Online Robot Path Adjustment for Obstacle-Aware Motion Planning
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Solution Overview
Problem
Traditional robotics planning requires extensive manual programming, which is time-consuming, error-prone, and inflexible, making it difficult for robots to adapt to changes in their environment, such as unexpected obstacles or objective locations, leading to potential collisions and reduced task success rates.
Innovation Solution
A system that can adapt robot motion plans during online operation by modifying initial plans to account for changes in objective locations and obstacles while maintaining the same footprint, allowing for efficient path optimization and collision avoidance without regenerating the entire motion plan, using an online motion planner and sensor inputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual programming is used to dictate robot movements, then the robot can perform tasks according to a predetermined schedule, but the programming process becomes tedious, time-consuming, and error-prone
Solution Approach 1:
The robot performs self-positioning by autonomously navigating to objectives using sensor feedback and path planning algorithms, eliminating the need for manual programming of movement paths. The system automatically adjusts its trajectory based on real-time environmental perception.
Solution Approach 2:
Manual programming is replaced with an automated online path planning system that uses sensor data and computational algorithms to generate robot trajectories in real-time, substituting the mechanical programming process with an intelligent software-based system.
2Productivity
If a manually generated schedule is created for one workcell, then the robot can operate efficiently in that specific environment, but the schedule cannot be used for other workcells with different robots, numbers of robots, or physical dimensions
Solution Approach 1:
The path planning system dynamically adapts to different workcell configurations by receiving real-time sensor data and computational inputs specific to each environment. The online planning algorithm adjusts robot trajectories based on current workcell parameters, robot positions, and environmental constraints, enabling the same system to operate efficiently across multiple different workcells.
Solution Approach 2:
The system changes operational parameters such as path coordinates, velocity profiles, and timing based on the specific characteristics of each workcell. By modifying these parameters dynamically rather than using fixed schedules, the robot can adapt to different physical dimensions, robot configurations, and environmental conditions.
3Loss of time
If the robot follows a pre-planned motion path offline, then the path is optimized for the expected environment, but the robot cannot efficiently adapt to changes such as objectives not being in expected locations or unexpected obstacles appearing
Solution Approach 1:
The system continuously receives feedback from sensors during robot operation, detecting actual objective locations and unexpected obstacles. This real-time feedback is fed into the online path planning algorithm, which computes adjusted trajectories to accommodate environmental changes while maintaining efficient path optimization.
Solution Approach 2:
The offline path planning provides preliminary trajectories that serve as initial guesses or starting points for the online planning algorithm. This preliminary action reduces the computational burden during real-time operation, allowing the system to quickly adapt to changes without performing complete path planning from scratch.
4Productivity
If the robot generates alternative motion plans during online operation, then the robot can find more efficient paths, but the computation required for generating and evaluating multiple alternative paths increases
Solution Approach 1:
The offline path planning pre-computes and provides preliminary motion plans that serve as initial solutions for the online algorithm. This preliminary action significantly reduces the computational search space during real-time operation, allowing the system to evaluate alternative paths more efficiently without excessive computational complexity.
Solution Approach 2:
The online path planning algorithm focuses computational resources on local adjustments to the trajectory rather than re-planning the entire path. By making localized modifications to waypoints and segments of the pre-computed offline plan, the system generates alternative paths with reduced computational complexity while still improving task completion speed.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for performing robot plan online adjustment. A method includes receiving an initial plan for performing a particular task with a robot having a sensor. The initial plan defines an initial path having a plurality of waypoints. Each waypoint is associated with a target position and a target velocity. The method includes generating an alternative path from the initial path. Generating an alternative path includes generating a plurality of alternative paths including performing respective modifications to one or more waypoints in the initial plan, evaluating each alternative path according to a simulated total time duration required for the robot to traverse the alternative path, and selecting an alternative path having a total time duration that is less than a total time duration of the initial plan.