Path planning in the hippocampo-prefrontal cortex pathway: An adaptive model based receding horizon planner

Ahmadi-Pajouh, Mohammad Ali; Towhidkhah, Farzad; Gharibzadeh, Shahriar; Mashhadimalek, Maysam

doi:10.1016/j.mehy.2006.06.060

Cited by 20 publications

(18 citation statements)

References 11 publications

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“…4 It seems natural to achieve collision avoidance using some type of planning into the future, and MPC-based navigation laws are discussed in Section 3.…”

Section: Biological Inspirationmentioning

confidence: 99%

“…The rule for switching between (7) and (3) employs two given parameters 4 > 0 and C > d 0 + , where C is the distance to an obstacle at which boundary following is commenced; its termination is allowed only if the vehicle is close enough to the obstacle: d i ≤ d 0 + . Specifically, the rule for switching between (7) and (3) To exclude the situation where the rule R1 becomes active simultaneously for several obstacles, the parameter C should be less than half the minimal spacing between the obstacles.…”

Section: Sliding Mode Controlmentioning

confidence: 99%

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Algorithms for collision-free navigation of mobile robots in complex cluttered environments: a survey

2014

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SUMMARYWe review a range of techniques related to navigation of unmanned vehicles through unknown environments with obstacles, especially those that rigorously ensure collision avoidance (given certain assumptions about the system). This topic continues to be an active area of research, and we highlight some directions in which available approaches may be improved. The paper discusses models of the sensors and vehicle kinematics, assumptions about the environment, and performance criteria. Methods applicable to stationary obstacles, moving obstacles and multiple vehicles scenarios are all reviewed. In preference to global approaches based on full knowledge of the environment, particular attention is given to reactive methods based on local sensory data, with a special focus on recently proposed navigation laws based on model predictive and sliding mode control.

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“…4 It seems natural to achieve collision avoidance using some type of planning into the future, and MPC-based navigation laws are discussed in Section 3.…”

Section: Biological Inspirationmentioning

confidence: 99%

Section: Sliding Mode Controlmentioning

confidence: 99%

Algorithms for collision-free navigation of mobile robots in complex cluttered environments: a survey

2014

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“…However, these methods are not able to reproduce the exact strategy employed by humans, if unpredicted disturbances occur [4, 10, 15]. Therefore, different approaches are found in literature that are applicable as corrective measures: constant re-planning [16, 17], re-planning at specific states [18, 19], integration of intermediate goals [4], or reactive approaches without prior planning [20]. With these approaches, the observed trajectories are replicable but the underlying human locomotion behavior remains unclear.…”

Section: Introductionmentioning

confidence: 99%

“…In order to tackle this problem, we must explore whether humans employ different behaviors within fully observable and partially observable environments. In this work, we follow the idea that humans employ a shorter planning horizon in complex or uncertain scenarios [16]. Thereby, it is not known how much complexity humans can handle before they start to adapt their planning.…”

Section: Introductionmentioning

confidence: 99%

“…The subjects’ movements are additionally disturbed by other virtual objects to trigger the anticipated avoidance behaviors. Changing the observability and therefore raising the uncertainty is accomplished by altering the complexity of the virtual environment based on the number of obstacles [16]. This experimental design is expected to reveal information about the planning horizon applied by humans and the accepted deviation of trajectories from distinct optimality criteria.…”

Section: Introductionmentioning

confidence: 99%

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Towards Assessing the Human Trajectory Planning Horizon

et al. 2016

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Mobile robots are envisioned to cooperate closely with humans and to integrate seamlessly into a shared environment. For locomotion, these environments resemble traversable areas which are shared between multiple agents like humans and robots. The seamless integration of mobile robots into these environments requires accurate predictions of human locomotion. This work considers optimal control and model predictive control approaches for accurate trajectory prediction and proposes to integrate aspects of human behavior to improve their performance. Recently developed models are not able to reproduce accurately trajectories that result from sudden avoidance maneuvers. Particularly, the human locomotion behavior when handling disturbances from other agents poses a problem. The goal of this work is to investigate whether humans alter their trajectory planning horizon, in order to resolve abruptly emerging collision situations. By modeling humans as model predictive controllers, the influence of the planning horizon is investigated in simulations. Based on these results, an experiment is designed to identify, whether humans initiate a change in their locomotion planning behavior while moving in a complex environment. The results support the hypothesis, that humans employ a shorter planning horizon to avoid collisions that are triggered by unexpected disturbances. Observations presented in this work are expected to further improve the generalizability and accuracy of prediction methods based on dynamic models.

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Hierarchic Interactive Path Planning in Virtual Reality

Cailhol

Fillatreau

Zhao

et al. 2015

Informatics in Control, Automation and Robotics

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To save time and money while designing new products, industry needs tools to design, test and validate the product using virtual prototypes. These virtual prototypes must enable to test the product at all Product Life-cycle Management (PLM) stages. Many operations in PLM involve human manipulation of product components in cluttered environment (product assembly, disassembly or maintenance). Virtual Reality (VR) enables real operators to perform these tests with virtual prototypes. This work introduces a novel path planning architecture allowing collaboration between a VR user and an automatic path planning system. It is based on an original environment model including semantic, topological and geometric information, and an automatic path planning process split in two phases: coarse (semantic and topological information) and fine (semantic and geometric information) planning. The collaboration between VR user and automatic path planner is made of 3 main aspects. First, the VR user is guided along a precomputed path through a haptic device whereas he VR user can go away from the proposed path to explore possible better ways. Second the authority of automatic planning system is balanced to let the user free to explore alternatives (geometric layer). Third the intents of VR user are predicted (on topological layer) to be integrated in the re-planning process. Experiments are provided to illustrate the multi-layer representation of the environment, the path planning process, the control sharing and the intent prediction.

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Path planning in the hippocampo-prefrontal cortex pathway: An adaptive model based receding horizon planner

Cited by 20 publications

References 11 publications

Algorithms for collision-free navigation of mobile robots in complex cluttered environments: a survey

Algorithms for collision-free navigation of mobile robots in complex cluttered environments: a survey

Towards Assessing the Human Trajectory Planning Horizon

Hierarchic Interactive Path Planning in Virtual Reality

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