scholarly journals Chemical Source Searching by Controlling a Wheeled Mobile Robot to Follow an Online Planned Route in Outdoor Field Environments

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 426 ◽  
Author(s):  
Ji-Gong Li ◽  
Meng-Li Cao ◽  
Qing-Hao Meng
Keyword(s):  
Mobile Robot ◽  
Field Experiments ◽  
Route Planning ◽  
Wheeled Mobile Robot ◽  
Time Varying ◽  
Chemical Plume ◽  
Average Success Rate ◽  
Historical Trajectory ◽  
Airflow Condition ◽  
Dynamic Route

In this paper, we present an estimation-based route planning (ERP) method for chemical source searching using a wheeled mobile robot and validate its effectiveness with outdoor field experiments. The ERP method plans a dynamic route for the robot to follow to search for a chemical source according to time-varying wind and an estimated chemical-patch path (C-PP), where C-PP is the historical trajectory of a chemical patch detected by the robot, and normally different from the chemical plume formed by the spatial distribution of all chemical patches previously released from the source. Owing to the limitations of normal gas sensors and actuation capability of ground mobile robots, it is quite hard for a single robot to directly trace the intermittent and rapidly swinging chemical plume resulting from the frequent and random changes of wind speed and direction in outdoor field environments. In these circumstances, tracking the C-PP originating from the chemical source back could help the robot approach the source. The proposed ERP method was tested in two different outdoor fields using a wheeled mobile robot. Experimental results indicate that the robot adapts to the time-varying airflow condition, arriving at the chemical source with an average success rate and approaching effectiveness of about 90% and 0.4~0.6, respectively.

The Optimized Point Stabilization Control for Robots Based on Bézier Planning

2012 ◽  
Vol 229-231 ◽  
pp. 2266-2269
Author(s):  
Yan Hong Du ◽  
Wei Yu Zhang ◽  
Xin Song ◽  
Yuan Liu ◽  
Ruo Kui Chang
Keyword(s):  
Mobile Robot ◽  
Route Planning ◽  
Penalty Functions ◽  
Wheeled Mobile Robot ◽  
Planning Method ◽  
Control Parameters ◽  
Constraint Condition ◽  
Constrained System ◽  
Stabilization Control ◽  
Point Stabilization

In this paper, the optimized point stabilization control of nonholonomic wheeled mobile robot has been researched, and point stabilization control of constraint nonholonomic wheeled system has been achieved through the geometry planning method based on Bézier, and constrained system is converted to un-constraint optimized question based on introducing penalty functions. The optimized control parameters has been got through Hooke-Jeeves method to achieve the perfect combination of the optimized route planning and optimized control, which can make the robot achieve the target pose under the constraint condition and improve the smooth of move path and reduce the stable time. The controller's validity is proved by the experiment.

A portable six-wheeled mobile robot with reconfigurable body and self-adaptable obstacle-climbing mechanisms

2022 ◽  
pp. 1-39
Author(s):  
Zhen Song ◽  
Zirong Luo ◽  
Guowu Wei ◽  
Jianzhong Shang
Keyword(s):  
Mobile Robot ◽  
Field Experiments ◽  
Mechanical Design ◽  
Wheeled Mobile Robot ◽  
Structure Parameters ◽  
Mechatronic Systems ◽  
Obstacle Crossing ◽  
Motion Stability ◽  
Physical Prototype ◽  
Static Conditions

Abstract Mobile robots can replace rescuers in rescue and detection missions in complex and unstructured environments and draw the interest of many researchers. This paper presents a novel six-wheeled mobile robot with a reconfigurable body and self-adaptable obstacle-climbing mechanisms, which can reconfigure itself to three locomotion states to realize the advantages of terrain adaptability, obstacle crossing ability and portability. Design criteria and mechanical design of the proposed mobile robot are firstly presented, based on which the geometry of the robot is modelled and the geometric constraint, static conditions and motion stability condition for obstacle crossing of the robot are derived and formulated. Numerical simulations are then conducted to verify the geometric passing capability, static passing capability and motion stability and find feasible structure parameters of the robot in obstacle crossing. Further, a physical prototype of the proposed mobile robot is developed and integrated with mechatronic systems and remote control. Using the prototype, field experiments are carried out to verify the feasibility of the proposed design and theoretical derivations. The results show that the proposed mobile robot satisfies all the criteria set and is feasible for applications in disastrous rescuing scenarios.

scholarly journals Stochastic Time-Varying Model Predictive Control for Trajectory Tracking of a Wheeled Mobile Robot

2021 ◽  
Vol 9 ◽  
Author(s):  
Weijiang Zheng ◽  
Bing Zhu
Keyword(s):  
Model Predictive Control ◽  
Mobile Robot ◽  
Predictive Control ◽  
Linear Time ◽  
Tracking Error ◽  
Wheeled Mobile Robot ◽  
Reference Trajectory ◽  
Time Varying ◽  
Probability Constraints ◽  
And Control

In this paper, a stochastic model predictive control (MPC) is proposed for the wheeled mobile robot to track a reference trajectory within a finite task horizon. The wheeled mobile robot is supposed to subject to additive stochastic disturbance with known probability distribution. It is also supposed that the mobile robot is subject to soft probability constraints on states and control inputs. The nonlinear mobile robot model is linearized and discretized into a discrete linear time-varying model, such that the linear time-varying MPC can be applied to forecast and control its future behavior. In the proposed stochastic MPC, the cost function is designed to penalize its tracking error and energy consumption. Based on quantile techniques, a learning-based approach is applied to transform the probability constraints to deterministic constraints, and to calculate the terminal constraint to guarantee recursive feasibility. It is proved that, with the proposed stochastic MPC, the tracking error of the closed-loop system is asymptotically average bounded. A simulation example is provided to support the theoretical result.

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