Hybrid Potential Field Based Control of Differential Drive Mobile Robots

Valbuena, Luis; Tanner, Herbert G.

doi:10.1007/s10846-012-9685-6

Cited by 43 publications

(14 citation statements)

References 41 publications

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“…The combination of these aspects results in categorizing obstacle avoidance schemes into local path (sensor-based), or global path methods [14]. Local-path methods such as artificial potential field (APF) [15], or model predictive control (MPC) [16], use the knowledge of the immediate surroundings of the mobile robot to estimate the shape of obstacles and their location. Thus the planification of the alternative path is performed locally based on the sensors' readings.…”

Section: Proposed Schemementioning

confidence: 99%

Combining Hector SLAM and Artificial Potential Field for Autonomous Navigation Inside a Greenhouse

Harik

Korsæth

2018

Robotics

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Abstract:The key factor for autonomous navigation is efficient perception of the surroundings, while being able to move safely from an initial to a final point. We deal in this paper with a wheeled mobile robot working in a GPS-denied environment typical for a greenhouse. The Hector Simultaneous Localization and Mapping (SLAM) approach is used in order to estimate the robots' pose using a LIght Detection And Ranging (LIDAR) sensor. Waypoint following and obstacle avoidance are ensured by means of a new artificial potential field (APF) controller presented in this paper. The combination of the Hector SLAM and the APF controller allows the mobile robot to perform periodic tasks that require autonomous navigation between predefined waypoints. It also provides the mobile robot with a robustness to changing conditions that may occur inside the greenhouse, caused by the dynamic of plant development through the season. In this study, we show that the robot is safe to operate autonomously with a human presence, and that in contrast to classical odometry methods, no calibration is needed for repositioning the robot over repetitive runs. We include here both hardware and software descriptions, as well as simulation and experimental results.

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Section: Proposed Schemementioning

confidence: 99%

Combining Hector SLAM and Artificial Potential Field for Autonomous Navigation Inside a Greenhouse

Harik

Korsæth

2018

Robotics

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“…To mitigate the local converge to a local optimal, some additions to the potential field have been introduced. Valbuena and Tanner (2012) proposed the way of adding velocity constraints, meanwhile García-Delgado et al (2015) extended the repulsive function with the change of magnitude dependent on the angle between the attractive force and the obstacle. The main aim is to avoid the cancellation of the repulsive and attractive forces when applied in opposite orientations.…”

Section: Introductionmentioning

confidence: 99%

Toward Shared Working Space of Human and Robotic Agents Through Dipole Flow Field for Dependable Path Planning

2018

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Recent industrial developments in autonomous systems, or agents, which assume that humans and the agents share the same space or even work in close proximity, open for new challenges in robotics, especially in motion planning and control. In these settings, the control system should be able to provide these agents a reliable path following control when they are working in a group or in collaboration with one or several humans in complex and dynamic environments. In such scenarios, these agents are not only moving to reach their goals, i.e., locations, they are also aware of the movements of other entities to find a collision-free path. Thus, this paper proposes a dependable, i.e., safe, reliable and effective, path planning algorithm for a group of agents that share their working space with humans. Firstly, the method employs the Theta* algorithm to initialize the paths from a starting point to a goal for a set of agents. As Theta* algorithm is computationally heavy, it only reruns when there is a significant change of the environment. To deal with the movements of the agents, a static flow field along the configured path is defined. This field is used by the agents to navigate and reach their goals even if the planned trajectories are changed. Secondly, a dipole field is calculated to avoid the collision of agents with other agents and human subjects. In this approach, each agent is assumed to be a source of a magnetic dipole field in which the magnetic moment is aligned with the moving direction of the agent. The magnetic dipole-dipole interactions between these agents generate repulsive forces to help them to avoid collision. The effectiveness of the proposed approach has been evaluated with extensive simulations. The results show that the static flow field is able to drive agents to the goals with a small number of requirements to update the path of agents. Meanwhile, the dipole flow field plays an important role to prevent collisions. The combination of these two fields results in a safe path planning algorithm, with a deterministic outcome, to navigate agents to their desired goals.

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“…Unfortunately, the above-mentioned approaches cannot be applied directly to nonholonomic robots, since the gradient of navigation function is not guaranteed to be nonzero, except for trivial cases. For nonholonomic robots, one usually utilizes switching controls or combines several different control laws [13], [21], [22].…”

Section: Introductionmentioning

confidence: 99%

“…Our navigation algorithm includes a generation scheme of feasible control sequences that are then optimized according to the navigation function. Hence, our approach does not require switching controls or combination of several different control, as is the case in [13], [21], and [22]. To the best of our knowledge, this is the first single-control-law navigation algorithm for differential drive mobile robots in environments with obstacles (nonconvex constraint set) whose convergence is proved by applying the stability theory of discontinuous discrete-time nonlinear systems.…”

Section: Introductionmentioning

confidence: 99%

Receding Horizon Control for Convergent Navigation of a Differential Drive Mobile Robot

Seder

Baotić

Petrović

2017

IEEE Trans. Contr. Syst. Technol.

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A receding horizon control (RHC) algorithm for convergent navigation of a differential drive mobile robot is proposed. Its objective function utilizes a local-minima-free navigation function to measure the cost-to-goal over the robot trajectory. The navigation function is derived from the path-search algorithm over a discretized 2-D search space. The proposed RHC navigation algorithm includes a systematic procedure for the generation of feasible control sequences. The optimal value of the objective function is employed as a Lyapunov function to prove a finite-time convergence of the discrete-time nonlinear closed-loop system to the goal state. The developed RHC navigation algorithm inherits fast replanning capability from the D* search algorithm, which is experimentally verified in changing indoor environments. The performance of the developed RHC navigation algorithm is compared with the state-of-the-art sample-based motion planning algorithm based on lattice graphs, which is combined with a trajectory tracking controller. The RHC navigation algorithm produces faster motion to the goal with significantly lower computational costs and it does not need any controller tuning to cope with diverse obstacle configurations.

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Hybrid Potential Field Based Control of Differential Drive Mobile Robots

Cited by 43 publications

References 41 publications

Combining Hector SLAM and Artificial Potential Field for Autonomous Navigation Inside a Greenhouse

Combining Hector SLAM and Artificial Potential Field for Autonomous Navigation Inside a Greenhouse

Toward Shared Working Space of Human and Robotic Agents Through Dipole Flow Field for Dependable Path Planning

Receding Horizon Control for Convergent Navigation of a Differential Drive Mobile Robot

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