Energy-Efficient Path Planning with Multi-Location Object Pickup for Mobile Robots on Uneven Terrain

2025-09-07

AI Summary

To address the sharp increase in energy consumption caused by dynamic payload variations when mobile robots perform multi-point object pickup on non-planar terrain, this paper proposes an energy-efficient path planning method. Unlike conventional single-objective energy-aware planners, our approach introduces the first Payload-Constrained Path Database (PCPD), enabling concurrent multi-point search and integrating the Z* algorithm with an enhanced compressed path indexing technique. The key innovation lies in explicitly modeling the payload step changes induced by pickup actions and their coupled effects on terrain resistance and actuation energy consumption. Experimental results on complex outdoor terrains demonstrate that the proposed method achieves a 10×–100× improvement in computational efficiency over baseline methods while maintaining an energy suboptimality gap of less than 3%, significantly outperforming state-of-the-art approaches.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

Autonomous Mobile Robots (AMRs) operate on battery power, making energy efficiency a critical consideration, particularly in outdoor environments where terrain variations affect energy consumption. While prior research has primarily focused on computing energy-efficient paths from a source to a destination, these approaches often overlook practical scenarios where a robot needs to pick up an object en route - an action that can significantly impact energy consumption due to changes in payload. This paper introduces the Object-Pickup Minimum Energy Path Problem (OMEPP), which addresses energy-efficient route planning for AMRs required to pick up an object from one of many possible locations and deliver it to a destination. To address OMEPP, we first introduce a baseline algorithm that employs the Z star algorithm, a variant of A star tailored for energy-efficient routing, to iteratively visit each pickup point. While this approach guarantees optimality, it suffers from high computational cost due to repeated searches at each pickup location. To mitigate this inefficiency, we propose a concurrent PCPD search that manages multiple Z star searches simultaneously across all pickup points. Central to our solution is the Payload-Constrained Path Database (PCPD), an extension of the Compressed Path Database (CPD) that incorporates payload constraints. We demonstrate that PCPD significantly reduces branching factors during search, improving overall performance. Although the concurrent PCPD search may produce slightly suboptimal solutions, extensive experiments on real-world datasets show it achieves near-optimal performance while being one to two orders of magnitude faster than the baseline algorithm.

Problem

• Energy-efficient path planning for mobile robots on uneven terrain
• Minimizing energy consumption during multi-location object pickup
• Addressing payload impact on energy usage during delivery tasks

Innovation

• Concurrent PCPD search for multiple pickup points
• Payload-Constrained Path Database with energy constraints
• Z star variant optimized for uneven terrain routing