This paper presents a planning system based on semantic reasoning for a general-purpose service robot, which is aimed at behaving more intelligently in domains that contain incomplete information, under-specified goals, and dynamic changes. First, Two kinds of data are generated by Natural Language Processing module from the speech: (i) action frames and their relationships; (ii) the modifier used to indicate some property or characteristic of a variable in the action frame. Next, the task’s goals are generated from these action frames and modifiers. These goals are represented as AI symbols, combining world state and domain knowledge, which are used to generate plans by an Answer Set Programming solver. Finally, the plan’s actions are executed one by one, and continuous sensing grounds useful information, which makes the robot use contingent knowledge to adapt to dynamic changes and faults. For each action in the plan, the planner gets its preconditions and effects from domain knowledge, so during the execution of the task, the environmental changes, especially those conflict with the actions, not only the action being performed but also the subsequent actions, can be detected and handled as early as possible. A series of case studies are used to evaluate the system and verify its ability to acquire knowledge through dialogue with users, solve problems with the acquired causal knowledge, and plan for complex tasks autonomously in the open world.
Robotic 3D bin packing (R‐3dBPP), aiming to place deformed cases in various sizes in the container without fences, is a comprehensive application that includes perception, planning, execution, and hardware design. Traditional studies assume that the context of the real space must be accurately perceived and represented. However, disjunctions between the planner and reality are unavoidable in R‐3dBPP, especially with low‐cost sensors. As far as the author knows, there is no practical solution. In this paper, the above assumption is discarded and the typical types of uncertainties prevalent to guide the design of the algorithms are formulated. A new online bin‐packing algorithm is proposed, keeping deformed boxes stacked in close contact with each other, so that the whole pallet is kept stable by friction between the boxes and the container. In order to meet the requirement of close contact under the non‐negligible errors from sensors and planners, a compliant‐based motion planning system is introduced. It replaces the precision‐based feedback with a compliant end‐effector and accompanying motion strategies. Last but not least, a complete online bin‐packing robot system is developed and the system's performance under the influence of the uncertainties mentioned above through simulation and physical experiments is evaluated.
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