This paper presents the first motion planning methodology applicable to articulated, nonpoint nonholonomic robots with guaranteed collision avoidance and convergence properties. It is based on a new class of nonsmooth Lyapunov functions and a novel extension of the navigation function method to account for nonpoint articulated robots. The dipolar inverse Lyapunov functions introduced are appropriate for nonholonomic control and offer superior performance characteristics compared to existing tools. The new potential field technique uses diffeomorphic transformations and exploits the resulting point-world topology. The combined approach is applied to the problem of handling deformable material by multiple nonholonomic mobile manipulators in an obstacle environment to yield a centralized coordinating control law. Simulation results verify asymptotic convergence of the robots, obstacle avoidance, boundedness of object deformations, and singularity avoidance for the manipulators.

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Nonholonomic navigation and control of cooperating mobile manipulators

Visual navigation and obstacle avoidance using a steering potential function

Recent advances in communication, computation, and embedded technologies support the development of cooperative multivehicle systems. For the purposes of this article, we adopt the following definition of cooperative behavior: "Given some task specified by a designer, a multiple-robot system displays cooperative behavior if, due to some underlying mechanism, for instance, the 'mechanism of cooperation,' there is an increase in the total utility of the system." The development of cooperative multivehicle systems is motivated by the recognition that, by distributing computer power and other resources, teams of mobile agents can perform many tasks more efficiently and robustly than an individual robot. For example, teams of robots can complete tasks such as multipoint surveillance, distributed localization and mapping, and cooperative transport. To facilitate the development of cooperative control systems and mobile sensor networks, we have created a cooperative multivehicle testbed (COMET) for research and experimentation.

Decentralized cooperative control - A multivehicle platform for research in networked embedded systems

Time-optimal, collision-free navigation of a car-like mobile robot using neuro-fuzzy approaches

This paper suggests a new way for nonholonomic mobile robots to navigate in obstacle environments using potential fields based on navigation functions. The proposed strategy is a time-invariant feedback control design with the distinguishing feature that it requires almost no switching compared to alternative methodologies of the same nature. Asymptotic convergence with collision avoidance for the proposed approach is established analytically, and the method is demonstrated on a differential-drive skid steering mobile robot.

Hybrid Potential Field Based Control of Differential Drive Mobile Robots

We treat the problem of navigation of a wheeled nonholonomic vehicle in a cluttered environment by: (i) finding, at every instant, a new collision free reference position using potential fields, and (ii) tracking this using nonholonomic tracking. Thus, our feedback solution can be applied in real time and guarantees both convergence to a final position and collision avoidance. The issue of final orientation accommodation is resolved for a number of practical configurations by using a fictitious obstacle around the goal state.

Navigation of nonholonomic vehicles in complex environments with potential fields and tracking

The problem of motion planing of a wheeled nonholonic vehicle is treated by decomposing the problem to the subproblems: 1) find a collision free path; and 2) approximate this path with a nonholonomic collision free path. This is treated, in real time, by solving the first subproblem using a potential fields strategy and the second one with nonholonomic tracking. Thus, collision avoidance of a nonholonomic wheeled vehicle in a feedback formulation is achieved.

P. Kakambouras

Papers

Navigation of nonholonomic vehicles in complex environments with potential fields and tracking

Potential fields for nonholonomic vehicles