Orientation-Aware Planning for Parallel Task Execution of Omni-Directional Mobile Robot
This work addresses the need for more efficient and flexible task execution in omni-directional mobile robots, particularly for applications like gimbals or sensors with limited fields of view, though it appears incremental as it builds on existing planning methods.
The paper tackled the problem of underutilizing the extra degree of freedom in omni-directional mobile robots by enabling simultaneous execution of orientation and position transition tasks, proposing an orientation-aware planning architecture that integrates these tasks into a single planning problem and introducing a modified trajectory optimization method (OATEB). Experiments in simulated and real environments demonstrated the method's capability to execute parallel tasks effectively, with a four-wheeled OMR successfully deployed in real-life scenarios.
Omni-directional mobile robot (OMR) systems have been very popular in academia and industry for their superb maneuverability and flexibility. Yet their potential has not been fully exploited, where the extra degree of freedom in OMR can potentially enable the robot to carry out extra tasks. For instance, gimbals or sensors on robots may suffer from a limited field of view or be constrained by the inherent mechanical design, which will require the chassis to be orientation-aware and respond in time. To solve this problem and further develop the OMR systems, in this paper, we categorize the tasks related to OMR chassis into orientation transition tasks and position transition tasks, where the two tasks can be carried out at the same time. By integrating the parallel task goals in a single planning problem, we proposed an orientation-aware planning architecture for OMR systems to execute the orientation transition and position transition in a unified and efficient way. A modified trajectory optimization method called orientation-aware timed-elastic-band (OATEB) is introduced to generate the trajectory that satisfies the requirements of both tasks. Experiments in both 2D simulated environments and real scenes are carried out. A four-wheeled OMR is deployed to conduct the real scene experiment and the results demonstrate that the proposed method is capable of simultaneously executing parallel tasks and is applicable to real-life scenarios.