Evolutionary action maps for navigating a robot in an unknown, multidimensional, stationary environment. II. Implementation and results

Masoud, Ahmad A.; Masoud, S.A.

doi:10.1109/robot.1997.619271

Cited by 24 publications

(20 citation statements)

References 12 publications

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“…a hybrid, PDE-ODE controller (HPC) to convert the acquired data into in-formation that is encoded in the structure of the micro-control action group. For more details about this type of control see (Masoud & Masoud, 2000a;Masoud & Masoud,1998;Masoud & Masoud, 1997;Masoud & Masoud,1994;Masoud & Masoud, 2000b;Masoud & Masoud, 2002). …”

Section: Fig 9 a Dense Matrix Of Interacting Virtual Agentsmentioning

confidence: 99%

Harmonic Potential Fields: an Effective Tool for Generating a Self-Organizing Behavior

Masoud¹

2011

Self Organizing Maps - Applications and Novel Algorithm Design

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Section: Fig 9 a Dense Matrix Of Interacting Virtual Agentsmentioning

confidence: 99%

Harmonic Potential Fields: an Effective Tool for Generating a Self-Organizing Behavior

Masoud¹

2011

Self Organizing Maps - Applications and Novel Algorithm Design

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“…It can be used to guide the motion of an arbitrarily shaped agent in an unknown environment regardless of its geometry or even topology relying only on the sensory data acquired online by the agent's finite range sensors. The method can also impose a variety of constraints on the agent's trajectory such as regional avoidance and directional constraints [3][4][5][6][7][8]. Harmonic functions are also Morse functions and a general form of the navigation functions suggested in [13], see appendix.…”

Section: Introductionmentioning

confidence: 99%

“…Planners assuming a PDE-ODE setting other than that of the one in (3) may be found in [3,7,8]. The trajectory, x(t), generated by the dynamical system in (4) is only a reference trajectory that should be fed to an LLC in order to generate the control signal, u, the robot is using.…”

Section: Introductionmentioning

confidence: 99%

Kinodynamic Motion Planning

Masoud

2010

IEEE Robot. Automat. Mag.

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Abstract-This paper extends the capabilities of the harmonic potential field approach to planning to cover both the kinematic and dynamic aspects of a robot's motion. The suggested approach converts the gradient guidance field from a harmonic potential to a control signal by augmenting it with a novel type of dampening forces suggested in this paper called: nonlinear, anisotropic, dampening forces (NADFs). The combination of the two provides a signal that can both guide a robot and effectively manage its dynamics. The kinodynamic planning signal inherits, fully, the guidance capabilities of the harmonic gradient field. It can also be easily configured to efficiently suppress the inertia-induced transients in the robot's trajectory without compromising the speed of operation. The approach works with dissipative systems as well as systems acted on by external forces without needing full knowledge of the system's dynamics. Theoretical developments and simulation results are provided in the paper.

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“…Harmonic planners are a subset of a more general class of planners that are called Hybrid PDE-ODE motion planners ( Figure-1). When set to operate in the context of a Hybrid Discrete-time Continuous-time system this class of planners transforms into an interesting and powerful class of motion planners that is called Evolutionary, Hybrid, PDE-ODE motion planners [26,27] (Figure-2). Evolutionary, Hybrid, PDE-ODE planners are situated, embodied, intelligent, and emergent mechanisms for behavior generation.…”

Section: Introductionmentioning

confidence: 99%

Integrating directional constraints in motion planning using nonlinear, anisotropic, harmonic potential fields

Masoud

Proceedings of the 1998 IEEE International Symposium on Intelligent Control (ISIC) Held Jointly With IEEE International Symposi

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This paper addresses an important problem that has remained, to the best of the author's knowledge, neglected in the motion planning literature. That is the incorporation of directional constraints along with obstacle avoidance in steering a robot to a target zone. The available motion planners deal exclusively with isotropic workspaces that place no restrictions on the direction of motion in their interior. Unfortunately, realistic workspaces do not support the above behavior. Instead, they impose a priori known restrictions on the direction of motion inside a workspace (e.g., ENTER and EXIT signs, direction of traffic along a one-way street). In this paper Harmonic field motion planners which can only deal with isotropic workspaces are generalized to incorporate anisotropic workspaces with preimposed directional constraints. Theoretical developments along with simulation results are provided.

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Evolutionary action maps for navigating a robot in an unknown, multidimensional, stationary environment. II. Implementation and results

Cited by 24 publications

References 12 publications

Harmonic Potential Fields: an Effective Tool for Generating a Self-Organizing Behavior

Harmonic Potential Fields: an Effective Tool for Generating a Self-Organizing Behavior

Kinodynamic Motion Planning

Integrating directional constraints in motion planning using nonlinear, anisotropic, harmonic potential fields

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