Hiroto Sakahara

Real-time motion planning in unknown environment: Voronoi-based StRRT (Spatiotemporal RRT)

For a robot to move autonomously in an unknown dynamic environment, a real-tune motion planning is necessary. When the motions of obstacles are unknown, motion planning is usually performed by virtually expanding the area obstacles occupy. However, there are many cases in which the adequate solution trajectory cannot be obtained due to inadequate expansion. Another approach would be to determine candidates of the safe trajectory from the layout of obstacles as a generalized Voronoi diagram and then randomly search for an appropriate trajectory. However, this requires many calculations. It may be sufficient if a local generalized Voronoi diagram is available for limiting the sampling area. Based on this idea, this paper proposes a Voronoi-based StRRT composed of an StRRT subjected to biasing extraction of sample points toward the border of a generalized Voronoi diagram and show the possibilities of this method to plan safe trajectories.

Safe Navigation in Unknown Dynamic Environments with Voronoi Based StRRT

For a robot to move autonomously in an unknown dynamic environment, a real-time motion generation is necessary. Especially, safety motion generations are important. There are a lot of methods for making safe motion. For example, it is popular to check only around the robot with additional sensors and avoid obstacles detected by the sensors, though, the coordination problem between global trajectory making and local obstacle avoidance should be accrued in this way. It is also popular to estimate the size of obstacles to be large, though adequate width of the dilatation is different by environment. Another approach would be to determine candidates of the safe trajectory from the layout of obstacles as a generalized Voronoi diagram and then randomly search for an appropriate trajectory. This approach can acutualize the global trajectory making and local obstacle avoidance at the same time. However, it may be sufficient if a local generalized Voronoi diagram is available for limiting the sampling area. Based on this idea, this paper explains Voronoi-based StRRT composed of an StRRT subjected to biasing extraction of sample points toward the border of a generalized Voronoi diagram and proposes the efficient method of applying StRRT to the omnidirectional mobile robots. Finally, it shows the possibilities of this method for practical situations.

Navigation control for tracking and catching a moving target

This paper presents feedback control laws for pursuing and catching a fly ball by taking Chapmans hypothesis into the closed-loop system connecting perceptions and actions. Through the analysis of the closed-loop system, we make it clear that the hypothetical trajectory Chapman showed is a special dynamic solution of the closed-loop system. Moreover, using a motion-analyzing technique over a finite time, it is shown that the proposed feedback control laws make it possible to generate a pursuing trajectory automatically that a fly ball can be caught in the right place and at the right time. It is also shown that the pursuing trajectory gets closer to the one Chapman showed as a feedback gain increases. In addition, we compare the proposed feedback control laws with Proportional Navigation (PN) which is the most common navigation technique for tracking a moving target, and demonstrate that the proposed control laws perform better than PN.

Motion Planning for producing a give-way behavior using Satiotemporal RRT

It is expected that more and more robots will be introduced into public societies and homes. The problem in such a case will be the smooth mobility of each robot. Collision avoidance between robots or between a robot and other obstacles including human beings is important as well as reaching its target point in order to carry out its task. Human beings often give way to each other to cope with this collision avoidance problem in a decentralized manner in which they plan and move individually without mutually exchanging information. This paper demonstrates that the robot can produce giving way to each other behavior similar to human beings based on a decentralized approach. Spatiotemporal RRT, which is an extended version of rapidly exploring random trees (RRT) that is a random sampling method for searching a large working space effectively, is used in order to produce this behavior. This paper also presents three typical types of environments that require giving way to each other and explains the result of numerical simulation with the proposed method.