Time-Energy Optimal Cluster Space Motion Planning for Mobile Robot Formations

In this paper, a mobile robot named VicRoB with 6 degrees of freedom (DOFs) driven by three tracked vehicles is designed and analyzed. The robot employs a 3-PPSR parallel configuration. The scheme of the mechanism and the inverse kinematic solution are given. A path planning method of a single tracked vehicle and a coordinated motion planning of three tracked vehicles are proposed. The mechanical structure and the electrical architecture of VicRoB prototype are illustrated. VicRoB can achieve the point-to-point motion mode and the continuous motion mode with employing the motion planning method. The orientation precision of VicRoB is measured in a series of motion experiments, which verifies the feasibility of the motion planning method. This work provides a kinematic basis for the orientation closed loop control of VicRoB whether it works on flat or rough road.

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The PETLON Algorithm to Plan Efficiently for Task-Level-Optimal Navigation

Journal of Artificial Intelligence Research ◽

10.1613/jair.1.12181 ◽

2020 ◽

Vol 69 ◽

pp. 471-500

Author(s):

Shih-Yun Lo ◽

Shiqi Zhang ◽

Peter Stone

Keyword(s):

Mobile Robot ◽

Motion Planning ◽

Mobile Robots ◽

Large Scale ◽

Accurate Estimate ◽

Indoor Environments ◽

Task Planning ◽

Trade Offs ◽

High Level ◽

Task Level

Intelligent mobile robots have recently become able to operate autonomously in large-scale indoor environments for extended periods of time. In this process, mobile robots need the capabilities of both task and motion planning. Task planning in such environments involves sequencing the robot’s high-level goals and subgoals, and typically requires reasoning about the locations of people, rooms, and objects in the environment, and their interactions to achieve a goal. One of the prerequisites for optimal task planning that is often overlooked is having an accurate estimate of the actual distance (or time) a robot needs to navigate from one location to another. State-of-the-art motion planning algorithms, though often computationally complex, are designed exactly for this purpose of finding routes through constrained spaces. In this article, we focus on integrating task and motion planning (TMP) to achieve task-level-optimal planning for robot navigation while maintaining manageable computational efficiency. To this end, we introduce TMP algorithm PETLON (Planning Efficiently for Task-Level-Optimal Navigation), including two configurations with different trade-offs over computational expenses between task and motion planning, for everyday service tasks using a mobile robot. Experiments have been conducted both in simulation and on a mobile robot using object delivery tasks in an indoor office environment. The key observation from the results is that PETLON is more efficient than a baseline approach that pre-computes motion costs of all possible navigation actions, while still producing plans that are optimal at the task level. We provide results with two different task planning paradigms in the implementation of PETLON, and offer TMP practitioners guidelines for the selection of task planners from an engineering perspective.

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