Distributed Anonymous Mobile Robots: Formation of Geometric Patterns
SIAM Journal on Computing
Convergence Properties of the Gravitational Algorithm in Asynchronous Robot Systems
SIAM Journal on Computing
Gathering of asynchronous robots with limited visibility
Theoretical Computer Science
Impossibility of gathering by a set of autonomous mobile robots
Theoretical Computer Science
Convergence of Autonomous Mobile Robots with Inaccurate Sensors and Movements
SIAM Journal on Computing
Solving the robots gathering problem
ICALP'03 Proceedings of the 30th international conference on Automata, languages and programming
Dynamic compass models and gathering algorithms for autonomous mobile robots
SIROCCO'07 Proceedings of the 14th international conference on Structural information and communication complexity
Gathering asynchronous mobile robots with inaccurate compasses
OPODIS'06 Proceedings of the 10th international conference on Principles of Distributed Systems
Gathering autonomous mobile robots with dynamic compasses: an optimal result
DISC'07 Proceedings of the 21st international conference on Distributed Computing
Optimal Byzantine Resilient Convergence in Asynchronous Robots Networks
SSS '09 Proceedings of the 11th International Symposium on Stabilization, Safety, and Security of Distributed Systems
Pattern formation through optimum matching by oblivious CORDA robots
OPODIS'10 Proceedings of the 14th international conference on Principles of distributed systems
The Gathering Problem for Two Oblivious Robots with Unreliable Compasses
SIAM Journal on Computing
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We consider the convergence problem of autonomous mobile robots with inaccurate sensors, which may return the erroneous location of other robots. In this paper, we newly introduce a uniform error model, which is a restricted variant of the original observation-error model proposed by Cohen and Peleg [4]. The degree of an observation error is characterized by distance errors and angle errors. While the original model (non-uniform model) allows that two or more points can have different error degrees, the uniform error model assumes that the same amount of error degree is incurred to all observed points in a single observation. The main focus of our study is to reveal how much such uniformity expands the feasibility of the convergence. In the non-uniform error model, it has been shown that no algorithm can achieve the convergence if the maximum error angle is more than or equal to π/3. This paper shows that the convergence problem is solvable under the uniform error if the maximum error angle is less than π/ 2. We also prove that there is no convergence algorithm for the maximum error angle more than or equal to π/2 even in the uniform error model, which implies the optimality of our algorithm in the sense of angle errors.