Steering Control Strategies for a Four-Wheel-Independent-Steering Bin Managing Robot

Automatic steering systems are a standard function for robotic agricultural equipment. Ackerman steering is the most common type of steering mechanism on such equipment, making them perform as car-like vehicles. Because of their kinematic constraints, it is quite difficult to maneuver carlike vehicles effectively in orchards due to confined working space, constrained by physical boundaries such as tree rows and other obstacles. To remove such technical difficulties, more sophisticated steering mechanisms and steering control strategies are required for robotic agricultural equipment. In order to conveniently manage fruit bins in confined tree aisles constrained by high density tree rows, a robotic bin management system, called bin-dog system, implementable in typical Washington State tree fruit orchards has been developed. This bin-dog system adopted a four-wheel-independent-steering (4WIS) mechanism as the solution to achieve the necessary drivability and maneuverability. To provide adequate controllability to this 4WIS, a four-mode steering strategy, including Ackermann steering, active front and rear steering (AFRS), crab steering, and spinning, were designed for this bin-dog to manage fruit bins effectively in the confined orchard space. This control system makes it possible for the bin-dog system to switch between four steering modes using the most appropriate steering mode to complete all maneuvering tasks in a most effective way. To design such a control system, it is important to understand the influence of major factors, such as longitudinal speed and control gain on performance under different steering modes when tracking various paths. In this paper, a pure pursuit method was implemented using a GPS-based navigation to evaluate auto-steering performance with both Ackermann steering and AFRS modes. Field tests were conducted to assess the navigation performance using different steering strategies when tracking different paths. The results indicated that by properly selecting a steering strategy for the situation, it is possible to achieve a satisfactory path tracking performance for tracking curvy paths or completing tasks such as merging and cornering.