Integrating multiple soft constraints for planning practical paths

Sampling-based algorithms are a common approach to high-dimensional real-world path planning problems. Unfortunately the solutions found using such planners are often not practical in that they do not take into account soft application-specific constraints. This paper formulates the practicality of paths based on the notion of soft constraints found in the Planning Domain Definition Language 3 (PDDL3) [21] and a range of optimization strategies are developed targeted towards user-preferred qualities by integrating soft constraints in the pre-processing, planning and post-processing phases of the sampling-based path planners. An auction-based resource allocation approach coordinates competing optimization strategies. This approach uses an adaptive bidding strategy for each optimizer and in each round the optimizer with the best predicted performance is selected. This general coordination system allows for flexibility in both the number and types of the optimizers used. Experimental validation demonstrates the effectiveness of the approach.

Authors

• Jing Yang
• Patrick W. Dymond
• Michael Jenkin

Keywords

• Path planning
• Planning
• Robot kinematics
• Cost function
• Joints
• Soft Constraints
• Resource Allocation
• Bidding
• Preprocessing Phase
• Degrees Of Freedom
• Optimization Process
• Path Length
• Kalman Filter
• Learning Phase
• Configuration Space
• Importance Weights
• Tentacles
• Robot Motion
• Hard Constraints
• Planning Algorithm
• Joint Limits
• Cost Path
• Recursive Least Squares
• Rounds Of Optimization
• Preprocessing Approach
• Post-processing Algorithm
• Robot Path
• Gradient Projection Method
• Pathfinding
• Goal State