Dynamic Multi-Objective Ergodic Path Planning Using Decomposition Methods

Robots are often employed in hazardous or inaccessible environments, such as disaster sites, extraterrestrial terrains, agricultural fields, and ocean floors. Autonomous operation is crucial in these scenarios to reduce reliance on human operators and enable real-time decision-making. However, robots must balance multiple, often conflicting, objectives. These objectives are subject to change based on new data or evolving conditions. This paper presents a novel approach to dynamic multi-objective trajectory planning. The proposed method leverages the boundary intersection decomposition technique to adaptively plan trajectories that balance multiple evolving objectives. Our approach ensures efficient and effective exploration by continuously optimizing the trade-offs between changing objectives. We show that our method performs on average 34 % better in terms of solution quality on the dynamic multi-objective trajectory planning problem as compared to prior work.

Authors

• Abigail Breitfeld
• David Wettergreen

Keywords

• Measurement
• Space vehicles
• Trajectory planning
• Search methods
• Oceans
• Real-time systems
• Planning
• Mobile robots
• Trajectory optimization
• Floors
• Path Planning
• Solution Quality
• Human Operator
• Dynamics Trajectories
• Ocean Floor
• Dynamic Planning
• Disaster Site
• Time Step
• Use Of Information
• Weight Vector
• Multi-objective Optimization
• Objective Value
• Dirac Delta
• Pareto Optimal
• Mobile Robot
• Objective Space
• Pareto Front
• Planning Time
• Map Objects
• Ideal Point
• Weighted Sum Method
• Dynamic Search
• Dynamic Objects
• Scalar Problem
• Negative Points
• Decision Vector
• Dynamic Optimization
• Fourier Coefficients