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Zhe Zhang1, Peijia Xie1, Zhirui Sun1,2, Bingyi Xia1,3, Bi-Ke Zhu1 and Jiankun Wang1,2
Zhe Zhang1, Peijia Xie1, Zhirui Sun1,2, Bingyi Xia1,3, Bi-Ke Zhu1 and Jiankun Wang1,2
1Shenzhen Key Laboratory of Robotics Perception and Intelligence, Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, China.
2Jiaxing Research Institute, Southern University of Science and Technology, Jiaxing, China.
3Peng Cheng National Laboratory, Shenzhen, China.
Abstract: Precise and flexible cart-pushing is a challenging task for mobile robots. The motion constraints during cart-pushing and the robot's redundancy lead to complex motion planning problems, while variable payloads and disturbances present complicated dynamics. In this work, we propose a novel planning and control framework for flexible whole-body coordination and robust adaptive control. Our motion planning method employs a local coordinate representation and a novel kinematic model to solve a nonlinear optimization problem, thereby enhancing motion maneuverability by generating feasible and flexible push poses. Furthermore, we present a disturbance rejection control method to resist disturbances and reduce control errors for the complex control problem without requiring an accurate dynamic model. We validate our method through extensive experiments in simulation and real-world settings, demonstrating its superiority over existing approaches. To the best of our knowledge, this is the first work to systematically evaluate the flexibility and robustness of cart-pushing methods in experiments.
Motion Planning Experiments
Motion Planning Experiments
Evaluate the ability of different local planners to track the sharp global paths in real-time, as this is a crucial factor affecting maneuverability.
Evaluate the ability of different local planners to track the sharp global paths in real-time, as this is a crucial factor affecting maneuverability.
Sharp Sine wave tracking
Sharp Sine wave tracking
NMPC
WB-MPC
TT-MPC
Ours (LF-MPC)
Square wave tracking
Square wave tracking
NMPC
WB-MPC
TT-MPC
Ours (LF-MPC)
Gaussian noise tracking
Gaussian noise tracking
NMPC
WB-MPC
TT-MPC
Ours (LF-MPC)
Adaptive Control Experiments
Adaptive Control Experiments
The arms control the cart's orientation to fluctuate within ±0.2 rads, while a 22.2 kg payload is applied as a disturbance to evaluate the controllers' adaptability.
The arms control the cart's orientation to fluctuate within ±0.2 rads, while a 22.2 kg payload is applied as a disturbance to evaluate the controllers' adaptability.
Joint PD controller
Joint PD controller
Adaptive PD controller [13]
Adaptive PD controller [13]
Proposed Controller
Proposed Controller
Robot Navigation Task
Robot Navigation Task
Generate sharp global paths (sparse waypoints) using Hybrid A* to guide the cart through narrow spaces, and use planning and control frameworks based on different models to plan push poses for tracking the global paths.
Generate sharp global paths (sparse waypoints) using Hybrid A* to guide the cart through narrow spaces, and use planning and control frameworks based on different models to plan push poses for tracking the global paths.
Long-distance navigation
Long-distance navigation
Xiao's model+PD controller [4] ✘
Xiao's model (arms fixed in a central pose). The local planner fails to effectively track the sharp global path, resulting in hazardous accelerations due to the large turning radius.
Schulze's model+PD controller [8] ✘
Schulze's model (direct whole-body planning in local coordinates). Due to the coupling of optimization objectives, local planning exhibits unnatural oscillations and failed.
LF model+adaptive controller (Ours)✔
Our model complete the process of recognizing a cart, grasping the cart, and maneuvering the cart through a narrow corridor.
Narrow space navigation
Narrow space navigation
Xiao's model+PD controller [4] ✘
Failed due to large turning radius.
Schulze's model+PD controller [8] ✘
Failed due to abnormal swinging.
LF model+adaptive controller (Ours)✔
Succeed.
Robustness Testing under Extreme Conditions
Robustness Testing under Extreme Conditions
Evaluate robustness under extreme conditions, such as control performance with a single arm failure and resistance to external impacts (This is crucial as impacts can induce unsafe accelerations leading to self-collisions, and excessive velocity changes may cause localization errors).
Evaluate robustness under extreme conditions, such as control performance with a single arm failure and resistance to external impacts (This is crucial as impacts can induce unsafe accelerations leading to self-collisions, and excessive velocity changes may cause localization errors).
Transportation task with one arm failure
Impact resistance thanks to nonlinear control law
Comparison
Comparison
Joint PD controller
The joint PD controller cannot provide sufficient stiffness during impacts, causing the cart to accelerate to speeds that the manipulator cannot control, resulting in self-collision
Ours
Our proposed controller rapidly increases stiffness at the moment of impact to stabilize the system, then maintains compliance without significant overshoot once the impact disappears
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