side spreading out considering the obstacle type and proximity.In addition, by identifying obstacles once encountered, it avoids getting stuck into the local loops and deadlocks.It is noticeable here that branches formed from every node in the search tree can be a maximum of 3, and for several straight line segments each node leads to only one child.This reduces the branching factor making a smallest possible search tree which in turn enables us to apply the exhaustive search for seeking the best possible path.It can be immediately noticed that this search, though exhaustive, is computationally cheap.This chapter explains our proposed method in detail.In section 2 we analyse the game theory based footstep planning strategy under reference.Section 3 introduces the concept of human-like footstep planning and analyses how it measures up to some existing approaches.Section 4 deals with planning for taking a single step.The footstep planner is explained in detail in section 5. Section 6 presents results of simulation of the proposed algorithm on a model of our humanoid robot 'Saika-3' (Konno et al., 2000) whereas section 7 concludes the chapter while highlighting salient future research prospects.

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