Abstract
We previously developed a novel composite wheel-leg-track explosive ordnance disposal (EOD) robot with high mobility, able to switch between a track or a self-balancing motion mode according to environmental conditions, named Scorpio. In this paper, we propose an adaptive nonlinear control algorithm for improving the stability of the robot in the self-balancing mode. First, a model of the dynamics of the robot was established, with which we designed the nonlinear cascade controller for combined balance and motion control. With our system, the attitude of the robot is estimated using a Kalman filtering algorithm. Based on this, an adaptive adjustment algorithm amends the parameters of the controller in real time according to the state of the robot, for improved stability. In addition, we formulated an adaptive zero-offset angle identification algorithm to compensate for deviations caused by changes to the robot's center of gravity (due to changes to its mechanical structure), ensuring that this stability could be maintained. Results of experiments conducted to verify their operation show that self-balancing control of Scorpio can be achieved with the proposed algorithms.
Highlights
explosive ordnance disposal (EOD) robots have a wide range of applications in the fields of security, disaster relief, anti-terrorism, and hazardous environment navigation
Most existing EOD robots operate in tracked or wheeled modes [1]–[4], resulting in the adoption of over-constrained structures. The limitations of such structures means that EOD robots need to overcome large frictions when making a turn, especially when engaging in point-turn motion, causing damage to mechanical structures, and posing high requirements on motor performance
Based on a nonlinear model of robot dynamics, we designed a nonlinear controller for balance and steering control of the robot
Summary
EOD (explosive ordnance disposal) robots have a wide range of applications in the fields of security, disaster relief, anti-terrorism, and hazardous environment navigation. This paper focuses on control of Scorpio’s self-balancing motion mode, where it effectively operates as a two-wheeled selfbalancing robot (TWSBR). (2) Self-balancing control of a TWSBR is affected by changes to its structure or load, which change its center of gravity, resulting in a random deviation of the angle of the balance point. To address these problems, in this paper, we present an adaptive nonlinear control algorithm (ANC). Deviations caused by changes to the robot’s center of gravity, due to changes to its structure or dynamic components, are compensated using an adaptive zero-offset angle identification algorithm, ensuring that the robot can maintain its stability.
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