On motion planning of mobile robots which coexist and cooperate with human

Tadokoro, Satoshı; Hayashi, Mitsunori; Manabe, Yasuhiro; Nakami, Yoshihiro; Takamori, Toshi

doi:10.1109/iros.1995.526265

Cited by 38 publications

(29 citation statements)

References 11 publications

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“…Robot motion planning in dynamic environments has recently received substantial attention due to the DARPA Urban Challenge [20] and growing interest in service and assistive robots (e.g., [47,51]). …”

Section: Literature Reviewmentioning

confidence: 99%

Robotic motion planning in dynamic, cluttered, uncertain environments

Toit

Burdick

2010

2010 IEEE International Conference on Robotics and Automation

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This thesis is concerned with robot motion planning in dynamic, cluttered, and uncertain environments. Successful and efficient robot operation in such environments requires reasoning about the future system evolution and the uncertainty associated with obstacles and moving agents in the environment. Current motion planning strategies ignore future information and are limited by the resulting growth of uncertainty as the system is evolved. This thesis presents an approach that accounts for future information gathering (and the quality of that information) in the planning process.The Partially Closed-Loop Receding Horizon Control approach, introduced in this thesis, is based on Dynamic Programming with imperfect state information. Probabilistic collision constraints, due to the need for obstacle avoidance between the robot and obstacles with uncertain locations and geometries, are developed and imposed. By accounting for the anticipated future information, the uncertainty associated with the system evolution is managed, allowing for greater numbers of moving agents and more complex agent behaviors to be handled. Simulation results demonstrate the benefit of the proposed approach over existing approaches in static and dynamic environments. Complex agent behaviors, including multimodal and interactive agent-robot models, are considered. vi

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Section: Literature Reviewmentioning

confidence: 99%

Robotic motion planning in dynamic, cluttered, uncertain environments

Toit

Burdick

2010

2010 IEEE International Conference on Robotics and Automation

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“…Tadokoro et al examine robot motion in the presence of humans [25] using a probabilistic prediction model of human motion. The goals of this work was collision avoidance and efficiency, but social rules and comfort are not taken into account.…”

Section: Human-aware Navigationmentioning

confidence: 99%

Plan-Based Control of Joint Human-Robot Activities

et al. 2010

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Cognition in technical systems is especially relevant for the interaction with humans. We present a newly emerging application for autonomous robots: companion robots that are not merely machines performing tasks for humans, but assistants that achieve joint goals with humans. This collaborative aspect entails specific challenges for AI and robotics. In this article, we describe several planning and action-related problems for human-robot collaboration and point out the challenges to implement cognitive robot assistants. MotivationResearch on intelligent autonomous robots has mainly focused on machines that perform certain services for humans, such as robot butlers or autonomous cars [7,16,24,26]. The interaction in such systems is restricted to a user interface to give a task to the robot and when acting, such a robot has to ensure the safety of humans in its vicinity.A new class of robot applications takes a different view: a robot assistant performs tasks together with a human in order to achieve a joint goal. This approach combines the cognitive capabilities and manual dexterity of humans with the strength, endurance and accuracy of robots. Rosenthal et al. [20] use the term "symbiotic relationship" to describe the reciprocal complementation of skills.On the one hand, the combination of human and robot skills moves useful robot applications closer to the state of marketable products. Whereas a robot butler would be expected to prepare a cup of tea all by itself, a robot assistant might only be expected to bring the kettle and the tea for a person to prepare the tea herself. On the other hand, the close interaction and collaboration with humans requires a range of new cognitive capabilities for robots in order to coordinate the human's and robot's actions in a way that feels natural to people. This paper is concerned with the action side of humanrobot collaboration. This includes AI techniques for planning and plan execution as well as specific action primitives for navigation and joint manipulation tasks. Application ScenariosTo illustrate the idea of assistant robots, we present two application scenarios where a collaboration of a human and a robot can be of substantial help, even if the robot has restricted capabilities.

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“…Furthermore, there has been developed a variety of techniques for tracking persons, for predicting future poses of persons, or for adopting the behavior of the robot according to the information obtained about the persons in its vicinity [29,19,34,20,5,17,18,16,32,4,2,28]. All these approaches, however, do not filter the measurements corresponding to persons in order to improve the model of the environment.…”

Section: Related Workmentioning

confidence: 99%

Mobile robot mapping in populated environments

Hähnel¹,

Schulz²,

Burgard³

2003

Advanced Robotics

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-The problem of learning maps with mobile robots has received considerable attention over the past years. Most of the approaches, however, assume that the environment is static during the data-acquisition phase. In this paper we consider the problem of creating maps with mobile robots in populated environments. Our approach uses a probabilistic method to track multiple people and to incorporate the estimates of the tracking technique into the mapping process. The resulting maps are more accurate since the number of spurious objects is reduced and since the robustness of range registration is improved. Our approach has been implemented and tested on real robots in indoor and outdoor scenarios. We present several experiments illustrating the capabilities of our approach to generate accurate 2d and 3d maps.

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On motion planning of mobile robots which coexist and cooperate with human

Cited by 38 publications

References 11 publications

Robotic motion planning in dynamic, cluttered, uncertain environments

Robotic motion planning in dynamic, cluttered, uncertain environments

Plan-Based Control of Joint Human-Robot Activities

Mobile robot mapping in populated environments

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