Hybrid Motion Control Switching for Energy-Efficient Mobile Robots
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Solution Overview
Problem
Autonomous navigation systems in self-driving vehicles and robots consume substantial electrical energy due to computationally expensive motion control frameworks like Model Predictive Control (MPC), which is inefficient and may not perform well in scenarios requiring precise navigation.
Innovation Solution
A hybrid motion control system that switches between MPC, simplified MPC, and PID control schemes based on operation scenarios to conserve energy, using LIDAR sensors and scenario selection logic to determine the appropriate control scheme for real-time navigation and energy efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If Model Predictive Control (MPC) framework is used for autonomous navigation, then collision-free motion control in dynamic environment is improved, but electrical energy consumption increases substantially
Solution Approach 1:
The system dynamically switches between MPC and PID control schemes based on the operational scenario. When the robot operates in open spaces, it uses the energy-efficient PID controller. When obstacles are detected requiring collision-free navigation, it switches to MPC. This dynamic adaptation resolves the contradiction by applying the computationally expensive MPC only when necessary for safety, rather than continuously.
Solution Approach 2:
The system changes the control parameter (control scheme selection) based on environmental conditions. By monitoring obstacle presence and spatial configuration, the system adjusts which controller is active, transitioning between PID and MPC modes. This parameter change allows the system to maintain collision-free operation when needed while minimizing energy consumption during normal operation.
2Use of energy by moving object
If simplified MPC is used for energy efficiency, then electrical energy consumption is reduced, but navigation precision deteriorates in scenarios requiring precise navigation
Solution Approach 1:
The system dynamically selects between simplified MPC, full MPC, and PID based on the specific operational context. When precise navigation is required (e.g., narrow passages, close proximity to obstacles), it activates full MPC or PID. When in open spaces, it uses simplified MPC or PID for energy efficiency. This dynamic selection resolves the precision-energy tradeoff by matching controller capability to task requirements.
Solution Approach 2:
The system applies different control qualities to different spatial contexts. In open areas where high precision is not critical, it uses simpler controllers. In constrained areas requiring precise maneuvering, it activates more sophisticated control schemes. This local differentiation of control quality ensures precision is maintained only where necessary, optimizing overall energy consumption.
3Measurement precision
If full MPC is used for precise navigation, then navigation precision is improved, but computational expense increases
Solution Approach 1:
The system dynamically adjusts computational complexity by switching between control schemes based on environmental complexity. In simple open environments, it uses PID with minimal computation. In complex environments with obstacles requiring precise navigation, it activates MPC. This dynamic adjustment resolves the contradiction by matching computational resources to actual navigation needs.
Solution Approach 2:
The control system is segmented into multiple independent controllers (PID, simplified MPC, full MPC) that can be selectively activated. Rather than running a single complex MPC continuously, the system segments the control function and activates only the necessary portion based on scenario requirements, reducing overall computational burden while maintaining precision when needed.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The hybrid motion control system effectively navigates through crowded and open environments while minimizing energy consumption by selecting the most efficient control scheme, ensuring real-time operation and desired motion performance.
Implementation Method 1
using LIDAR sensors and scenario selection logic to determine the appropriate control scheme for real-time navigation
Data Source
AI summary
Scenario discriminative hybrid motion control for robots and methods of use are disclosed herein. A method may include determining a number of objects in a space, determining when a goal is within the space, and selectively switching between a plurality of control schemes based on the number of objects in the space and whether the goal is within the space. The plurality of control schemes including a model predictive control scheme, a simplified model predictive control scheme, and a proportional-integral-derivative scheme. Selectively switching between the plurality of control schemes reduces power consumption of an automated system compared to when the automated system utilizes only the model predictive control scheme.